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HR
AI Hiring Assistant
Overview
lyzr’s AI Hiring Assistant automates the hiring lifecycle – from resume parsing and candidate ranking to scheduling interviews and generating final reports. Built for high-volume recruitment, it ensures faster decisions and better-quality hires with minimal human intervention.
Lyzr Workflow
Lyzr’s AI Hiring Assistant integrates with ATS, scans resumes using NLP, ranks candidates based on job fit, and autonomously schedules interviews by coordinating with hiring managers and candidates.
Problem Statements
- Manual resume overload
Recruiters spend hours screening irrelevant resumes, missing quality talent. - Scheduling chaos
Back-and-forth emails lead to delays and drop-offs. - No intelligent candidate ranking
HR lacks data-driven insights to shortlist top-fit candidates efficiently.
How the Agent Works
A step-by-step walkthrough of the AI Hiring Assistant
- Step 1: Job submitted
The hiring manager defines a role. The JD is parsed and understood by the AI Agent. - Step 2: Candidate matching
The Candidate Matching Agent scans internal & external resume databases to find the closest fits. - Step 3: Resume screening
The Screening Agent analyzes resumes and enriches them with missing data. - Step 4: Candidate questionnaire
Shortlisted candidates receive dynamic, pre-screening questions tailored to the role. - Step 5: Interview scheduling
The Scheduler Agent finds mutually available slots between recruiter and candidate—automated, error-free. - Step 6: Phone screening by AI
An AI Voice Agent conducts a structured phone interview and scores the candidate. - Step 7: Consolidated scoring
An evaluation agent aggregates insights from resumes, questionnaires, and interviews to rank candidates. - Step 8: Offer readiness
The best-matched candidates are prepared for final interviews or offer rollouts. - Step 9: Candidate reports generated
A complete, auditable report is generated with recommendations for every candidate. - Step 10: HR team reviews
HR steps in only for decision-making—no manual execution needed.
Benefits & Capabilities
- Speed without compromise
Slash hiring cycles from weeks to days with automated resume screening and interview coordination. - Data-led candidate shortlisting
Gain structured, unbiased reports that help HR and hiring managers make better decisions. - Seamless integrations
Plug into your ATS, calendars, and email systems effortlessly—no disruption required. - Elevate recruiter productivity
Let recruiters focus on strategic tasks while AI handles the grunt work.
Tech Stack
- LLM: GPT-4 for resume parsing & candidate matching
- ATS Integration: Workday, Greenhouse, Lever
- Scheduling APIs: Google Calendar, Outlook
- Vector Database: Qdrant for resume retrieval and matching
- Memory Modules: Short-term (session data), Long-term (candidate tracking)
- Agent Framework: Built using Lyzr’s AI Agent API
- Agents: AI Resume Screening & Parsing, AI-driven Candidate Scoring, Automated Interview Scheduling
AI Exit Inteview Agent
Overview
Lyzr’s Exit Interview Agent automates the entire offboarding conversation, collecting structured insights through voice, chat, or email. It helps HR teams uncover real reasons behind exits – turning employee attrition into actionable feedback.
Lyzr Workflow
Problem Statements
- Low participation in exit interviews
Employees often skip or rush interviews, leading to incomplete data. - Unstructured feedback
Manual interviews result in scattered, qualitative responses that are hard to analyze. - Delayed insights
By the time HR sees patterns, it’s already too late to prevent the next exit.
How the Agent Works
- Step 1: Exit trigger detected
Once HR marks an employee for exit, the Exit Interview Agent is auto-activated. - Step 2: Communication initiated
The agent reaches out via the employee’s preferred channel—chat, email, or voice. - Step 3: Sentiment-aware conversation
Using natural language understanding, the agent adapts its tone and questions dynamically. - Step 4: Feedback collection
The agent collects insights on team culture, management, workload, compensation, and growth. - Step 5: Real-time sentiment analysis
Responses are analyzed in real time to gauge satisfaction, regret, or red flags. - Step 6: Report generation
Structured summaries and emotion-tagged quotes are compiled into a visual dashboard. - Step 7: HR insights dashboard
Team-level attrition trends, reasons for exit, and actionable suggestions are presented to HR. - Step 8: Manager alerts (optional)
Critical feedback is shared (anonymously) with team managers to initiate improvements.
Benefits & Capabilities
- Honest feedback, automatically captured
Employees are more candid with an AI agent, leading to deeper insights. - Structured insights at scale
Every response is categorized, scored, and visualized—no more Excel sheets and guesswork. - Identify trends before they become problems
Spot patterns across departments or roles and proactively fix culture or process gaps. - Saves HR time and resources
HR gets ready-to-use reports without conducting or transcribing a single interview.
Tech Stack
- LLM: GPT-4o, Claude 3 for natural language understanding & report generation.
- Communication Channels: Slack, MS Teams, Email, Voice via ElevenLabs / Vapi.ai.
- Sentiment Analysis Tools: OpenAI APIs, Langchain integrations.
- Vector Database: Qdrant – for storing response history & similarity searches.
- Memory Modules: Short-term (current exit case), Long-term (department-level patterns).
- Agent Framework: Built with Lyzr’s AI Agent API.
- AI Agents Deployed: Conversation Agent, Sentiment Analysis Agent, Report Generator Agent.
Job Description Generator
Overview
This blueprint outlines a Modular AI-Powered Job Description Generator, a single-agent solution designed to streamline the process of writing effective, inclusive, and high-conversion job descriptions. Built using Bolt, this system leverages generative AI to dynamically craft tailored job descriptions based on role inputs, hiring goals, and desired tone—empowering hiring managers to focus on attracting the right talent, faster. Unlike traditional JD templates or static form-fill tools, this agent delivers a flexible, explainable, and customizable approach to job posting. It adapts to different roles, industries, and tone requirements, provides inclusivity and clarity checks, and generates ready-to-publish descriptions that integrate easily with applicant tracking systems or job boards. The result is a faster, smarter, and more equitable hiring workflow optimized for speed and quality.
Lyzr Workflow
Problem Statements
Creating effective job descriptions is often a tedious and inconsistent process for hiring managers and recruiters. Legacy tools rely on outdated templates or require manual curation across multiple tools, leading to misaligned expectations, uninspiring job posts, and biased language that can reduce applicant diversity. As hiring teams scale or evolve, the lack of agility in current JD creation workflows leads to bottlenecks, longer time-to-hire, and difficulty in standardizing tone and structure across departments. Moreover, most solutions lack built-in intelligence to optimize job descriptions for clarity, inclusiveness, and candidate appeal. To attract top talent in today’s competitive landscape, organizations need a modular, AI-driven job description agent that adapts to changing hiring needs, simplifies the creation process, ensures quality and inclusivity, and seamlessly integrates with existing hiring platforms.
How the Agent Works
- User Input: The hiring manager provides basic role details:
- Job Title
- Role Summary
- Customize Settings (Optional): The user can adjust preferences such as:
- Tone of voice (e.g., formal, inclusive)
- Employment type (full-time, part-time, contract)
- Seniority level
- Department or industry context
- AI Job Description Generator Agent: Using the provided inputs and settings, the agent:
- Generates a structured, compelling job description
- Fills in key sections: responsibilities, qualifications, benefits
- Checks for inclusive language and clarity
- Output: The final result is a ready-to-use Job Description, which can be:
- Exported to PDF or DOCX
- Shared with HR platforms or ATS
- Reviewed collaboratively by the hiring team
Benefits & Capabilities
- Modular AI Agent Architecture: Each section of the job description—such as responsibilities, qualifications, tone, and inclusivity—is powered by a dedicated, reusable AI component. This enables flexible customization, continuous improvements, and seamless integration into existing HR workflows or ATS systems.
- End-to-End Job Description Creation: From job title input to final, formatted description, the system automates the full JD writing process. It ensures that all critical sections are covered—responsibilities, qualifications, benefits, and company mission—without requiring manual copy-paste or template editing.
- Real-Time AI Suggestions & Refinements: Hiring managers receive contextual AI suggestions for wording, tone, and clarity. The system also detects and flags jargon, ambiguity, or redundancy, helping ensure that the job description is both effective and accessible.
- Inclusivity & Language Optimization: Built-in language analysis checks for gender bias, exclusive terms, and readability. AI-powered rewrites offer inclusive alternatives to help attract a broader, more diverse talent pool.
- Collaboration & Export-Ready Output: Easily share or export the final job description in multiple formats (PDF, DOCX, ATS-ready). An optional review agent can facilitate approvals, version control, or comments from HR or hiring teams.
Tech Stack
- LLM: GPT-4o, Claude Sonnet, Gemini 1.5 Flash.
- Prompt Templates: Modular JD section templates (Responsibilities, Benefits, etc.).
- Input Sources: Manual input (Title, Summary), Org-level defaults.
- Output Channels: PDF, DOCX, ATS connectors
- Language Analysis: Inclusive language & bias-check APIs.
- Integration Support: Zapier, Greenhouse API, Lever.
HR Helpdesk Agent
Overview
An intelligent help desk agent that automates HR query handling, ensures policy-aligned responses, and gives HR teams visibility into recurring employee concerns—delivering instant support, every time.
Lyzr Workflow
Problem Statements
Traditional HR help desks are slow, inefficient, and often lead to frustration due to long response times, repetitive queries, and difficulty in tracking resolutions. Employees frequently need answers regarding policies, payroll, leave requests, or benefits but often struggle with delayed responses from HR teams overwhelmed with manual inquiries. Additionally, HR teams lack data-driven insights into recurring employee concerns, making it hard to proactively improve HR services.
