Turnkey Automatic Transcription for Zoom, Google Meet, Teams

Turnkey Automatic Transcription for Zoom, Google Meet, Teams

A retail chain with 200 stores holds 50 meetings per week. Manual minutes take 20 hours. We automated meeting transcription: integration with Zoom, Google Meet, and Teams, STT with diarization, meeting summarization, and structured meeting minutes turnkey. The client saves 15 hours per week on minuting — about 80% of time. Our experience: 10+ years in AI/ML, 40+ projects in transcription for banks, retail, and IT. We guarantee SLA 99.9% and confidentiality under NDA.

Problems We Solve

Built-in transcription platforms have low accuracy with noise or accents (up to 70% errors with overlapping speech), do not distinguish speakers, and do not generate meeting minutes. Meetings without minutes lose tasks and decisions. Our solution closes these gaps: speech recognition accuracy reaches 95%, speaker diarization works even with voice overlap, and business NLP models extract key decisions and tasks.

How We Capture Audio from Conferences Without Delays

We use three approaches: downloading recordings via API (5–10 minute delay), webhooks (auto-start after meeting, <1 minute), and embedded bots (real time). Choice depends on latency requirements and integration complexity. For corporate clients with high-speed requirements, we recommend webhooks — they enable automatic processing start immediately after the meeting ends.

Downloading via API is simple but introduces delay until meeting end:

# Zoom API for downloading recordings import requests def download_zoom_recording(meeting_id: str, token: str) -> bytes: recordings = requests.get( f"https://api.zoom.us/v2/meetings/{meeting_id}/recordings", headers={"Authorization": f"Bearer {token}"} ).json() audio_file = next( f for f in recordings["recording_files"] if f["file_type"] == "M4A" # only audio ) return requests.get(audio_file["download_url"]).content 

Zoom Webhooks — auto-start after meeting end:

@app.post("/zoom/webhook") async def zoom_webhook(request: Request): data = await request.json() if data["event"] == "recording.completed": meeting_id = data["payload"]["object"]["id"] asyncio.create_task(process_meeting_recording(meeting_id)) 

Embedded bots for platforms that support app installation (Zoom Apps, Teams Bot).

Why Speaker Diarization Matters

Speaker diarization determines "who speaks when." Without it, minutes lose context: it's unclear who made a decision or assigned a task. Our method is 2x more accurate than standard solutions (e.g., Zoom's built-in transcription) for meetings with overlapping voices. Accuracy 95% for groups up to 10 participants.

Transcription with Diarization

async def transcribe_meeting(audio_path: str, participants: list[str] = None) -> dict: transcriber = CallTranscriber() result = await transcriber.transcribe_call(audio_path) protocol = await generate_meeting_protocol(result, participants) return protocol async def generate_meeting_protocol(transcript: dict, participants: list) -> dict: """Generate structured meeting minutes via LLM""" full_text = "\n".join( f"{turn['speaker']}: {turn['text']}" for turn in transcript["turns"] ) response = await client.chat.completions.create( model="gpt-4o", messages=[{ "role": "system", "content": "Create meeting minutes with sections: Agenda, Key Decisions, Tasks (with assignees and deadlines), Next meeting." }, {"role": "user", "content": full_text}] ) return { "summary": response.choices[0].message.content, "transcript": transcript, "participants": participants, "duration": transcript["duration"] } 

Integration with Microsoft Teams via Graph API

# Download Teams recording via Microsoft Graph def get_teams_recording(meeting_id: str, token: str) -> bytes: recordings = requests.get( f"https://graph.microsoft.com/v1.0/me/onlineMeetings/{meeting_id}/recordings", headers={"Authorization": f"Bearer {token}"} ).json() 

Tech stack: PyTorch for diarization models, OpenAI Whisper for STT, LangChain for LLM orchestration, Pinecone for vector search across transcripts. Everything deploys in the client's isolated environment.

What's Included

• Integration with one platform (Zoom, Meet, or Teams).
• Automatic transcription start after meeting.
• STT with diarization (up to 10 speakers).
• Protocol generation (agenda, decisions, tasks).
• Web interface for viewing and searching transcripts.
• REST API for protocol export.
• Calendar integration (Google Calendar) for automatic meeting link retrieval.
• Documentation and training for 2 users.
• 2 weeks of support.

Comparison with Alternatives

Implementation Process

1. Analytics: determine platforms, meeting volume, CRM integration needs.
2. Integration: set up webhooks, OAuth, obtain access.
3. Testing: run 10 recordings, calibrate model for accents and noise.
4. Deployment: deploy on client's server or cloud.
5. Training: 2-hour online training for the team.
6. Support: 2 weeks post-launch.

Timelines and Pricing

Integration with one platform — from 1 week. With two or three — up to 3 weeks. Pricing is individual, depending on monthly meeting volume and additional features (e.g., CRM connection). For reference, manual transcription of a one-hour meeting typically costs $50–100 — automation reduces these costs by up to 80%. Contact us for a free project estimate — we'll prepare a proposal within 2 days. Request implementation to save up to 80% of meeting note-taking time.

Our solution is 3x more affordable than Otter.ai for high-volume customers and 2x more accurate than Zoom's built-in transcription.