AI Call Transcription and Summarization to CRM

Development of AI Call Transcription and Summarization into CRM

System automatically transcribes the call, creates a brief summary, and attaches it to the customer card in CRM. Operator no longer spends 3–5 minutes manually documenting call results.

Transcription → Summary → CRM Pipeline

async def process_completed_call(call_event: dict):
    call_id = call_event["call_id"]
    recording_url = call_event["recording_url"]
    crm_contact_id = call_event.get("crm_contact_id")

    # 1. Download recording
    audio = await download_recording(recording_url)

    # 2. Transcribe with diarization
    transcript = await transcribe_with_diarization(audio)

    # 3. Generate summary
    summary = await generate_call_summary(transcript)

    # 4. Update CRM
    if crm_contact_id:
        await crm.update_contact(
            contact_id=crm_contact_id,
            data={
                "last_call_summary": summary["short"],
                "last_call_transcript": transcript["full_text"],
                "last_call_outcomes": summary["outcomes"],
                "next_action": summary["next_action"],
                "call_sentiment": summary["sentiment"]
            }
        )

Summary Generation

Uses LLM to create structured summaries:

1. Brief summary (2-3 sentences)
2. Reason for calling
3. Topics discussed
4. Result/solution
5. Next action
6. Customer sentiment

CRM Integration

Supports: Bitrix24, amoCRM, Salesforce, and other popular CRM platforms.

Timeline: pipeline with one CRM — 2–3 weeks. Multi-platform system — 1.5 months.