How the Agent Works
The AI HR Help Desk Agent is the central engine that processes employee queries, whether it’s about payroll, benefits, leave policies, or company procedures. Employees submit their queries via Slack, Microsoft Teams, email, or an HR portal, and the AI immediately retrieves answers from an HR knowledge base. If the query is straightforward, the AI provides an instant response using LLMs trained on company policies and HR documentation. For more complex cases, the AI Ticketing Agent categorizes and prioritizes the request before escalating it to a human HR representative via an integrated HR service desk (e.g., Zendesk, Freshservice, ServiceNow).
As employees interact with the system, the AI Insights Agent analyzes trends in HR inquiries, helping HR teams proactively identify and address recurring concerns. A HR Compliance & Policy Agent ensures that answers are aligned with the latest company policies and legal regulations. Additionally, the HR Action Agent allows employees to submit requests—such as updating personal information or applying for leave—directly through the chatbot without needing manual intervention.
This AI-driven workflow streamlines HR support, reduces wait times, and enables HR teams to focus on strategic employee engagement rather than administrative queries.
Benefits & Capabilities
- Instant, policy-aligned answers: Employees get responses immediately—with accuracy, clarity, and compliance built-in.
- 24/7 availability: Support doesn’t stop when the day ends—queries are handled round the clock.
- Built-in analytics: HR teams receive insights into top queries, helping them streamline documentation and training.
- Seamless escalations: Complex questions? The agent auto-escalates to a human through your existing ticketing system.
Tech Stack
- LLM: GPT-4, Claude 3.
- Knowledge Retrieval: Pinecone, Qdrant.
- HRMS Integrations: Workday, BambooHR, SAP SuccessFactors.
- Ticketing Integrations: Zendesk, Freshservice, ServiceNow.
- Memory Modules: Short-term (session-based), Long-term (historical tracking).
- Collaboration Channels: Slack, Microsoft Teams, Email Bot.
- Workflow Automation:Lyzr AI Agent API.
- Supporting Agents: Compliance Agent, Memory Module, AI Insights Agent
AI L&D Agent
Overview
An intelligent tutor agent that delivers personalized, adaptive learning experiences—automating content delivery, progress tracking, and skill assessments across your workforce.
Lyzr Workflow

Problem Statements
Traditional Learning & Development (L&D) programs struggle with engagement, personalization, and effectiveness. Employees often receive generic training that doesn’t align with their career goals or skill gaps. Learning is static, with limited adaptability to individual needs, and tracking progress requires extensive manual effort. Additionally, organizations lack real-time insights into how training impacts employee performance, making it difficult to measure ROI.
How the Agent Works
AI-powered learning and development (L&D) enables organizations to create highly personalized, adaptive, and data-driven training experiences. Instead of relying on one-size-fits-all corporate training programs, AI agents assess employee skills, job roles, and career aspirations to recommend customized learning paths.
The AI L&D Tutor Agent pulls resources from an LMS (Learning Management System), integrating seamlessly with platforms like Coursera, Udemy, and LinkedIn Learning to provide high-quality, targeted content.
Employees receive AI-generated microlearning modules, quizzes, and real-world case studies, making it easier to learn while maintaining productivity. To ensure effectiveness, the AI Assessment Agent evaluates progress through interactive Q&A sessions, real-world simulations, and quizzes, providing instant feedback and reinforcement learning suggestions.
The Learning Analytics Agent continuously monitors engagement, completion rates, and knowledge retention, adjusting training difficulty and suggesting alternative learning paths based on performance. This adaptive learning model ensures employees progress at their own pace while staying aligned with organizational goals.
HR teams benefit from automated reporting and AI-driven insights into learning effectiveness, skill gaps, and training ROI tracking. AI-generated reports highlight workforce competency trends and suggest next steps for skill development and career progression. By integrating AI-powered learning automation, organizations can scale upskilling efforts, drive workforce agility, and ensure continuous career growth, making HR learning workflows more efficient, intelligent, and impactful.
Benefits & Capabilities
- Personalized learning paths: Training is mapped to individual goals, roles, and upskilling needs.
- Adaptive learning in real time: Progress-based content delivery helps learners move at their own pace.
- Smart assessments & feedback: The system dynamically evaluates learning and recommends improvements.
- Dashboards for visibility: Get instant views on learning outcomes, ROI, and skill gaps across teams.
Tech Stack
- LLM: GPT-4, Claude 3.
- Vector Database: Qdrant, Pinecone, Weaviate.
- LMS Integrations: Coursera, Udemy, LinkedIn Learning, Custom LMS.
- Memory Modules: Short-term (session tracking), Long-term (career progression).
- Collaboration Tools: Slack, Microsoft Teams (for nudges and reminders).
- Workflow Automation: Lyzr AI Agent API.
- Dashboards:Streamlit, Power BI, Tableau.
- Supporting Agents:AI L&D Tutor, Assessment Agent, Learning Analytics Agent, Career Advisor.
ESAT Survey Agent
Overview
Capture what your employees really feel—instantly. This AI-powered satisfaction survey agent automates distribution, performs real-time sentiment analysis, and helps HR teams make data-backed decisions, faster.
Lyzr Workflow


Problem Statements
- Low survey participation: Employees often skip surveys due to lack of relevance or anonymity concerns.
- Manual analysis overload: HR teams are buried in feedback with no scalable way to extract insights.
- Delayed action = lost trust: When feedback isn’t acted on quickly, it erodes employee belief in the process.
How the Agent Works
- Survey distribution begins: Personalized surveys are sent via Slack, Email, or HR Portals using the Survey Distribution Agent.
- Employees submit responses: Surveys are dynamic and role-specific to ensure high engagement and relevance.
- Real-time sentiment analysis: The Survey Response Analysis Agent categorizes feedback by topic and emotion.
- Insights are structured: The Employee Feedback Insights Agent organizes responses into key themes and generates reports.
- Trend analysis begins: Responses are stored in a vector database to enable historical tracking and future benchmarking.
- HR receives alerts & plans: Real-time alerts notify HR of red flags; action plans are recommended instantly.
- AI coaching for employees: The AI HR Coach shares personalized feedback and enables follow-ups directly with employees.
- HR tools sync: Insights integrate with Workday, BambooHR, or SAP to close the loop from insight to action.
Benefits & Capabilities
- High engagement surveys: Dynamic and anonymous surveys boost participation and authenticity.
- Real-time HR insights: Instant analysis surfaces employee sentiment and themes as responses come in.
- Long-term trend tracking: Monitor shifts in morale, engagement, and feedback patterns over time.
- Coaching built-in: Provide employees with AI-generated responses and actionable feedback automatically.
Tech Stack
- LLMs: GPT-4, Claude 3
- Survey Distribution: Slack API, Google Forms API, Workday, BambooHR
- Vector Database: Qdrant
- Memory Modules: Short-term (session-based), Long-term (trend tracking)
- Agent Framework: Built using Lyzr AI Agent API
- Dashboards & Reporting: Streamlit, Tableau
- Supporting Agents: Survey Distribution Agent, Survey Response Analysis Agent, Employee Feedback Insights Agent, AI HR Coach, Trend Analysis Agent
AI Performance Review Agent
Overview
From spreadsheets to Slack threads—AI now reads it all. This blueprint introduces an intelligent performance review agent that automates evaluations by pulling real-time feedback from self-assessments, chats, meetings, and even psychometric data. It’s holistic. It’s unbiased. It’s built for growth.
Lyzr Workflow


Problem Statements
Traditional performance management is inefficient, time-consuming, and often biased. Employees and managers rely on manual reviews, subjective assessments, and incomplete data from scattered sources. This leads to inconsistent feedback, lack of actionable insights, and limited growth opportunities for employees.
How the Agent Works
Benefits & Capabilities
- Holistic Performance Analysis: Integrates feedback from multiple channels—chats, calls, 1:1s—for an unbiased view of employee performance.
- Faster, Smarter Reviews: Reduces review time by automating evaluation and reporting workflows for HR and managers.
- Personalized Growth Plans: Identifies strengths, weaknesses, and recommends custom development goals for each employee.
- Built-In Coaching Support: AI-powered coaching agents guide employees with actionable advice post-review.
Tech Stack
- LLMs: GPT-4o (OpenAI), Claude 3
- Data Sources: Google Forms, Slack, Zoom, 1:1 notes, HR systems
- Vector Database: Qdrant
- Agent Framework: Lyzr AI Agent API
- Hosting: AWS
- Supporting Agents: Performance Report Analysis Agent, Slack Messages Analysis Agent, Zoom Meetings Analysis Agent, 1:1 Feedback Analysis Agent, Psychometric Analysis Agent, Employee Performance Analyst Agent, Review & Report Generator Agent
Banking
Cross-Border Payment Optimization Agent
Overview
FinOptimize is an AI-powered platform designed to streamline and optimize international payment processes for U.S. commercial banks and their enterprise clients. It leverages multiple AI agents to intelligently route payments, forecast currency trends, ensure compliance, and simulate financial scenarios in real time.
Lyzr Workflow
Problem Statements
Traditional international payment systems are plagued by inefficiencies, opaque fees, regulatory risks, and poor currency timing. Businesses lack the tools to simulate and optimize these transactions dynamically, resulting in lost value and increased operational risk.
Solution: FinOptimize addresses this by integrating AI agents to:
- Forecast currency movements and suggest optimal conversion windows
- Simulate cross-border payment scenarios and associated risks
- Automate compliance verification and scoring
- Optimize routing for cost, time, and regulatory considerations
How the Agent Works
This blueprint outlines how AI agents collaboratively process, analyze, and optimize international payments in real time — ensuring cost-efficiency, regulatory compliance, and operational scalability.
