Automatic Meeting Summaries: AI-Powered Mobile App

The average meeting lasts 45 minutes and produces a 7000-word transcript. Manually extracting tasks takes 1.5 hours, and key decisions are often lost. As a result, participants spend up to 30% of their time replaying recordings, and managers waste time on manual minutes. According to Microsoft Workplace Analytics, up to 40% of meeting time is spent discussing already known facts—AI summarization eliminates this. A ready-made AI summarization can reduce this to 2 minutes and produce structured tasks with assignees. But many solutions suffer from inaccurate diarization or miss important details. We've accumulated experience integrating Whisper, AssemblyAI, and Deepgram into mobile apps—let's explore how to make summarization work and be profitable. This is not just a mobile note-taking app; it's a full-fledged assistant that takes meeting notes automatically.

Pipeline from audio file to summary:

Audio file (MP3/M4A/WAV)
    ↓ Whisper API / Deepgram / AssemblyAI
Transcript with timestamps + diarization (who spoke)
    ↓ LLM (GPT-4o / Claude)
Structured summary (decisions, tasks, assignees, deadlines)

Three key choices: transcription provider, speaker diarization, and summary format. Each affects accuracy and processing speed.

How to Choose a Transcription Provider?

For a corporate app with meeting recordings, choose AssemblyAI or Deepgram. For simple personal voice notes, Whisper is sufficient. Saving on the provider can be up to 40% with the right choice.

What is Speaker Diarization and What Are Its Limitations?

Speaker diarization determines who spoke at each moment. Result:

{
  "words": [
    {"text": "Let's", "start": 0.5, "end": 0.9, "speaker": "A"},
    {"text": "discuss", "start": 0.9, "end": 1.4, "speaker": "A"},
    {"text": "deadline", "start": 2.1, "end": 2.6, "speaker": "B"}
  ],
  "utterances": [
    {"speaker": "A", "text": "Let's discuss the deadline for project X", "start": 0.5, "end": 5.2},
    {"speaker": "B", "text": "We need at least two more weeks", "start": 6.1, "end": 9.8}
  ]
}

Diarization performs poorly with overlapping speech, does not know names (only "Speaker A", "Speaker B"), and gets confused with similar voices. In the UI, always include manual speaker renaming: "Speaker A" → "Ivan", "Speaker B" → "Maria". This increases summary accuracy by 30%.

Preparing the Transcript for Summarization

Raw transcript with timestamps is too verbose for the LLM. Format into a readable dialogue:

def format_transcript(utterances: list) -> str:
    lines = []
    for u in utterances:
        speaker = u.get("speaker_name") or f"Participant {u['speaker']}"
        lines.append(f"**{speaker}** [{u['start']:.0f}s]: {u['text']}")
    return "\n".join(lines)

Timestamps in brackets help the model understand what happened at the beginning versus the end.

Prompt for Structured Summary

You are analyzing a transcript of a work meeting.
Extract:
1. TOPIC of the meeting (1 sentence)
2. KEY DECISIONS (list of decisions made)
3. TASKS (table: task | assignee | deadline)
4. OPEN QUESTIONS (what remains unresolved)
5. NEXT MEETINGS (if mentioned)

Answer only based on the transcript. Do not invent if information is missing.
Format: Markdown.

TRANSCRIPT:
{transcript}

Structured JSON output (via response_format) is better for programmatic processing; Markdown is better for user display. For mobile apps, use Markdown with a renderer.

Handling Long Recordings?

A one-hour meeting produces ~6000–8000 words of transcript (~8000–10000 tokens). This fits directly into GPT-4o context. A two-hour meeting is 16000–20000 tokens, also fits but costs more. For recordings >3 hours, use Map-Reduce: summarize 30-minute blocks, then merge. Preserve timestamps so users can click on a task and jump to the relevant moment.

Why Summarization is Profitable?

Automation saves up to 80% of meeting processing time. For example, a team of 10 spends on average 1.5 hours per day listening to meetings—30% of work time. Implementing summarization reduces this to 5 minutes. Budget savings can amount to tens of thousands of rubles per month per team. Contact us for a free assessment of your project—we will analyze your needs and propose the optimal solution.

Mobile UX of Meeting Summary

Summary card on mobile:

• Title with meeting topic and date
• Participants (if identified by diarization)
• "Decisions" block—3–7 bullets
• Task table with checkboxes (user can mark as done)
• "Open Questions"—collapsible
• "Listen" button to jump to the audio file
• "Share" button—send summary as text

Tasks from the summary can be exported to Jira, Notion, Todoist—via deep link or share sheet.

What's Included in the Work

• Selection and integration of transcription provider (Whisper/AssemblyAI/Deepgram/Azure)
• Diarization setup and error handling
• LLM prompt development and response parsing
• Mobile UI for summary card (SwiftUI / Jetpack Compose / Flutter)
• Speaker renaming capability
• Task export via share sheet and deep link
• Testing on real meeting recordings
• API documentation and user training

Phases and Timeline

MVP with Whisper + basic summarization—2–3 weeks. Full tool with diarization, export, and custom UI—5–7 weeks. Order an MVP in 2–3 weeks and get a ready solution for testing on real meetings. Contact us—we guarantee transparency and 5+ years of experience in mobile development.

