AI Meeting Analysis Platform: From Transcription to Recommendations

Problem: meetings happen, insights get lost.

Your sales department is conducting dozens of calls a day, but:

• No time review each call manually
• Subjective assessment quality of managers' work
• Insights are being lost. - what works, what doesn't
• It's unclear where the gap is between the best and average managers
• Beginners learn slowly - There is no mentorship system based on actual calls.

The same applies to customer development interview, HR interviews, customer support calls.

Solution: Full-cycle meeting analysis AI platform

I am developing corporate systems automatic analysis of meeting recordings using AI. Here's what your business gets:

🎤 Automatic Transcription

Speech-to-Text integration: - Audio upload via web interface or mobile application - Chunk support for long meetings (upload in parts) - Speech recognition via AssemblyAI / Google Speech-to-Text - Speaker identification (speaker diarization) - Support for Russian and English languages

Asynchronous processing: via RabbitMQ with a retry mechanism for reliability.

✅ Analysis using custom checklists

Flexible checklists to fit your methodology: - Creation of checklists with categories and criteria - Automatic meeting evaluation for each item (Yes / Partially / No) - AI comments explaining the evaluation with quotes from the transcript - Overall percentage of checklist compliance

Examples of checklists- Sales: Presentation → Needs Discovery → Handling Objections → Closing - HR: Interview Structure → Technical Questions → Soft Skills → Cultural Fit - Support: Greeting → Problem Identification → Solution → Follow-up

Analysis of the meeting recording with AI recommendations and checklist verification results.

💡 AI recommendations and insights

Smart analysis Based on LLM: - Summary (summary) of the meeting in 2-3 sentences - Recommendations for manager improvement - Identification of gaps in scripts and methodology Sentiment analysis - customer mood at different stages Extraction of key points - objections, purchase triggers, next steps

🎯 Prompt System with Versioning

Full control over AI analysis: - A prompt library for different types of analysis - Prompt versioning (ability to roll back to a previous version) - Testing prompts on historical data - A/B testing of different prompts - Logging of all LLM requests for debugging

Supported LLMs: OpenAI API, Claude, Polza.AI (700+ models)

👥 Multi-tenancy

Flexible structure for any business: - Companies - isolated data for each client Teams within companies (sales, support, HR) Users with roles (admin, user, viewer) - Custom checklists at the company level - Separate integration settings

📱 Mobile Application

Real-time loading audio: - Record calls directly in the app - Upload chunks as you record (no need to wait for the meeting to end) - Automatic synchronization with the server - Notifications when analysis is ready

📊 User Web Portal

User-friendly interface for work: - List of all meetings with filtering and search - Detailed meeting page with tabs: - 📝 Overview (summary, participants, status) - 🎤 Transcript with timestamps and speakers - 💡 AI recommendations - ✅ Checklist results - 🤖 Prompt results - Export of transcripts and analyses - Sharing meetings with colleagues

🔧 Admin Panel

Full control system: - Management of companies, teams, users - Creation and editing of checklists - Prompt library with versioning - Configuration of integrations (Speech-to-Text, LLM, S3) - Processing logs for debugging - LLM request history with copying for analysis

Technical glass

🏗️ Architecture

Technology stack

• Frontend: React + TypeScript + Vite
• Backend: Python FastAPI + SQLAlchemy
• Database: PostgreSQL
• Queue: RabbitMQ (asynchronous processing)
• StorageS3-compatible storage for audio
• Speech-to-Text: AssemblyAI / Google Cloud
• LLM: OpenAI / Claude / Polza.AI
• Deploy: Docker + Nginx + pm2
• Auth: JWT tokens with refresh

Case: B2B SaaS company with a sales department

Client: SaaS company with 25 sales managers TaskIncrease the conversion of demo calls into deals

What have they done: - A platform with automatic transcription of all demo calls - A 12-point call quality checklist (from introduction to closing) - AI-powered recommendations for each manager after calls - A dashboard for team leads with team analytics - A mobile app for call recording

Results for 2 months: - 📈 Demo-to-deal conversion increased from 18% to 27% (+50%) - 🎯 5 critical gaps have been identified. in sales scripts - ⏱️ Time to analyze the call reduced from 30 minutes to 3 minutes (90%) - 🚀 Beginners move up to the intermediate level in 2 weeks instead of 2 months - 💰 ROI paid off in 6 weeks

Development time: 8 weeks from briefing to production

What you get

✅ Web application for users with a beautiful UI ✅ Admin panel to control the entire system ✅ API for mobile applications (iOS/Android) ✅ Asynchronous worker for audio processing Source code with ownership rights ✅ Documentation APIs and Guides ✅ Team training working with the platform

Cost and timelines

• MVP (transcription + basic analysis + web portal): 6 weeks
• Professional (+ checklists + prompts + admin panel): 8 weeks
• Enterprise (+ multi-tenancy + mobile API + white-label): 10-12 weeks

The cost depends on the scope of features and integrations. Let's discuss it on a call.

Start the project

Ready to implement AI meeting analysis in your company?

Next steps: 1. Fill out the brief below with a description of your task 2. Send examples of meeting audio/transcripts (optional) 3. We will schedule a call within 24 hours to clarify the details 4. I will prepare a detailed technical specification and a UI prototype 5. We will start development with weekly demos

Contact information: @sashanoxon

I work according to the technical specifications with a focus on business results. If I see an opportunity to improve the product or add value, I will do it, even if it wasn't in the original spec. Success is measured by real metrics: conversion growth, time savings, profit increase.