1. Payment Intake Handler: The workflow begins by ingesting payment instructions from multiple enterprise sources:
- Payment portals (Web/API)
- Core banking and ERP/treasury systems
- Uploaded batch files (CSV/XML)
- Live market feeds (FX rates, SWIFT metadata)
The Payment Intake Handler parses and standardizes incoming requests for further processing.
2. Transaction Optimizer: This agent acts as the orchestration brain. Based on transaction context, it routes the request to relevant agents that assess timing, routing, and compliance dimensions.
3. AI Currency Forecasting Engine Agent: Leverages market data and predictive models to recommend optimal timing for currency conversion, reducing FX risk and maximizing value.
4. AI Payment Routing Optimizer Agent: Analyzes available corridors, intermediary fees, transfer times, and historical success rates to determine the most efficient payment route.
5. Pre-Screening Compliance Verification Agent: Performs early-stage KYC/AML and jurisdiction-based screening to quickly flag high-risk transactions before deep simulation begins.
6. AI Scenario Simulation Agent: Runs simulations on different routing and conversion combinations, projecting:
- Transfer cost and time
- Currency rate impact
- Regulatory risk
This enables informed trade-off decisions.
7. AI Compliance Verification Agent: Executes in-depth compliance validation using global regulatory databases, embargo lists, and rule-based logic to ensure regulatory adherence.
8. Master Transaction Optimizer: Synthesizes all agent outputs (routing, currency, compliance, simulations) to make the final decision on how the transaction should proceed. It then activates downstream agents to execute and report on the transaction.
9. Treasury Dashboard Agent: Generates visual dashboards that summarize:
- Optimized routes and FX timing decisions
- Compliance results
- Aggregate cost savings and performance metrics
10. Audit Trail & Logging Agent: Creates secure, traceable logs of all decision points and agent actions — enabling audit-readiness for internal and external reviews.
11. Feedback Learning Loop Agent: Ingests post-transaction outcomes (e.g., delays, compliance flags, fees incurred) and feeds them back into the AI models for continuous improvement.
12. Reporting & Filing Agent: Automates regulatory reporting (e.g., SWIFT filings, tax disclosures), exporting data in required formats like CSV, XML, or direct submissions.
13. Payment Execution System: Initiates the actual fund transfer using integrated payment gateways or SWIFT messaging, completing the transaction lifecycle.
Summary: This agentic workflow transforms cross-border payments into a highly optimized, transparent, and scalable process. Each AI agent specializes in a specific domain — from currency forecasting to compliance verification — and works in harmony under the Transaction Optimizer’s orchestration logic to deliver maximum value with minimal risk.
Benefits & Capabilities
- Modular AI Agent Framework: Every critical AML function—KYC, monitoring, screening, and reporting—is powered by a dedicated, independently deployable agent. This modular design enables rapid iteration, seamless updates, and easy integration with existing banking systems or compliance stacks.
- Comprehensive Risk & Identity Analysis: The agent system automates the entire onboarding and monitoring lifecycle—from ID extraction and liveness detection to address validation and behavioral risk assessment—delivering a faster, more accurate AML review.
- Instant Sanctions Screening & Risk Scoring: Real-time checks against OFAC, PEP, and global AML watchlists ensure immediate detection of high-risk individuals. Intelligent profiling dynamically adjusts risk levels based on geographic, behavioral, and transactional inputs.
- Regulator-Ready Audit Trail: All actions, decisions, and data inputs are compiled into a fully traceable audit report—automatically formatted for internal compliance review or FinCEN and FFIEC regulatory needs.
- Intelligent Escalation & User Recovery: An optional support agent engages users if verification steps fail or produce ambiguity, guiding them through resolution while reducing drop-offs and manual reviews.
Tech Stack
- LLM: GPT-4, Claude 3
- Market Data & FX Feeds: Bloomberg, Reuters, SWIFT metadata, central bank APIs
- Payment Intake Sources: Core banking systems, ERPs (SAP, Oracle), APIs, batch file uploads (CSV/XML)
- Agent Framework: Lyzr AI Agent API
- Vector Database: Qdrant, Pinecone
- Compliance Databases: OFAC, PEP lists, FATF, EU Sanctions, FinCEN watchlists
- Treasury Dashboard: Streamlit, Tableau, Power BI
- Reporting Format Integrations: CSV, XML, ISO 20022, SWIFT
- Hosting: AWS or Private Cloud
- Supporting Agents: Currency Forecasting Agent, Routing Agent, Compliance Agents, Simulation Agent, Audit Logging Agent, Reporting Agent, Payment Execution Agent
Real-Time Payment Agent
Overview
This blueprint introduces a Modular AI-Powered Real-Time Payment Agent, a multi-agent system designed to automate, monitor, and explain instant transactions via RTP and FedNow, while ensuring compliance with U.S. financial regulations. Built using Bolt, the architecture decomposes core payment orchestration tasks—such as intent parsing, fraud detection, sanctions screening, compliance checks, network selection, transaction execution, and audit logging—into specialized AI agents. Unlike legacy payment systems that are monolithic, rule-bound, or tightly coupled to a single vendor’s stack, this agent-based design provides a flexible, explainable, and composable alternative. Each agent is independently deployable, tunable, and easily integrates with external data sources like OFAC, FedNow sandbox, RTP networks, and internal risk engines. The result is a faster, smarter, and more secure real-time payment infrastructure that supports compliance, enhances customer experience, and reduces operational risk.
Lyzr Workflow
Problem Statements
Real-time payment systems in the U.S. face rising demand from consumers and enterprises but are often built on rigid, fragmented infrastructure. Existing solutions tend to hardcode logic for network selection, fraud rules, or transaction limits, making them difficult to adapt for evolving regulatory frameworks or enterprise-specific needs. Compliance checks are often bolted on rather than deeply integrated, leading to delayed payments, unnecessary holds, and lack of transparency for users and auditors. Furthermore, these systems typically offer poor explainability when a transaction is rejected or flagged, leaving analysts with insufficient context. As RTP and FedNow adoption grows, financial institutions need a modular, AI-native payment agent that can intelligently parse user intent, evaluate risk, route transactions, and produce audit-ready decision logs in real-time—all without compromising performance or regulatory standards.
How the Agent Works
This workflow outlines how a real-time payment request is handled from the moment a user submits it to the point where it is either successfully completed or flagged for review. The process is powered by a series of intelligent agents, each handling a specific step to ensure speed, security, and compliance.
- User Input (Chat or API): The journey starts when a user submits a payment request — either by chatting with an assistant or through a connected app or system. The request could be something like asking to send money to a contact or pay a bill.
- AI Intent Parser: The first agent listens carefully to what the user wants and identifies the key details: who to send money to, how much, and why. This ensures the system understands the request clearly before taking action.
- Compliance Checker: Next, a dedicated agent checks the request to ensure it follows internal financial policies and risk thresholds. For example, it makes sure the amount isn’t too high, and that the user has permission to send it.
- Sanctions Screening: This agent checks whether the person or business receiving the money is on any government watchlists — such as those related to fraud, terrorism, or money laundering. This step ensures legal compliance and protects against risky transfers.
- Transaction Executor: Once everything checks out, the system proceeds to actually send the payment. It selects the fastest and most appropriate payment network available — like RTP or FedNow — and processes the transfer securely.
- AI Audit Logger: As the payment goes through, this agent keeps track of everything that happened — what the user asked for, what decisions were made along the way, and the final outcome. This is important for internal reviews or future audits.
- Master Transaction Optimizer: This final agent reviews the full process to make sure everything ran smoothly. It helps ensure the right decisions were made and that the result can be shared with the user and logged accurately.
Final Outputs
- Execution Status: The: user is notified whether the payment was successful, delayed, or flagged for further review.
- Decision Log Generated: A complete summary of how the payment was handled is saved, so it can be reviewed by compliance teams or regulators if needed.
Benefits & Capabilities
- Modular AI-Powered Architecture: Each core function—such as intent recognition, compliance validation, sanctions screening, transaction execution, and audit logging—is managed by a dedicated AI agent. This modular setup allows for fast updates, easy customization, and seamless integration with existing banking platforms and payment infrastructure.
- End-to-End Risk & Identity Intelligence: From user onboarding to transaction execution, the agent system continuously evaluates risk by analyzing identity data, behavioral patterns, and transactional context—streamlining verification and reducing false positives without sacrificing security.
- Real-Time Sanctions & Risk Screening: All payments are instantly checked against global watchlists (e.g., OFAC, PEP) to flag potential threats. Risk scoring is dynamically adapted based on the user’s behavior, location, transaction type, and historical patterns—ensuring proactive compliance with AML standards.
- Built-In, Regulator-Ready Audit Logging: Every step in the payment flow is logged in a detailed, timestamped audit trail—capturing decisions, outcomes, and rationale. The logs are automatically formatted for internal audits and can be used for external regulatory reporting (e.g., FinCEN, FFIEC).
- Smart Escalation & Resolution Support (Optional): When a transaction is flagged or a verification fails, an optional support agent can engage the user in real-time—guiding them through next steps, collecting missing information, or escalating to a compliance analyst—reducing friction and increasing completion rates.
