AI-Powered CRM System
An AI-powered CRM for our client that automated 70% of routine sales tasks and drove a 45% increase in lead conversion across 200+ sales teams — using machine learning for lead scoring and OpenAI-powered outreach personalization.
Project Information
Client
Confidential - Cannot Disclose
Category
CRM Development
Location
Austin, USA
The Challenge
the client's sales reps were spending over 20 hours per week on data entry, follow-up scheduling, and manually triaging leads — leaving less than 30% of their time for actual selling. Their off-the-shelf CRM had no intelligence layer: every lead looked identical regardless of buying signals. Leadership could see they were leaving revenue on the table, but couldn't quantify how much. They needed a system that would tell reps who to call, when to call, and what to say — not just store contacts.
Our Solution
We built the backend in Python with Django because the ML model training needed to live close to the data processing layer. The lead scoring model trains on historical win/loss data and behavioral signals — page visits, email opens, response times — and updates scores in real time as new events come in. OpenAI's API powers personalized email draft generation: reps get a suggested first draft based on the lead's industry, role, and engagement history, then edit rather than write from scratch. React with TypeScript handles the frontend, with a natural language query interface so managers can ask "show me deals at risk this quarter" without writing SQL. HubSpot and Salesforce sync bidirectionally via webhooks so teams don't have to choose between tools.
The Results
Deployed across 200+ sales teams. Lead conversion up 45%. 70% of routine tasks automated, saving each rep 20+ hours per week. Lead scoring alone improved sales efficiency by 38% by directing attention to high-probability deals. Revenue grew 52% within 6 months. Sales cycle shortened by 25%. The system processes 1M+ customer interactions monthly.
Technologies Used
Technologies & Tools
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