AI-Powered Meeting Minutes Automation

Manual meeting minutes consume 15–25% of participants' time. Companies lose up to 15% of their budget on ineffective meetings and manual note-taking. In a typical organization with 50 meetings per week, manual minutes take up to 20 person-hours, and up to 30% of tasks are lost or duplicated. Critical decisions get buried, deadlines slip, and accountability blurs. We solve this with a pipeline: audio recording → diarization → transcription → NLP extraction → structured minutes. It works in real time or post‑factum, integrates with Zoom, Google Meet, MS Teams, Slack, Jira, and Notion. Our solution achieves 2–3× better task extraction accuracy than off‑the‑shelf services thanks to custom NLP.

Problems Teams Face

• No single source of truth. Participants remember meetings differently. Decisions get re‑debated, tasks duplicated.
• Transcription without diarization. Simple services like Otter.ai don't separate speaker turns — the transcript is unreadable.
• Integration with task trackers. Even with a transcript, tasks must be entered into Jira/Notion by hand.

We close all three gaps. Our pipeline uses Whisper large‑v3 (Russian WER ~4.5% on clean recordings), pyannote.audio 3.1 for diarization (DER ~8% on multichannel conferences), and GPT‑4o for structure extraction. In sensitive scenarios we can deploy a local LLaMA 3 70B — confidential data never leaves the perimeter.

How We Customise AI Minutes for Your Infrastructure

A typical project takes 4–6 weeks and includes:

1. Audit current meetings – collect sample recordings, identify patterns (stand‑ups, code reviews, one‑on‑ones).
2. Choose the model – for short meetings (<1h) Whisper + GPT‑4o is enough; for long ones (3h+) we use VAD‑based chunking and parallel processing.
3. Set up integrations – via Zoom Recording API, Google Workspace Events, Microsoft Graph. Output is a webhook that triggers the pipeline.
4. Define the minutes format – Markdown for Confluence, custom templates for Notion, automatic task creation in Jira with deadlines from the transcript.

Real‑World Case

A fintech company with 200+ employees, weekly all‑hands for 150 people. Manual minutes took 8 person‑hours per week. We deployed the pipeline on their Kubernetes cluster with GPU T4. Results:

• Processing time for 1‑hour recording: 12 minutes (including diarization and NLP).
• Name recognition accuracy: 97% after fine‑tuning Whisper on corporate terms.
• Savings: 7 hours per week on minutes preparation alone.

Why Off‑the‑Shelf Solutions Fall Short

For startups with 5–10 meetings per week, off‑the‑shelf works. But for enterprises with confidential data, specialized terminology, and compliance requirements, our solution gives control and accuracy.

What's Included in the Delivery

• Transcription + diarization pipeline – Python code with CUDA support, unit‑tested.
• NLP module for extracting decisions and tasks – prompts tested on 500+ transcripts.
• Integrations – input: Zoom/Teams/Google Meet; output: Notion/Confluence/Jira/Slack.
• Documentation – README, architecture diagram, operations manual.
• Team training – 2‑hour workshop.
• 3‑month warranty – bug fixes, adaptation to API updates.

Get a free project assessment. Contact us — we'll show you how to cut minutes time by 5–10×. Consult with our AI engineer.

Implementation Details

Transcription with Diarization

import whisper from pyannote.audio import Pipeline import torch class MeetingTranscriber: def __init__(self): self.whisper = whisper.load_model("large-v3", device="cuda") self.diarizer = Pipeline.from_pretrained( "pyannote/speaker-diarization-3.1", use_auth_token="HF_TOKEN" ) def transcribe_with_speakers(self, audio_path: str) -> list[dict]: diarization = self.diarizer(audio_path) segments_by_speaker = [ {"speaker": turn.speaker, "start": turn.start, "end": turn.end} for turn, _, _ in diarization.itertracks(yield_label=True) ] result = self.whisper.transcribe(audio_path, language="ru", word_timestamps=True) transcript = [] for seg in result["segments"]: speaker = self._find_speaker(seg["start"], segments_by_speaker) transcript.append({ "speaker": speaker, "start": seg["start"], "end": seg["end"], "text": seg["text"].strip() }) return transcript def _find_speaker(self, timestamp: float, diar_segments: list) -> str: for s in diar_segments: if s["start"] <= timestamp <= s["end"]: return s["speaker"] return "UNKNOWN" 

NLP Processing and Structure Extraction

from openai import AsyncOpenAI import json client = AsyncOpenAI() async def extract_meeting_structure(transcript: list[dict]) -> dict: formatted = "\n".join([ f"[{seg['speaker']} | {int(seg['start']//60):02d}:{int(seg['start']%60):02d}] {seg['text']}" for seg in transcript ]) response = await client.chat.completions.create( model="gpt-4o", messages=[{ "role": "system", "content": """Ты — ассистент для протоколирования встреч. Проанализируй транскрипт и верни JSON: { "summary": "краткое резюме 2-3 предложения", "participants": ["SPEAKER_00 = Иван Петров", ...], "agenda_items": [{"topic": "...", "discussion": "..."}], "decisions": [{"decision": "...", "context": "..."}], "action_items": [{"task": "...", "owner": "...", "deadline": "..."}], "next_meeting": "дата/условие следующей встречи если обсуждалась" }""" }, { "role": "user", "content": f"Транскрипт встречи:\n\n{formatted[:8000]}" }], response_format={"type": "json_object"} ) return json.loads(response.choices[0].message.content) 

Minutes Formatting and Export

def format_meeting_minutes(structure: dict, transcript: list[dict]) -> str: date = datetime.now().strftime("%d.%m.%Y") duration_min = int(transcript[-1]["end"] / 60) if transcript else 0 md = f"""## Протокол встречи от {date} **Продолжительность:** {duration_min} минут **Участники:** {", ".join(structure.get("participants", []))} ### Краткое резюме {structure.get("summary", "")} ### Принятые решения """ for d in structure.get("decisions", []): md += f"- **{d['decision']}**\n _{d.get('context', '')}_\n\n" md += "### Задачи\n\n" md += "| Задача | Ответственный | Срок |\n|--------|--------------|------|\n" for item in structure.get("action_items", []): md += f"| {item['task']} | {item.get('owner', '—')} | {item.get('deadline', '—')} |\n" return md class MinutesExporter: async def to_notion(self, minutes: str, database_id: str): ... async def to_confluence(self, minutes: str, space_key: str): ... async def to_jira_tasks(self, action_items: list, project_key: str): ... async def to_slack(self, summary: str, channel_id: str): ... async def to_email(self, minutes: str, recipients: list[str]): ... 

Webhook Integration (Zoom Example)

@app.post("/webhook/zoom/recording") async def zoom_recording_webhook(payload: dict): if payload["event"] == "recording.completed": recording_url = payload["payload"]["object"]["recording_files"][0]["download_url"] meeting_id = payload["payload"]["object"]["uuid"] asyncio.create_task(process_meeting_recording(meeting_id, recording_url)) return {"status": "ok"} 

Timeline

Basic pipeline (transcription + NLP + Markdown) – 1–2 weeks. Full system with Zoom/Teams/Notion/Jira integrations – 4–6 weeks. Exact timeline depends on the number of recording sources and customisation requirements.

We have been in AI automation for over 5 years, delivering 30+ projects for finance, retail, and IT. We provide a warranty on pipeline functionality.

For a consultation and project assessment, contact us. Request a free audit of your meetings — we'll show you how much time you can save.