Tech Stack
- LLMs: GPT-4, Claude 3
- Payment Networks: RTP (Real-Time Payments), FedNow
- Compliance Databases: OFAC, PEP, FATF, FinCEN
- Audit & Logging: Lyzr Audit Trail Framework, ISO 20022 export, encrypted log storage
- User Input Channels: Web app, API, Chatbot (Slack, Teams, WhatsApp)
- Vector Databases: Qdrant, Pinecone
- Hosting: AWS or Private Cloud
- Agent Framework: Lyzr AI Agent API
- Supporting Agents: Intent Parser, Compliance Agent, Sanctions Screener, Execution Agent, Audit Logger, Master Optimizer, Support Escalation Agent
KYC Processing
Overview
This KYC Agent Blueprint outlines a full-stack verification system powered by specialized AI agents. From OCR-based document ingestion to address checks, biometric validation, sanctions screening, and AML reporting—each step is handled independently yet orchestrated centrally through a compliance engine. The system integrates with trusted APIs like Onfido, LexisNexis, USPS, and Trulioo to provide a secure, fast, and auditable onboarding experience.
Lyzr Workflow
Problem Statements
Manual KYC processes are slow, error-prone, and insufficiently scalable for modern digital onboarding needs. Financial institutions and fintech platforms face several key challenges:
- Fragmented Workflows: Document upload, identity checks, sanctions screening, and compliance reporting are typically siloed across different tools and teams.
- Operational Bottlenecks: Human-driven reviews increase onboarding time, leading to drop-offs and poor customer experience.
- Fraud & Compliance Risk: Weak liveness detection and lack of continuous monitoring leave gaps in fraud prevention and AML compliance.
- Lack of Explainability: Regulatory audits require detailed logs and decision trails, which are often unavailable or poorly structured in legacy systems.
- Limited Integration with Trusted Data Providers: Many orgs rely on homegrown or outdated databases for verification, instead of leveraging trusted APIs like USPS, LexisNexis, or Onfido.
This blueprint solves these issues by providing an agentic automation layer that integrates external APIs, orchestrates task-specific AI agents, and generates explainable compliance outputs — all while enabling human override and model retraining loops.
How the Agent Works
The KYC Agent Workflow is composed of a chain of specialized AI agents, each responsible for a distinct verification or compliance task. The system is modular and extensible, with each agent optionally integrating with trusted third-party APIs and services.
Here’s the step-by-step agent flow:
1. Document Processing Agent: Accepts uploaded identity documents such as passports and IDs.
Extracts key fields using OCR tools from:
- AWS Textract
- Google Cloud Vision
- Azure Cognitive Services
- OmniAI
Passes structured identity data downstream for further processing.
2. Address Verification Agent: Validates extracted address details using trusted address registries:
- Melissa
- USPS Address API
Ensures the address is real, mailable, and compliant with onboarding standards.
3. Sanctions Check Agent: Verifies customer identity against global sanctions, watchlists, and politically exposed person (PEP) databases via:
- LexisNexis Risk Solutions
- Experian
- Trulioo
Returns match confidence and flags high-risk profiles.
4. Liveness Verification Agent: Confirms that the user is physically present and matches the ID photo.
Integrates with biometric and facial liveness providers:
- Onfido
- iProov
- Jumio
Detects spoofing attempts and ensures face-ID consistency.
5. Compliance Orchestration Engine: Central controller that routes verification results and escalates cases.
- Triggers additional compliance agents based on risk scores and overrides.
- Manages decision workflows, audit trails, and agent intercommunication.
6. AML Risk Dashboard Agent:
- Aggregates verification data and risk indicators.
- Generates a real-time Risk Score for each customer profile.
- Supports alerting and escalation based on institution-defined thresholds.
7. Reporting & Filing Agents: Files suspicious activity reports (SAR) and supports regulatory requests.
Includes:
- SAR Filing & Reporting Agent (FinCEN XML/CSV submission)
- Regulatory Response Agent (314(b), subpoenas, internal queries)
8. Oversight & Monitoring Agents: Maintain an immutable record of system decisions and user actions.
Includes:
- Audit Trail & Evidence Logger Agent.
- Real-time role-based access logging and data provenance.
9. Continuous Learning Loop:
- Case Feedback & Model Trainer Agent ingests overrides, reviewer comments, and edge cases.
- Feeds updates back into the orchestration engine for retraining and continuous system improvement.
Final Outputs
- KYC Verified Profile → Structured data with identity, address, sanctions, and liveness check results.
- Risk Score → System-generated score for fraud and compliance risk, reviewed in real time.
- Audit Log → Timestamped, traceable decision trail for every onboarding event.
- Regulatory Reports → Ready-to-export SAR, AML reports, and compliance summaries.
Benefits & Capabilities
- Modular AI Agent Architecture: Each verification step—OCR, address, sanctions, biometrics—is managed by its own agent, allowing scalable, low-code customization and fast updates.
- End-to-End Digital Onboarding: Automates the entire KYC process: from first document upload to final compliance reporting—reducing drop-offs and review times.
- Real-Time Sanctions & Risk Screening: Instant screening against OFAC, FATF, and PEP lists using LexisNexis and Trulioo APIs. Risk scoring adjusts dynamically based on input patterns.
- Audit-Ready Logs & Evidence Capture: Every decision is logged with timestamps, agent metadata, and raw inputs—fully compliant with FinCEN, FFIEC, and internal audit needs.
- Smart Escalation & User Support: When verification fails, an optional support agent engages the user to collect missing information and reduce churn.
Tech Stack
- LLMs: GPT-4, Claude 3
- OCR / IDP: AWS Textract, Google Vision, Azure, OmniAI
- Sanctions & Risk Checks: LexisNexis, Experian, Trulioo, OFAC
- Biometric / Liveness: Onfido, Jumio, iProov
- Address Verification: Melissa, USPS
- Dashboard & Risk Scoring: Lyzr AML Dashboard Agent, internal risk models
- Audit & Reporting: FinCEN-ready XML/CSV filings, 314(b) Response Agent
- Orchestration Engine: Lyzr Compliance Engine
- Agent Framework: Lyzr AI Agent API
- Hosting: AWS / VPC for secure deployment
AML Agent
Overview
This blueprint presents a Modular AI-Powered AML Agent, a multi-agent system designed to detect and report suspicious financial activity in accordance with U.S. anti-money laundering (AML) laws and regulations. Built using Bolt, this system decomposes core AML functions—such as customer due diligence (CDD), transaction monitoring, sanctions screening, risk assessment, and SAR filing—into dedicated AI agents. Unlike traditional AML systems, which are often monolithic, opaque, and vendor-dependent, this approach offers a flexible, transparent, and auditable compliance framework. Each agent can function autonomously, be retrained or swapped, and easily integrates with internal systems or external data sources such as OFAC, FinCEN, credit bureaus, and banking core systems. The result is a more adaptive, explainable, and efficient AML process that meets regulatory expectations while improving analyst productivity and investigation outcomes.
Lyzr Workflow
Problem Statements
Traditional AML systems in U.S. banking are often disjointed, rule-heavy, and slow to adapt to evolving threats and compliance requirements. Many rely on static rule engines, manual review queues, or siloed third-party tools that cannot scale to meet the demands of modern transaction volumes or dynamic customer risk profiles. As regulations tighten and financial crime tactics become more sophisticated, institutions struggle to modernize without expensive replatforming or extended vendor lock-in. Additionally, legacy systems lack the transparency and modularity needed to explain risk decisions or customize thresholds for different segments or geographies. This leads to operational bottlenecks, high false-positive rates, and compliance risk exposure. Financial institutions need a modular, AI-native AML solution that can intelligently monitor behavior, dynamically assess risk, support rapid investigations, and generate auditable, regulator-ready outputs across the entire financial crime lifecycle.
How the Agent Works
When a user initiates activity—whether during onboarding, transaction processing, or ongoing account monitoring—an orchestrated sequence of intelligent agents is activated to perform real-time detection, behavioral risk assessment, and regulatory compliance screening. Each agent operates independently yet shares context through a unified orchestration layer, ensuring auditability, traceability, and compliance alignment across the entire AML lifecycle.
1. KYC/Customer Due-Diligence Agent: Extracts and verifies customer identity details from uploaded documents using OCR, validates authenticity, and assigns a base risk profile. Connects to third-party KYC databases and performs liveness checks when needed.
2. Transaction Monitoring Agent:Analyzes real-time and historical transaction behavior to detect suspicious patterns—such as structuring, rapid fund movement, or geographic anomalies. Tags transactions for risk evaluation or escalation.
3. Watchlist Screening Agent: Cross-checks customer and transaction metadata against multiple compliance sources including:
- OFAC SDN list
- Politically Exposed Persons (PEPs)
- Adverse media
- Internal blacklists
Triggers alerts for flagged matches.
4. Risk Profiling Agent: Combines insights from KYC, transaction behavior, and watchlist results to assign a dynamic AML risk score. Risk levels (Low, Medium, High) determine the next action path—monitor, escalate, or block.
5. Case Management Agent: If risk thresholds are exceeded, this agent creates a structured case containing evidence, metadata, and preliminary agent findings. Cases can be routed to analysts or escalated for SAR generation.
6. SAR Generation Agent: Prepares a fully populated Suspicious Activity Report (SAR) aligned with FinCEN XML/CSV format. Includes transaction narratives, KYC data, and triggering event details. May optionally submit directly to regulators.
7. Master Transaction Optimizer: Acts as the central coordination point for post-SAR decisions. Routes data to output agents for reporting, dashboarding, filing, or retraining. Also enforces policy outcomes (hold, flag, or escalate transactions).
