How Saksoft Accelerated Development with Lyzr’s AI Coding Agent

Developers spend 30–40% of their time navigating codebases instead of building new features. Add to that the constant need to ensure every new function matches project-specific styles, and what should be a quick task often stretches into days.

The cost isn’t just developer frustration, it’s slower delivery cycles, mounting inconsistencies, and missed opportunities.

This was the challenge Saksoft wanted to solve. They needed a way to give their teams instant access to the right code, while ensuring new code was generated in line with existing conventions. No more digging through endless files.

No more wasted hours in rework. That’s where Lyzr came in, with an AI-powered Coding Agent designed to understand repositories, process documentation, and generate project-ready code on demand.

Challenges Faced by Saksoft

1. Navigating Large Codebases

Developers struggled to locate specific functions, classes, or variables in sprawling repositories. Even simple requests, like finding all authentication-related functions, consumed hours that could have been used to deliver new features.

2. Inefficient Code Retrieval and Context

Traditional search methods lacked the ability to understand context, often returning incomplete or irrelevant results. Developers had to manually stitch information together, delaying delivery timelines.

Issue	Impact on Teams
Generic keyword-based search	Inaccurate or irrelevant results
No understanding of context	Developers wasted hours piecing information
Lack of metadata in results	Slower debugging and feature implementation

3. Error-Prone Code Generation

Writing new functionality required constant cross-checking to match project conventions. This manual process led to inconsistencies, rework, and delays — turning routine coding into a major productivity drain.

The Ideal Solution

Saksoft needed more than just a faster search tool, hey needed an intelligent assistant for their developers. The ideal solution would:

• Understand repositories in depth: analyze functions, classes, variables, and project structure.
• Enable natural language search: allow developers to query codebases in plain English and get precise, context-aware results.
• Generate project-ready code: produce new functions aligned with existing styles, logic, and conventions.
• Leverage documentation: use uploaded project documentation to strengthen contextual understanding.
• Ensure security and scalability: integrate seamlessly with existing workflows while maintaining enterprise-grade security.

In short, the solution had to reduce wasted effort, accelerate delivery, and give developers confidence that every piece of code, whether searched or generated, matched the standards of Saksoft’s projects.

How Lyzr Solved It

Saksoft leveraged Lyzr’s Coding Agent, a FastAPI-based AI solution designed to streamline development workflows. By integrating GitHub repositories, analyzing project structures, and using Retrieval-Augmented Generation (RAG) with Lyzr agents, developers gained context-aware assistance for both code search and generation.

The system intelligently interprets code and documentation, providing accurate and project-consistent code snippets while saving developers significant time.

Key Features

The Coding Agent provides a suite of features aimed at improving productivity and code quality:

Feature	Description
Project Management	Create, update, and manage projects that contain repositories and documentation.
Repository Integration	Add GitHub repositories, automatically clone them, and parse code elements such as functions, classes, and variables.
Documentation Addition	Upload textual documentation; the system processes it in chunks to enhance AI context via RAG.
Code Search	Search code using natural language queries, retrieving relevant snippets, file paths, and line numbers.
Code Generation	Generate new code aligned with repository logic, style, and conventions.

Workflow & Agent Architecture

The Coding Agent follows a clear multi-step workflow:

1. Repository Analysis
   • GitHub repositories are cloned to a temporary directory.
   • RepositoryAnalyzer parses code elements and stores structured data in MongoDB for RAG training.
2. Documentation Integration
   • Users provide project-related documentation.
   • Text is split into overlapping chunks, enhancing AI contextual understanding.
3. AI Agent Invocation
   • Each project has two dedicated agents:
     • Search Lyzr Agent: Handles natural language-based code retrieval.
     • Generate Lyzr Agent: Generates context-aware code snippets.
4. Query Handling
   • Developers submit queries via /code/search or /code/generate endpoints.
   • The system routes requests to the appropriate agent and returns code or search results.

AI Agents Overview

Agent	Role	Input	Output
Repo Analysis Agent	Extracts repository structure, API endpoints, database schemas, and UI components	GitHub repo	Structured insights stored for RAG training
Search Agent	Responds to natural language queries	Developer query	Relevant code snippets with metadata (file path, line number)
Generate Agent	Produces new code aligned with project conventions	Developer prompt	Context-aware code snippet consistent with repo style

Cloud Architecture & Security

• Hosting: AWS ECS runs the containerized backend.
• Database: Amazon DocumentDB stores project data, user info, and chat sessions.
• Security Measures:
  • Secure access to private repositories.
  • Data privacy maintained in cloud storage.
  • RAG system only uses project-specific code and documentation for context.

Results in Action

Lyzr’s Coding Agent transformed how Saksoft’s developers interact with their codebase. Below are real-world examples demonstrating its impact.

Code Search Example

• Scenario: A developer needed to locate all functions related to user authentication in a large repository.
• Query: “Find all functions handling user authentication.”
• Response:
  • A structured list of function names
  • File locations and line numbers
  • Relevant code snippets
• Impact: Developers could instantly access required code elements without manually scanning files, reducing search time dramatically.

Code Generation Example

• Scenario: A developer wanted to implement a logout feature consistent with the project’s existing coding style.
• Query: “Generate a function to handle user logout.”
• Response:
  • A fully formatted code snippet
  • Follows project conventions, logic, and structure
• Impact: Eliminates errors and ensures consistent code quality, enabling developers to focus on higher-level tasks.

Wrapping Up

By implementing Lyzr’s AI-powered Coding Agent, Saksoft significantly simplified its development workflow.

Developers now spend less time navigating codebases and more time building features, with confidence that every new function aligns with project conventions.

The combination of context-aware code search, automated generation, and seamless integration with repositories and documentation has reduced rework, accelerated delivery timelines, and improved overall code quality.

Lyzr’s solution not only addressed the immediate productivity challenges but also empowered Saksoft’s teams to work smarter, ensuring faster, more consistent, and reliable software development at scale.

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