Case Study

AI-Powered CRM Ecosystem

Integrating custom LLM agents into a legacy CRM to automate lead qualification and personalizing customer outreach at scale.

Role
AI Solution Architect
Timeline
4 Months
Industry
SaaS / SalesTech
Focus
Next.js

Problem Breakdown

The sales team was spending 60% of their time on manual lead qualification and generic email follow-ups, leading to slow response times and missed opportunities.

Architecture Decisions

  • /RAG architecture for grounding LLM responses in CRM data
  • /LangChain for agent orchestration and workflow logic
  • /Pinecone vector database for efficient semantic search

Trade-offs

  • ¬Latency trade-offs for more accurate hyper-personalized responses
  • ¬Cost vs accuracy balance when choosing between GPT-4 and smaller models
  • ¬Initial data labeling effort required for reliable RAG performance

Key Outcomes

  • Automated 70% of the initial lead qualification process.
  • Increased lead conversion rate by 40%.
  • Reduced outreach response time from 24 hours to under 15 minutes.
  • Enabled the sales team to focus on high-intent closing calls.
Next.jsPythonOpenAIPineconeLangChain

Have a similar system challenge?

I specialize in solving high-stakes technical problems for founders. Let's build something scalable together.

Book a technical discovery call 

Typically respond within 24 hours