8. AML Risk Dashboard Agent: Presents a real-time view of high-risk accounts, open alerts, escalation rates, and compliance KPIs. Helps AML officers and regulators monitor systemic risk across the organization.
9. SAR Filing & Reporting Agent: Handles SAR submission to FinCEN or other authorities. Maintains submission logs, timestamps, and validation statuses. Supports versioning and regulatory API integrations.
10. Audit Trail & Evidence Logger Agent: Stores an immutable, time-stamped trail of every decision made by each agent. Captures user actions, overrides, and system logs to support internal audit and compliance reviews.
11. Regulatory Response Agent: Supports structured information sharing (e.g., 314(b) requests), subpoenas, and internal inquiries. Connects historical records and decision logs to assist with legal investigations.
12. Case Feedback & Model Trainer Agent: Collects analyst feedback on resolved cases (confirmed, false positive, etc.) and uses this to fine-tune rules, retrain ML models, and reduce future alert fatigue.
Final Outputs
- Structured AML Cases → Auto-generated with agent metadata and evidence trails
- Real-Time Risk Scores → Dynamic and explainable scores based on activity, behavior, and identity
- Regulatory Filings → FinCEN SARs prepared in XML/CSV and submission-ready
- Audit Logs → Immutable, searchable trails for all actions, agent outputs, and human overrides
Benefits & Capabilities
- Modular AI Agent Framework for AML: Each core AML function—identity verification, transaction monitoring, sanctions screening, risk profiling, case management, and regulatory filing—is handled by a dedicated, independently deployable agent. This modular architecture enables scalable deployment, targeted improvements, and seamless integration with existing compliance ecosystems, core banking platforms, or RegTech tools.
- End-to-End Risk Intelligence & Identity Analysis: The system automates the entire AML lifecycle—from KYC due diligence and behavioral monitoring to case escalation and SAR filing. Agents perform ID parsing, document validation, transaction risk detection, and dynamic risk scoring to deliver faster, more consistent AML decisions with minimal human intervention.
- Real-Time Sanctions Screening & Dynamic Risk Scoring: Customers and transactions are screened instantly against OFAC, PEP, and global AML watchlists. Context-aware risk scoring adapts in real-time based on user behavior, transaction geography, frequency, and thresholds—ensuring prompt identification of high-risk profiles.
- Audit-Ready Reporting for Compliance: All agent actions, decisions, and inputs are automatically compiled into a tamper-evident audit trail. The system supports native output formats for FinCEN SAR filings, internal audits, and FFIEC compliance reviews, ensuring regulators and internal stakeholders have full visibility.
- Smart Escalation & User Recovery Workflow: When verification anomalies arise—such as mismatches, high-risk triggers, or inconclusive screenings—an optional support agent proactively engages the user. This improves resolution time, reduces false positives, and lowers abandonment rates while maintaining full regulatory traceability.
Tech Stack
- LLMs: GPT-4, Claude 3
- Identity & Verification: Onfido, Jumio, iProov, AWS Textract
- Sanctions / Risk Checks: LexisNexis, Experian, Trulioo, OFAC, FATF
- Reporting & Filings: FinCEN XML/CSV formats, FFIEC formats
- Transaction Monitoring: Real-time + historical behavior analysis via internal engine
- Audit & Oversight: Agent Trail Logger, Audit Trail Generator
- Agent Orchestration: Lyzr AI Agent API
- Hosting: AWS / On-prem deployment
- Dashboard & Compliance: AML Risk Dashboard Agent
Self-Improving Customer Support Agent
Overview
This blueprint outlines a self-improving Customer Support Agent, a sophisticated multi-agent system designed to efficiently manage customer support interactions across chat, email, voice, and messaging platforms. Leveraging an integrated knowledge base, the agent autonomously resolves queries and continuously learns from customer and internal human-agent interactions. While traditional platforms like ServiceNow and Intercom have introduced AI capabilities, they typically offer mere guidance through third-party resources. In contrast, Lyzr’s self-improving agent adopts a proactive, solution-oriented persona, directly addressing and solving customer issues, rather than redirecting users to external resources. This agent continuously enhances its capabilities, ensuring progressive improvement with every interaction.
Lyzr Workflow


Problem Statements
Traditional customer support solutions, including established platforms like ServiceNow and Intercom, often provide AI agents that can manage basic customer interactions but fall short of directly resolving customer queries. Typically, these agents merely guide customers to third-party resources or provide links requiring further customer action. This passive approach can lead to customer frustration, prolonged resolution times, and increased workload for human support agents. Moreover, existing agents generally lack robust self-learning capabilities, resulting in limited improvement in their effectiveness over time. To address these challenges, organizations need an intelligent, multi-channel support agent that can autonomously resolve customer queries, learn from every interaction, and continuously enhance its capabilities, thereby significantly improving customer satisfaction and operational efficiency.
How the Agent Works
- Execute RAG on the knowledge base to find answers.
- Consulting structured databases through a Text-to-SQL agent to extract quantitative data.
- Interacting with third-party APIs via a model context protocol (MCP) to gather additional information from external systems.
Final Outputs
- Resolved Query Notification → Delivered to the user via the same channel.
- Escalation Summary → Packaged for human agents with full context.
- Knowledge Base Enrichment → Used to train the LLM and update QA systems.
Benefits & Capabilities
- Self-Learning and Continuous Improvement: Each customer interaction triggers a self-learning process, refining the agent’s responses and enhancing the underlying LLM.
- Comprehensive Multi-Channel Support: Seamlessly handles customer inquiries across various channels, including voice, chat, email, and messaging platforms.
- Automated and Intelligent Issue Resolution: A sophisticated Master Issue Resolver coordinates resolution efforts, minimizing manual intervention while maximizing accuracy and efficiency.
- Integration with Organizational Intelligence: Built on Lyzr’s AgentMesh technology, the system contributes to an integrated Organizational General Intelligence (OGI), creating collective insights and intelligence from all AI Agents built across the enterprise.
Tech Stack
- LLMs: GPT-4, Claude 3
- RAG Engine: Lyzr + Vector DB (Qdrant / Weaviate)
- Channel Integrations: Slack, Microsoft Teams, WhatsApp, Email (SMTP/IMAP), Voice APIs
- Query Orchestration: Master Issue Resolver, Lyzr AI Agent API
- Data Access Agents: Text-to-SQL Agent, API Agent (via MCP)
- Human Escalation: Ticketing System (Zendesk, Freshservice), Escalation Summarizer
- Learning Feedback Loop: Training Queue, QA Updater, Model Tuner
- Hosting: AWS / On-premise
- AgentMesh Intelligence Layer: Enables cross-agent learning & enterprise memory
Knowledge Search Agent
Overview
This blueprint outlines a Modular AI-Powered Knowledge Agent for Banking, a multi-agent system designed to streamline and scale access to institutional knowledge across banking operations. Built using Bolt, this system transforms knowledge retrieval into an intelligent, conversational experience for employees, compliance officers, and customer support teams. It decomposes key knowledge functions—such as policy interpretation, procedure lookup, regulatory reference, internal system FAQs, and cross-departmental queries—into specialized agents. Unlike traditional knowledge bases, which are static, hard to navigate, and siloed by team or format, this architecture delivers a dynamic, contextual, and auditable search experience. Each agent can be retrained, swapped, or fine-tuned independently, and integrates easily with internal repositories like SharePoint, Confluence, banking core systems, and document management platforms. The result is a more intelligent, scalable, and user-friendly way to surface institutional knowledge while improving operational speed and reducing repetitive support queries.
Lyzr Workflow
Problem Statements
In most banking environments, institutional knowledge is fragmented across siloed systems—static intranet portals, compliance binders, outdated SharePoint folders, and inconsistent tribal knowledge. Employees waste time searching for policies, asking repetitive questions, or relying on outdated documentation. Traditional knowledge management systems are often static, keyword-based, and disconnected from day-to-day workflows. This leads to high support volumes, slow onboarding, inconsistent policy enforcement, and operational inefficiencies. As compliance requirements and internal processes evolve rapidly, financial institutions struggle to maintain a single source of truth that is both reliable and accessible. What’s needed is a modular, AI-native knowledge agent that can retrieve, summarize, contextualize, and present information in a human-like, conversational way—reducing friction, ensuring compliance, and enabling employees to focus on value-added tasks rather than repetitive lookup work.
How the Agent Works
The Knowledge Search for Banking Agent Workflow is designed to provide fast, accurate, and context-rich responses to employee questions using a modular AI-agent architecture. The flow is initiated by a user query and orchestrated through the following agents:
1. User Input Agent: Captures the user’s natural language query through chat, form input, or voice. It parses intent, context, and any metadata required for accurate routing.
2. Knowledge Retrieval Agent: Uses semantic search and vector embeddings to retrieve the most relevant documents or content snippets from the institutional Knowledge Base. This includes policy documents, SOPs, regulatory guidelines, training materials, and FAQs.
3. Compliance Orchestration Engine: Routes the retrieved knowledge to the appropriate downstream agents for processing, formatting, summarization, quality checks, and learning updates.
4. Knowledge Summarization Agent: Converts retrieved information into concise executive summaries, cheat sheets, or contextual overviews, optimized for quick consumption by the end user.
5. Insight Packaging & Delivery Agent: Formats and delivers the final output into the user’s preferred channel or format—such as Slack messages, PDF documents, email, or slide decks.
6. Knowledge Gaps & Accuracy Monitoring Agent: Continuously monitors the quality of results by detecting outdated content, hallucinations, or low-confidence answers. Flags issues to maintain trust and compliance.
