Sales representatives in small and medium businesses (SMBs) spend 10–20 minutes after every client call summarizing discussions, drafting follow-up emails, and updating CRMs manually.
AI Sales Copilot automates this process using generative AI — it listens, understands, and writes like a real sales assistant.
Problem
Post-call administrative tasks are repetitive, time-consuming, and error-prone.
Sales reps lose focus and valuable time that could be spent closing deals.
Solution
AI Sales Copilot automatically:
- Transcribes sales calls (via Whisper)
- Summarizes discussions and detects sentiment
- Generates a professional follow-up email in the rep’s tone
- Logs key action items and next steps in a structured CRM format
Impact
- Saves ~10 minutes per call
- Ensures consistent, high-quality communication
- Keeps CRM data accurate and up-to-date
✅ Upload audio (.wav) or text transcript
✅ Automatic call transcription (Whisper)
✅ Smart summarization & sentiment analysis (LangChain + LLM)
✅ AI-generated follow-up email (tone-matched)
✅ Extracted “next steps” and action items
✅ Save/export results to a local SQLite CRM
✅ Streamlit dashboard for interactive use
| Metric | Target |
|---|---|
| ⏱ Time saved per call | ≥ 10 min |
| ✅ JSON valid outputs | ≥ 95 % |
| 📨 Email quality rating (1-5) | ≥ 4.0 |
| ⚡ Latency (audio → email) | ≤ 2 min |
| 🎯 Data accuracy (facts retained) | ≥ 90 % |
Role: Sales Account Executive (SMB)
Needs: Faster follow-ups, less admin time, consistent emails
User Stories
- Upload a sales call and receive a concise summary in < 30 s.
- Automatically generate a ready-to-send follow-up email.
- Extract next steps and deadlines for CRM logging.
- Download or export results as JSON or Markdown.
- LLM & Orchestration: LangChain · IBM watsonx / OpenAI GPT
- Speech-to-Text: Whisper
- UI: Streamlit
- Data Storage: SQLite · pandas · Chroma (optional memory)
- Validation: Pydantic · JSON Schema
- Environment: Python 3.10+