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A global professional services and consulting firm's Innovation and Product team was running idea evaluation manually, collecting product and business ideas from employees and researching each one by hand across four evaluation pillars before assigning a readiness score. They partnered with Lyzr to automate the process end to end.
Every submitted idea required in-depth manual research across Total Addressable Market (TAM), Scalability, Ecosystem Fit, and Competitive Intensity, with a single readiness score compiled by hand for each submission.
Without a standardized process, the manual approach introduced variability in evaluation quality from one reviewer to the next, and the effort involved slowed down decision-making across the innovation pipeline.
The time required to research and score each idea by hand limited how many ideas the team could realistically evaluate, and left little room for the deeper strategic review the process was meant to support.
Lyzr built an orchestrated, multi-agent research superflow that automates the full idea evaluation lifecycle, from intake through research, scoring, and reporting.
An AI-powered conversational chatbot guides employees step by step to capture the idea name, target customer, problem statement, solution description, USP, product fitment, risks, and assumptions, validating inputs in real time so nothing incomplete or ambiguous reaches the research stage.
Once an idea is submitted, the superflow triggers specialized agents that research TAM, Scalability, Ecosystem Fit, and Competitive Intensity, replacing hours of manual desk research with a single triggered workflow.
A weighted scoring model converts research findings into pillar-level scores and a consolidated Idea Readiness Score, ensuring every idea is measured against the same criteria.
The system compiles a report with an executive summary, pillar-wise insights, market opportunity assessment, risk indicators, and strategic recommendations for every idea, made available through a dashboard for the Innovation Review Team.
Every idea now moves through the same defined pillars and the same weighted scoring model, replacing evaluation quality that previously varied by reviewer.
By automating research and report generation, the process is designed to move from submission to a reviewable, scored report far faster than the prior manual workflow.
Automating research and scoring removes the manual bottleneck that previously capped how many ideas could realistically be reviewed in depth.
The Innovation Review Team now works from a standardized report and readiness score for every idea, supporting a structured go/no-go evaluation and clearer decisions on approval, incubation, or rejection.
Lyzr Agent Platform is SOC2, GDPR, and ISThe solution runs on AWS, with the backend containerized on ECS Fargate inside a VPC spanning public and private subnets, a WAF and load balancer at the edge, and IAM, Secrets Manager, and KMS governing access and credentials. O 27001 compliant, enabling secure deployment across enterprise environments.
Research insights are aggregated through a governed AWS Bedrock AgentCore web gateway, and idea submissions, evaluation outputs, and readiness scores are stored in a centralized, secure database within private subnets.
The system maintains historical versions of every research report and score for auditability and traceability, so the Innovation Review Team can verify how each readiness score was reached.
Every deployment in this case study started with one conversation. Bring your use case, your stack, your constraints. We'll show you the path from here.