7. Auto-Improver Agent: Learns from user interactions to fine-tune prompts, reformat queries, or re-rank document retrieval strategies, improving performance over time.
8. Case Feedback & Model Trainer Agent: Incorporates user feedback to retrain embedding models, update source documents, or alert knowledge managers. Ensures continuous learning and content evolution.
Final Outputs
- Contextual Response → Delivered as chat reply, email, or formatted document.
- Source Traceability → Includes citations and doc links.
- Model Feedback → Logged for training and gap identification.
Benefits & Capabilities
The Knowledge Search for Banking Agent Workflow delivers significant operational and strategic advantages by transforming how employees access and interact with institutional knowledge.
- Natural Language Understanding: Interprets complex user queries in plain language and maps them to relevant policies, procedures, or internal knowledge without requiring keyword-based searches.
- Contextual Retrieval with RAG: Leverages advanced retrieval-augmented generation (RAG) to pull the most relevant information from distributed knowledge bases, ensuring responses are grounded in trusted content.
- Multi-Format Insight Delivery: Delivers outputs in formats tailored to user preferences and workflows, including Slack messages, PDFs, emails, or slides.
- Real-Time Knowledge Monitoring: Continuously assesses the freshness and accuracy of retrieved information, flagging outdated content, hallucinations, or low-confidence responses for review.
- Feedback-Driven Learning Loop: Captures user feedback at scale to automatically fine-tune prompts, update rankings, and retrain embeddings, keeping the system adaptive and user-centered.
- Modular & Auditable Architecture: Each agent is modular, explainable, and independently upgradable—enabling audit trails, system transparency, and easy integration with enterprise systems (e.g., SharePoint, banking core systems, policy repositories).
Tech Stack
- LLMs: GPT-4, Claude 3
- Retrieval Engine: RAG (Lyzr + Pinecone / Qdrant)
- Sources: SharePoint, Confluence, GDrive, Policy Docs, Core Banking KBs
- Summarization Agent: GPT-4 + Lyzr Prompt Engine
- Compliance Orchestration: Internal Policy Checker, Risk Validator
- Output Channels: Slack, Teams, Email, Web UI, PDF Generator
- Monitoring & Feedback Agents: Confidence Scorer, Auto-Improver, Feedback Trainer
- Hosting: AWS / On-Premise / Private Cloud
Refund Processing Agent
Overview
RefundPro AI is an AI-powered platform designed to revolutionize the refund process for businesses and financial institutions. The platform leverages intelligent agents to automate and streamline every step of the refund cycle—from the initial submission of a refund request to secure transaction processing and real-time customer support. By integrating advanced AI techniques such as Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning-based fraud detection, RefundPro AI minimizes manual intervention, reduces processing time, and enhances accuracy and compliance.
Problem Statements
Conventional refund processing systems are often hampered by slow, labor-intensive procedures, leading to delays, errors, and increased operational costs. Manual verification and outdated fraud detection mechanisms contribute to longer turnaround times and can result in dissatisfied customers. Moreover, the lack of integration between various systems leaves organizations unable to effectively manage high volumes of transactions while ensuring regulatory compliance.
Solution: RefundPro AI addresses these challenges by integrating sophisticated AI capabilities to:
- Automate Data Extraction and Verification: Leverage OCR and NLP to quickly extract and validate key details from refund-related documents, ensuring that all data is accurate and consistent with internal records.
- Enhance Fraud Detection: Deploy machine learning models to analyze historical refund data, detect anomalies, and generate risk scores that help flag potentially fraudulent requests for further review.
- Streamline Customer Support: Utilize an AI-powered chatbot available 24/7 to guide customers through the refund process, provide status updates, and answer queries, thereby reducing the burden on human agents.
- Optimize Transaction Processing: Seamlessly integrate with payment gateways for secure and rapid refund transactions, complete with comprehensive audit logs and real-time status notifications to ensure transparency and regulatory adherence.
Lyzr Workflow
How the Agent Works
This blueprint describes the sequential and collaborative process by which RefundPro AI manages refund requests. Intelligent agents work together to automate data extraction, verify legitimacy, detect fraud, and ultimately execute refunds reliably, all while maintaining comprehensive audit trails and continuous learning.
1. Refund Request Intake Agent: The workflow begins by receiving refund requests from multiple channels:
- Digital Submissions: Web forms, mobile apps, and API integrations.
- Batch Uploads: CSV/XML files from partner systems.
- Document Uploads: Scanned receipts, invoices, or emails.
This agent parses and standardizes the incoming refund requests, ensuring that the data is prepared for further processing.
2. Refund Routing Optimizer Agent: Serving as the orchestration hub, this agent routes each refund request to the appropriate downstream agents based on criteria such as refund type, request complexity, and transaction history. It determines which areas require expedited processing versus deeper analysis.
3. AI Document Verification Agent: Leveraging advanced OCR and NLP techniques, this agent processes all submitted documents to extract and validate key data (e.g., transaction IDs, purchase dates, refund amounts). It compares this extracted information against internal transaction records to ensure consistency.
4. AI Fraud Detection Agent: This agent employs machine learning models trained on historical refund data to assess the risk profile of each request. It analyzes patterns, anomalies, and behavioral signals to assign a risk score, flagging suspicious transactions for further review.
5. Pre-Screening Eligibility and Compliance Agent: Conducts early-stage checks against company policies and regulatory criteria, such as refund windows and eligibility rules. This step quickly filters out requests that do not meet basic criteria, thus streamlining the overall process.
6. AI Refund Simulation Agent: Before a final decision is made, this agent runs simulations to predict the impact of various refund scenarios. It projects:
- Processing Time and Cost: Estimates the operational implications.
- Fraud Risk Impact: Evaluates potential losses or system vulnerabilities.
- Data Discrepancies: Assesses the likelihood of errors in submitted data.
These simulations help refine the decision-making process by weighing trade-offs.
7. AI Compliance Agent: Performs an in-depth compliance review by querying global regulatory databases, internal policy repositories, and fraud watchlists. This agent ensures that the refund request adheres to all necessary financial and legal standards.
8. Master Refund Decision Optimizer: Synthesizes inputs from the document verification, fraud detection, pre-screening, simulation, and compliance agents. It makes the final call on whether to approve, deny, or escalate the refund request for manual review. Once the decision is made, downstream agents are activated to execute the outcome.
9. Refund Dashboard Agent: Generates dynamic visual dashboards for internal stakeholders, summarizing:
- Refund Approvals and Rejections: A clear view of the current refund status.
- Risk Scores and Compliance Outcomes: Insights into flagged issues.
- Operational Metrics: Processing times, cost savings, and performance trends.
10. Audit Trail & Logging Agent: Creates secure, detailed logs of every decision point and agent action throughout the refund process. This comprehensive record-keeping ensures audit readiness and facilitates future analyses or compliance reviews.
11. Feedback Learning Loop Agent: Collects post-refund data, including customer feedback, processing delays, manual override instances, and incurred costs. This information is fed back into the AI models to continuously enhance accuracy and efficiency.
12. Reporting & Filing Agent: Automates internal reporting and external regulatory filings by compiling necessary data into standardized formats (CSV, XML, or direct system submissions), ensuring that all compliance requirements are met.
13. Refund Execution System Agent: Once a refund is approved, this agent activates the final transaction process by interfacing with integrated payment gateways or banking systems. It securely processes the refund and communicates confirmation back to the customer and internal systems.
Benefits & Capabilities
- Modular AI Agent Framework: Every critical refund function—document verification, fraud detection, eligibility screening, and transaction execution—is managed by dedicated, independently deployable agents. This modular design facilitates rapid updates, easy integration with existing payment systems, and flexible scalability.
- Comprehensive Data Extraction & Verification: The system automates the entire refund validation lifecycle—from document OCR/NLP processing through to cross-checking transaction records—ensuring refunds are processed accurately and swiftly with minimal manual intervention.
- Instant Fraud Screening & Risk Analysis: Real-time evaluation against historical data and behavioral patterns allows for immediate detection of suspicious refund requests. Intelligent risk profiling dynamically adjusts evaluations based on transaction history, refund reasons, and contextual signals to reduce fraud and operational risk.
- Audit-Ready Logging & Compliance: Every action, decision, and data interaction is captured in a fully traceable audit trail. This robust logging mechanism simplifies internal audits and regulatory reviews, ensuring the refund process meets stringent financial compliance standards.
- Proactive Escalation & Customer Support: An integrated support agent proactively engages users if verification steps are ambiguous or fail, guiding them through resolution processes. This reduces customer drop-offs and minimizes the need for manual reviews, ultimately enhancing the overall user experience.
Regulatory Monitoring Agent
Overview
This blueprint presents a Modular AI-Powered Regulatory Monitoring Agent, a multi-agent system engineered to keep banks and financial institutions continuously compliant. Leveraging real-time tracking, advanced generative AI, and robust indexing capabilities, the Regulatory Monitoring Agent dynamically monitors regulatory websites, extracts key documents, and enables compliance teams to interact via natural language queries. Unlike traditional systems that are fragmented and manual, this solution offers a flexible, transparent, and auditable compliance framework that integrates seamlessly with internal systems and external data sources, ensuring that regulatory updates are always at your fingertips.
Problem Statements
Modern regulatory environments are in constant flux, creating significant challenges for financial institutions:
- Manual Tracking Overload: Regulatory bodies frequently update guidelines and issue new notices, and traditional manual monitoring methods often result in delays or overlooked updates.
- Compliance Risks: Failure to capture or accurately interpret regulatory changes can lead to non-compliance, triggering penalties and damaging reputations.
