Lead Rescue turns high volumes of inbound and outbound calls into structured, actionable data. It combines automatic speech recognition, NLP, and GPT-based analysis so businesses can understand customer intent, measure lead quality, and improve sales performance — without manually listening to every call.
The system processes 10–50 calls in minutes, transcribing conversations and scoring them so teams can focus on the highest-value prospects and respond in real time.
The problem
Teams recorded high volumes of calls but couldn't review them at scale, losing insight on lead quality, sentiment, and follow-ups — and missing high-value prospects.
Our approach
- Built an Automatic Speech Recognition pipeline that transcribes recorded calls into searchable, analyzable text.
- Layered GPT and NLP analysis to detect intent, objections, and sentiment, then compute lead-heat scores to prioritize prospects.
- Added real-time alerts and a scalable Node.js + WebRTC backend to handle growing call volume.
The solution
An AI call-intelligence system that converts raw recordings into transcripts, insights, and lead scores — helping sales teams focus on the highest-probability leads and respond to opportunities in real time.
What we built
Automatic transcription
ASR converts recorded calls into accurate, searchable text transcripts.
Conversation intelligence
GPT and NLP detect interest, objections, sentiment, and intent.
Lead heat scoring
Scores prioritize prospects with the highest probability of conversion.
Real-time alerts
Notifies teams of hot opportunities and potential problems as they surface.
Scalable backend
Node.js and WebRTC handle growing call volume.
Structured insights
Summaries, action items, and scores stored for search and review.