Local AI Chat

Q: Do I have to use one fixed AI model?

No. Meeting Note supports multiple local model options, from lightweight to supermodel tiers.

Q: Will this work on lower-end phones?

Yes—lighter models are available specifically for broader compatibility and faster response.

Q: What if a supermodel is too heavy for my device?

The app includes safety checks and fallback behavior to avoid unstable loads.

Meeting Note gives your team a smarter way to work with meeting history: on-device AI when you want local speed and privacy, plus custom provider mode when you want cloud-scale power.

One summary workflow for fast-moving teams

In high-frequency collaboration, the bottleneck is rarely access — it’s interpretation.

Teams repeatedly ask:

What did we decide last week?
What changed between meetings?
Who owns this follow-up?
What is still unresolved?

Without an intelligent layer, people scan long records by hand and decision latency grows.
Local AI Chat turns meeting context into a query-able knowledge layer, so teams move from “searching” to “acting” faster.

Model lineup (choose what fits your device)

Meeting Note does not force a one-size-fits-all model. It gives you a practical range:

SmolLM2 (135M) — ultra-light, fast, broad device compatibility
Qwen 2.5 (0.5B) — balanced speed + quality
Qwen 2.5 (1.5B) — stronger reasoning with larger footprint
Phi-3.5-mini (3.8B) — deeper logic for advanced use cases
Gemma-2-(9B) — richer synthesis and analysis
Mistral NeMo (12.2B) — high-capacity multilingual / long-context tasks

So “Summarize this meeting” can run efficiently on lightweight phones, or scale up on high-memory devices.

Custom API Key support

Meeting Note now supports custom AI providers in addition to local models.

In AI Settings, users can choose a meeting chat provider:

Local (on-device)
OpenAI-compatible
Anthropic
Gemini
Ollama

For supported cloud providers, users can add a Base URL, choose a Model ID, and save their API key securely on-device.
Model lists can be refreshed directly from the provider, and responses can be streamed live as tokens arrive.

If a cloud/custom provider fails, Meeting Note can automatically fall back to a local model (when enabled), so workflows keep moving.

How Meeting Note adapts across device classes

Meeting Note’s local AI path is designed for real-world hardware diversity:

Downloads model files to device storage (GGUF flow)
Uses native acceleration preferences where available (Vulkan on Android, Metal on iOS)
Applies model-aware context and inference settings
Falls back to compatible runtime/model behavior when needed
Uses safety checks to avoid unstable heavy-model loads on constrained devices

In short: lighter models for reach, heavier models for depth — without making users guess what will work.

Built for summaries, recaps, and Minutes composition

Inside Meeting AI Chat, users can trigger quick workflows like:

Summarize
TO-DO List
Budget Overview

Local AI Chat Responses are grounded in:

Current meeting transcript content
Meeting metadata (title, date, status, owner, length, team, project)
Focused context selection and transcript compression for long sessions

That grounding makes outputs practical for MoM workflows: cleaner drafts, clearer ownership, and faster preparation.

What teams get

Faster context retrieval for decisions

Ask directly, get relevant answers quickly, and move from context lookup to execution.
Better continuity in high-frequency collaboration

Reconnect to prior discussions without repetitive re-alignment loops.
More useful outputs for busy stakeholders

Leaders and contributors can absorb key outcomes, risks, and next steps with less cognitive load.
Stronger follow-up quality

Clearer decision trails improve accountability and reduce ambiguity.
Lower friction between recorded and usable

Meeting data becomes an active planning asset, not a passive archive.
Better support for iterative planning

Track evolving decisions, compare reasoning, and carry forward unresolved issues with confidence.