- Fragmented Information Sources: Regulatory documents are dispersed across various platforms and formats, making consolidation and rapid search cumbersome.
- Resource-Heavy Queries: Dependence on legal experts for interpreting complex regulatory language creates bottlenecks and escalates operational costs.
Financial institutions require a modular, AI-native regulatory solution that automates the monitoring, indexing, and interpretation of regulatory data—empowering compliance teams to make informed decisions swiftly and accurately.
Lyzr Workflow
How the Agent Works
- Function: Continuously scans and scrapes key regulatory websites for updates, circulars, guidelines, and notifications.
- Technical Detail: Utilizes scheduled crawlers and adaptive web parsers to extract relevant content from diverse formats (HTML, PDFs, XML).
- Function: Processes and indexes scraped regulatory documents, categorizing them by publication date, act number, and regulatory topic.
- Technical Detail: Implements vectorization and metadata tagging to enable rapid retrieval of documents in response to queries.
- Function: Provides an interactive chat-based interface that allows compliance teams to ask natural language queries about regulations.
- Technical Detail: Powered by Lyzr’s proprietary Generative AI framework, it leverages NLP and semantic search to deliver precise, context-aware responses sourced from indexed documents.
- Function: Monitors the regulatory landscape for the latest changes and categorizes them for immediate review.
- Technical Detail: Employs real-time data ingestion and time-series analysis to flag and prioritize new regulatory content.
- Function: Presents a comprehensive, structured dashboard that organizes regulatory documents, highlights critical policies, definitions, and compliance points.
- Technical Detail: Features interactive visualizations and filters to allow users to navigate through updates quickly.
- Function: Triggers proactive alerts via email, Slack, or internal dashboards when new regulatory notices or significant updates are detected.
- Technical Detail: Integrates with common communication platforms and leverages webhook APIs to deliver timely notifications.
- Function: Records every action, decision, and query in a tamper-evident log, ensuring a fully auditable compliance trail.
- Technical Detail: Uses immutable logging mechanisms to support internal audits and regulatory reviews.
- Function: Facilitates seamless integration with existing compliance management tools and external regulatory databases.
- Technical Detail: Provides RESTful APIs and standardized data connectors to ensure smooth data exchange and interoperability.
Benefits & Capabilities
- Modular AI Agent Framework for Regulatory Monitoring: Each key function—document scraping, indexing, natural language querying, alerting, and audit logging—is managed by a dedicated, independently deployable agent. This modularity enables scalable deployment, targeted improvements, and effortless integration with core compliance systems.
- Real-Time Regulatory Insights & GenAI-Powered Querying: The system automates the entire regulatory lifecycle—from continuous monitoring and dynamic indexing to natural language processing and real-time query responses. This ensures that compliance teams receive accurate, contextually relevant insights with minimal manual effort.
- Proactive Compliance & Automated Alerts: By maintaining an up-to-date repository of regulatory changes and pushing timely alerts, the agent empowers teams to stay ahead of emerging risks and regulatory updates, minimizing potential compliance breaches.
- Audit-Ready Reporting & Transparent Operations: All agent actions are compiled into a tamper-evident audit trail, providing full visibility for internal audits and regulatory reviews. This transparency supports trustworthy, verifiable compliance processes.
- Scalable Integration & Customization: The Regulatory Monitoring Agent’s API-first architecture enables seamless integration with existing compliance management systems and can be easily customized to monitor additional regulatory bodies—be it SEBI, RBI, or any global regulator.
Marketing
E-Book Generator Agent
Overview
This blueprint presents an AI eBook Generator Agent, a powerful content creation system designed to help marketers and content teams rapidly produce high-quality, SEO-optimized eBooks. By combining structured inputs (such as title, purpose, and audience) with live web research and internal knowledge sources, the agent autonomously drafts long-form content aligned with brand voice and audience needs. Unlike generic AI writing tools that produce disconnected or templated content, the Lyzr eBook Generator Agent leverages multi-agent collaboration to synthesize insights, incorporate SEO recommendations, and generate narrative-driven eBooks that are ready for review and publication. With every project, the agent becomes more effective—learning from user feedback, brand-specific glossaries, and evolving tone preferences to improve its performance and relevance over time.
Lyzr Workflow
Problem Statements
Marketers and content teams often face significant bottlenecks when producing long-form content like eBooks. Traditional writing workflows can be time-consuming, resource-heavy, and inconsistent in quality, particularly when relying on generic AI tools or disconnected freelancers. These solutions frequently require extensive human rewriting and rarely align with evolving SEO strategies or brand-specific terminology. Additionally, without a clear system for learning from feedback or reusing proprietary research, content quality remains stagnant and inefficient. To overcome these challenges, organizations need an intelligent content generation agent—one that can autonomously draft, optimize, and refine eBooks, while learning from inputs, reviews, and outcomes to continuously enhance its content generation capabilities.
How the Agent Works
When a marketer initiates a request with inputs such as title, purpose, audience, and description, the AI eBook Generator Agent begins the content creation process. This agent synthesizes content by drawing from live web research, internal research materials, and existing brand knowledge to produce a comprehensive draft tailored to the specified audience and objective.
Once the initial draft is generated, it is sent for review by a Content Editor. The editor evaluates tone, clarity, factual accuracy, and brand alignment. Any edits or feedback are fed back into the agent loop to continuously enhance writing quality and alignment with editorial standards.
The refined draft is then passed to the AI SEO Optimization Agent, which leverages tools like Ahrefs, Google Keyword Planner, SEMrush, and Moz to analyze and optimize the content. It enriches the eBook with target keywords, improves meta elements, adjusts readability, and ensures that the content is aligned with current search engine ranking best practices.
To ensure terminology consistency and domain-specific language, the agent references Proprietary Glossaries and Terminology Databases, tailoring phrasing and structure to match organizational or industry standards.
Finally, the optimized eBook draft is routed to a Content Strategist for approval. This human-in-the-loop ensures the final content aligns with overall marketing and campaign goals. Upon approval, the content is published in eBook format, ready for distribution across channels.
Throughout the process, all agent interactions—including editor feedback, SEO performance signals, and strategist preferences—are captured as training signals. These signals help fine-tune the behavior of both the eBook Generator and SEO Optimization agents, resulting in ongoing improvements in quality, brand voice, and discoverability over time.
Final Outputs
- Polished eBook → Fully formatted, reviewed, and optimized for search and distribution.
- SEO Report → Includes keywords used, readability score, and metadata alignment.
- Training Signals Captured → Agent learns from every project.
Benefits & Capabilities
- Self-Learning and Content Refinement: With each project, the agent incorporates feedback from editors, strategists, and performance data to improve narrative quality, tone, and structure—continuously fine-tuning the underlying LLM for future content generation.
- Automated Long-Form Content Creation: Generates high-quality, structured eBooks based on marketer inputs, research materials, and live web sources—dramatically reducing content production time and human effort.
- SEO Optimization with Industry-Standard Tools: Integrates directly with platforms like Ahrefs, Google Keyword Planner, and Moz to embed relevant keywords, enhance searchability, and align content with current SEO trends and algorithms.
- Collaboration-Ready Workflow: Designed for human-in-the-loop review and strategic alignment, enabling content editors and strategists to easily provide feedback, approve drafts, and maintain brand consistency.
- Terminology and Brand Consistency at Scale: Leverages proprietary glossaries and structured terminology databases to ensure all content aligns with internal language, industry terms, and brand voice.
- Integrated Organizational Intelligence: As part of Lyzr’s AgentMesh ecosystem, the eBook Generator Agent contributes to a broader Organizational General Intelligence (OGI)—learning from and enriching insights shared across all connected agents in the enterprise.
Tech Stack
- LLMs: GPT-4, Claude 3
- SEO Integration: Ahrefs, Moz, SEMrush, Google Keyword Planner
- Feedback Capture: In-app comments, CMS integrations
- Editor Tools: Grammarly, Hemingway App, Style Guide Plugin
- Output Formats: PDF, DOCX, HTML, CMS Uploads
- Prompt & Flow Engine: Lyzr’s Bolt Framework
- Training Signals: Editor feedback, performance analytics, user corrections
Linkedin Marketer
Overview
Lyzr’s LinkedIn Marketer Agent automates thought leadership, hiring posts, product updates, and campaign content directly on LinkedIn. Trained on your brand voice and target persona, it generates, schedules, and analyzes posts—so your founders, leaders, or marketers can focus on growing the business, not writing copy.
Lyzr Workflow
Problem Statements
- Inconsistent posting habits: Busy teams forget to post or lose momentum after 1–2 weeks of trying.
- Too many tools, not enough output: From Notion to Buffer to ChatGPT to Canva—content creation is scattered and disjointed.
- Generic, low-performing content: Most AI tools create bland, templated posts that don’t reflect your brand or engage your audience.
How the Agent Works
- User input or topic detection: The agent pulls ideas from calendar events, product updates, blog links, or prompts from the user.
- Brand voice & audience modeling: It personalizes the tone based on company voice, past high-performing posts, and the intended reader persona.
- Content generation: Posts are generated with hooks, formatting, emojis (if needed), and a clear CTA—tailored to the platform.
- Hashtag & timing optimizer: The agent suggests hashtags and best posting times for reach and engagement.
- Approval or auto-publish: Users can approve, edit, or directly publish via LinkedIn’s API.
- Engagement monitoring: The agent tracks comments, likes, and shares, surfacing top conversations to the user.
- Performance analytics: Post-wise analytics and weekly engagement summaries are shared via Slack or email.
