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Building an AI-Powered Contact Enrichment Platform

#ai #langchain #case-study #saas

When a B2B SaaS company approached me about automating their lead research process, their SDRs were spending 2 hours per lead batch on manual research. The goal was simple: make it faster without sacrificing accuracy.

The Problem

Sales teams waste enormous amounts of time on manual data enrichment — finding verified emails, LinkedIn profiles, company data, and building prospect profiles. It’s repetitive, error-prone, and doesn’t scale.

The Solution

I built a custom AI agent using LangChain and the Claude API that automates the entire enrichment workflow:

  • Automated research across multiple data sources
  • Email verification with 95% accuracy
  • LinkedIn profile matching using fuzzy search and LLM reasoning
  • Company data enrichment including size, industry, tech stack, and funding info

The system processes 10,000+ contacts daily with minimal human oversight.

Tech Stack

  • LangChain for agent orchestration and tool-calling
  • Claude API for reasoning and data extraction
  • Next.js for the dashboard and management UI
  • PostgreSQL for structured data storage and deduplication

Results

  • 80% faster data enrichment speed
  • SDR manual research time dropped from 2 hours to 15 minutes per lead batch
  • Sales team closed 40% more deals in Q3
  • 95% accuracy on verified contact data

The key insight: LLM-powered agents excel at tasks that require reasoning across unstructured data from multiple sources — exactly what sales research demands.