CRM Pipeline Automation

Overview

A SaaS company's sales team was spending more time updating Salesforce than actually selling. Every call needed manual logging, lead scores were based on gut feel, and follow-up tasks fell through the cracks. We built an AI layer on top of their CRM that automates the busywork — logging calls from transcripts, scoring leads based on engagement signals, predicting which deals will close, and triggering the right follow-up action at the right time.

The Challenge

The CRM had years of inconsistent data, custom fields that meant different things to different reps, and workflows that had been patched together over time. The AI needed to work within this messy reality, not require a clean-room environment. It also needed to earn rep trust — if the AI's lead scores didn't match their intuition, they'd ignore it.

Our Approach

We started by analyzing historical deal data to build a predictive model. The AI listens to call recordings (with consent), extracts key information, and auto-populates CRM fields. Lead scoring uses a blend of firmographic data, engagement signals (email opens, website visits, call sentiment), and pattern matching against historically won deals. We rolled it out gradually — showing AI scores alongside rep scores until trust was established.

Key Features

• Automatic call logging from transcripts
• AI-powered lead scoring with explainable factors
• Deal close probability prediction
• Automated follow-up sequence triggering
• Pipeline health alerts and recommendations
• Rep performance coaching insights
• Revenue forecasting with AI accuracy

Results

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