- Learning loop: High-performing post patterns are used to improve future content recommendations.
Benefits & Capabilities
- Stay top of mind on LinkedIn: Maintain a consistent presence without investing hours every week.
- Posts that sound like you: Trained on your tone and past content—no generic “LinkedIn broetry” here.
- All-in-one content engine: Write, schedule, publish, and track—all from one conversational agent.
- Data-backed improvement: Learns what works and what doesn’t—improving post quality week after week.
Tech Stack
- LLM: GPT-4o, Claude 3 for post generation and tone modeling
- Scheduling & Publishing: LinkedIn API, Zapier integrations
- Analytics & Feedback: Post engagement tracker, inbuilt dashboard
- Knowledge Sources: Company blog, Notion, newsletters, product changelogs
- Memory Modules: Short-term (weekly content themes), Long-term (top posts, writing style)
- Agent Framework: Built with Lyzr’s AI Agent API
- AI Agents Deployed: Content Generator Agent, Tone Tuner Agent, Publishing Agent, Analytics Agent
Sales
RFQ Automation Agent
Overview
This blueprint introduces a Modular AI-Powered RFQ Agent, a multi-agent system designed to streamline and accelerate the response process for customer Request for Quotes (RFQs) across B2B sales teams. Built using the Bolt framework, the system breaks down core RFQ functions—such as email ingestion, document parsing, product matching, quote composition, escalation, and delivery—into specialized, interoperable AI agents. Unlike traditional quote processing methods, which rely heavily on manual entry, static templates, or siloed sales tools, this AI-native approach offers a scalable, intelligent, and auditable quoting workflow. Each agent can operate independently, be retrained or replaced, and integrate seamlessly with pricing catalogs, CRMs, ERPs, or communication tools like Slack and Outlook. The result is a faster, more accurate quoting process that improves sales productivity, reduces response time, and enhances the customer experience.
Lyzr Workflow
Problem Statements
Traditional RFQ workflows are often fragmented, slow, and highly manual—especially in mid-to-enterprise B2B environments. Sales teams must parse inbound emails, open and review complex attachments, lookup product codes, apply pricing rules, and manually assemble quote responses. This not only increases response times but also introduces the risk of human error, misquoting, or missing key details from customer requests. As quote volumes grow and customer expectations for rapid turnaround increase, organizations struggle to scale their processes without adding headcount or introducing delays. Legacy tools lack the flexibility to integrate unstructured inputs like PDFs or spreadsheets, and offer little to no automation in areas such as document parsing, quote generation, or escalation. Businesses need a modular, AI-native RFQ solution that can intelligently extract quote details, validate data, dynamically compose quotes, and ensure rapid, auditable delivery—all while keeping human sales reps in the loop for high-value or ambiguous cases.
How the Agent Works
When a customer or prospect submits a request for quote—via email, web form, or embedded interface—an orchestrated sequence of intelligent agents is triggered to automate data extraction, quote composition, validation, and delivery. Each agent operates independently yet remains contextually aware through a shared orchestration layer that ensures traceability, version control, and end-to-end quote lifecycle management.
1. EmailParserAgent: Ingests incoming RFQ emails and parses the sender metadata, subject line, and message body. If attachments are present, the agent flags them for document extraction. It classifies the request, identifies intent, and standardizes the input for downstream processing.
2. DocumentExtractionAgent: Applies OCR and Intelligent Document Processing (IDP) to attached files (PDFs, Excel sheets, images) to extract structured data such as product SKUs, quantities, due dates, and specifications. Multiple parsers may be used in parallel to ensure extraction accuracy and redundancy.
3. DataValidationAgent: Cross-references the extracted line items against the latest pricing catalog, SKU list, and inventory data. Validates formats, units, and special terms (e.g., volume discounts or shipping requests) to ensure completeness and correctness.
4. QuoteComposerAgent: Generates a structured quote based on validated data, applying predefined business rules such as tiered pricing, taxes, testing costs, and regional surcharges. The quote is formatted using a customizable template and saved as a draft for review or direct delivery.
5. EscalationAgent: If the system detects anomalies (e.g., invalid SKUs, missing quantities, conflicting delivery dates), this agent triggers an alert and routes the request to the inside
Benefits & Capabilities
- Modular AI Agent Architecture: Each stage of the RFQ process—from email ingestion and document parsing to quote composition and escalation—is handled by a specialized, independently deployable agent. This modularity enables rapid enhancements, easy integration with CRMs or ERPs, and streamlined updates without disrupting the full workflow.
- End-to-End Quote Automation: Automates the entire RFQ lifecycle, including email parsing, attachment extraction, line-item validation, and quote generation—dramatically reducing manual effort and accelerating response times for sales teams.
- Smart Validation & Dynamic Pricing: Integrates real-time product catalogs, business rules, and discount logic to ensure every quote is accurate, compliant, and competitively priced—minimizing errors and increasing customer satisfaction.
- Audit-Ready Workflow Visibility: Every step is logged and traceable, with decisions, escalations, and quote versions compiled into a transparent, version-controlled audit trail—ideal for internal reviews or customer success metrics.
- Intelligent Escalation & Sales Collaboration: When confidence is low or data is incomplete, the system automatically alerts the inside sales team via Slack or email. A built-in support flow enables rapid clarification and smooth handoff between AI and human agents.
Tech Stack
- LLM: GPT-4o, Claude 3 for parsing, summarization & quote generation
- Document Extraction: AWS Textract, Azure Form Recognizer, Langchain wrappers
- Email Parsing: Microsoft Graph API, Gmail API, Zapier
- Pricing Data: CRM (Salesforce, HubSpot), ERP (SAP, Oracle), Flat file lookup
- Communication Channels: Slack, Email, Web Portals
- Agent Framework: Lyzr’s AI Agent API
- AI Agents Deployed: EmailParserAgent, DocumentExtractionAgent, DataValidationAgent, QuoteComposerAgent, EscalationAgent, AuditLoggerAgent
Customer Service
Customer Sentiment Analysis
Overview
This blueprint outlines a Customer Sentiment Analysis Agent, designed to automate the extraction and interpretation of customer feedback from various digital sources. The system leverages modular agents to handle distinct tasks—from data collection to sentiment computation, visualization, and continuous learning. By integrating these components, the blueprint offers marketers, start-ups, and enterprises a streamlined approach to monitor customer sentiment and derive actionable insights with minimal manual intervention.
Lyzr Workflow
Problem Statements
Manual sentiment analysis involves many challenges:
- Fragmented Data Sources: Collecting data from various channels (social media, reviews, surveys) requires multiple disparate tools and methods.
- Data Quality Issues: Raw data is unstructured and noisy. Cleaning, normalizing, and preparing data for analysis is labor-intensive.
- Inconsistent Analysis: Without automated NLP, sentiment scoring is subjective and prone to bias, leading to inaccurate or delayed insights.
- Time and Cost Inefficiencies: Manual processes are time-consuming and resource heavy, slowing down response times and hindering proactive action.
- Scalability and Adaptability: As data volumes grow and language evolves (slang, emojis, misspellings), manual methods struggle to keep up, leaving businesses reactive rather than proactive.
Our agent addresses each of these pain points by automating data scraping, preprocessing, sentiment evaluation, and reporting in one integrated workflow.
How the Agent Works
- Data Collection Agent: Gathers real-time feedback from surveys, NPS, support logs, Twitter, Glassdoor, review platforms, and CRM notes.
- Data Preprocessing Agent: Cleans and structures noisy feedback using NLP techniques—tokenizing, normalizing, and handling emojis, misspellings, and slang.
- Sentiment Analysis Agent: Applies LLMs and pre-trained classifiers to detect sentiment (positive, negative, neutral) and emotion markers (anger, joy, frustration, etc.).
- Trend & Topic Aggregation Agent: Identifies common patterns, recurring themes, and sentiment shifts over time—broken down by product, region, channel, or segment.
- Insight Visualization & Reporting Agent: Generates executive-level dashboards and dynamic visualizations showing top pain points, trending feedback, and customer mood over time.
- Feedback & Learning Loop Agent: Learns from team feedback and labeled edge cases to continuously improve sentiment classification, trend detection, and topic models.
Final Outputs
- Sentiment Dashboard → Live view of customer mood across channels.
- Trend Reports → Monthly insights for marketing, product, and CX.
- Trigger Alerts → Real-time alerts when sentiment dips or spikes.
Benefits & Capabilities
- High Automation: Automates the entire workflow, cutting manual effort by up to 90%.
- Consistent Accuracy: Uses standardized NLP to eliminate bias and deliver reliable insights.
- Real-Time Insights: Tracks sentiment evolution for immediate, proactive actions.
- Scalability: Efficiently processes high volumes of data from multiple channels.
- Actionable Reporting: Converts data into clear, intuitive dashboards for quick decision-making.
- Continuous Improvement: Integrates feedback to adapt and update models over time.
Tech Stack
- LLMs & NLP Engines: GPT-4, Claude 3, Huggingface Transformers, TextBlob, NLTK
- Data Sources: Twitter API, Facebook API, Glassdoor, TrustPilot, Google Reviews
- Preprocessing Tools: spaCy, Regex, Custom Normalizers
- Trend & Topic Modeling: BERTopic, LDA, TF-IDF, sklearn
- Dashboarding Tools: Tableau, PowerBI, Streamlit, Dash
- Storage / DB: MongoDB, PostgreSQL, BigQuery
- Feedback Trainer Module: Human labeling UI, Lyzr Trainer API
- Hosting: AWS / On-Premise / Private Cloud