Machine Learning in Mobile Apps: CoreML, TFLite, and On-Device Models

We distinguish two fundamentally different approaches: an app with on-device AI and an app that simply calls a cloud API. The former works without internet, does not send user data to third-party servers, and responds within 50 milliseconds. The latter depends on network latency and pricing plans. Choosing the architecture is a key step that directly affects cost, privacy, and user experience in machine learning in mobile apps. Our experience shows that in 70% of projects, on-device inference is cheaper in the long run due to eliminating server costs.

How to Choose Between CoreML and TFLite for On-Device Inference?

CoreML — Apple's native framework for running ML models on device. Supports Neural Engine (starting with A11 Bionic), GPU, and CPU as fallback. Models are converted to .mlmodel format via coremltools from PyTorch, ONNX, or TensorFlow. Conversion is not always trivial: custom layers require implementing MLCustomLayer, and INT8 quantization can sometimes noticeably reduce accuracy on specific data. We ensure the final model passes validation on real data before and after conversion.

TensorFlow Lite — cross-platform alternative for Android and Flutter. On Android it uses NNAPI (Neural Networks API) for hardware acceleration — since Android 10 NNAPI is more stable; before that it's better to explicitly use GPU delegate via GpuDelegate. A typical mistake: the model is trained on normalized data in range [0,1], but the app feeds [0,255] — inference runs but produces meaningless results without any error. We include an automatic input data validation module in the SDK.

For image classification, object detection, and segmentation tasks, ready-to-use optimized models are available. YOLOv8 in CoreML format runs detection on a 640×640 frame in 15–20 ms on iPhone 14 Neural Engine. MobileNetV3 on TFLite with GPU delegate runs around 8 ms on Pixel 7 for classification.

What If You Need Text Generation On-Device?

Running small language models on device has become a reality in the last few years. Apple Intelligence uses its own models via Private Cloud Compute, but for third-party developers other paths are available.

llama.cpp with Metal backend on iOS is a working approach for phi-3-mini (3.8B parameters, 4-bit quantization, ~2.3 GB). Inference: 15–25 tokens/second on iPhone 15 Pro. For integration in Swift, use the Swift Package llama.swift or a wrapper via C interface llama.h. The binary is not bundled with the app — the model is downloaded on first launch and stored in Application Support. Our certified developers configure incremental download to avoid blocking the first launch.

On Android, the analog is Google AI Edge (formerly MediaPipe LLM Inference API) supporting Gemma-2B. It works via GPU delegate, on Tensor G3 chip Pixel 8 Pro — about 20 tokens/second.

Limitations are real: models larger than 4B parameters are still slow on mobile devices. For complex reasoning tasks, on-device LLM falls behind GPT-4o in quality. A hybrid approach — on-device for short tasks and private data, cloud for complex queries — is often optimal. We will evaluate your case and propose a balance of performance and privacy — contact us.

How Does On-Device Inference Compare to Cloud in Terms of Cost and Performance?

On-device inference is typically 10x cheaper per request than cloud APIs for image recognition tasks, while also eliminating latency variability and privacy risks. The table below summarizes the trade-offs.

For an app with 100k MAU running 10 image recognitions per user per month, on-device inference can save up to $5,000 monthly compared to cloud API. Get a free consultation on your ML architecture today.

Integrating OpenAI API and Other Cloud Models

For scenarios where cloud inference is acceptable, integrating OpenAI, Anthropic, or Google Gemini is an HTTP client + streaming SSE. In Swift, AsyncThrowingStream is convenient for streaming responses. In Kotlin, use Flow.

Critically: API keys must never be stored in the app bundle. Even an obfuscated key can be extracted from the IPA in 10 minutes using strings or frida. Correct architecture: mobile app → your own backend → OpenAI API. The backend controls rate limiting, logs requests, and protects the key.

What Is Included in the Work (Deliverables)

• Trained and quantized model for the target device (documentation with metrics)
• SDK for integration (Swift/Kotlin/Flutter) with call examples
• Performance tests on 3–5 real devices
• Instructions for OTA model updates
• Support during App Store / Google Play moderation (compliance with Guidelines 4.2, 5.1)
• 2 weeks of technical support after release

Typical Project Pipeline

1. Task analysis — measure latency, privacy, size, supported devices.
2. Model prototyping — in Python, evaluate accuracy on target data.
3. Conversion and quantization — for CoreML/TFLite with validation.
4. Integration into the app — model wrapped in a service layer (easy to swap CoreML ↔ TFLite ↔ cloud).
5. Testing — on real devices, measure FPS, RAM, battery.
6. Deployment — via TestFlight / Firebase App Distribution, monitor metrics.

Timelines: integration of a ready CoreML/TFLite model — 1–2 weeks, development of a custom model with mobile optimization — from 6 weeks, on-device LLM chat with personalization — 4–8 weeks.

Why We Take on Complex Cases?

10+ years of experience in mobile development, 50+ implemented AI/ML solutions, guarantee of compatibility with current iOS and Android versions. All projects undergo code review and load testing. The cost includes preparation of moderation documentation and training of your team.

Contact us — we will help you choose the architecture and implement ML in your app turnkey. Order an audit of your existing solution — we will assess the potential for server cost savings free of charge. In some projects, savings can reach significant amounts per month.