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MeetingToActions

Meeting notes become a verified list of people, actions, workstreams, and deadlines — with explicit handling for ambiguity instead of silent guessing. Built for M&A advisers and deal teams who need faster follow-up and tighter ownership.

What it does

In fast-moving deals and operating reviews, follow-up on agreed actions is still manual, error-prone, and hard to audit. Meeting assistants transcribe, but they do not build a board you can actually run from.

MeetingToActions reads the transcript, extracts the commitments made, resolves owners and deadlines, and flags the ambiguities rather than glossing them — so the action list you leave the room with is one you can work from.

Architecture

The workflow

A closed-loop agent workflow for turning messy notes into a reliable action tracker.

01

Take in meeting notes

Raw notes are split into structured segments the system can reason over.

Segments preserve speaker and timestamp context for later grounding.
02

Extract what matters

AI identifies actions, owners, workstreams, and timing references.

Each extraction keeps a pointer back to the note span that produced it.
03

Pressure-test the output

Separate review passes check whether each item is grounded and specific.

Dedicated critics per field: evidence, ownership, scope, timing.
Evidence-backed
04

Resolve ambiguity

A judge step handles edge cases and only escalates when ambiguity is real.

Most conflicts auto-resolve; a human is paged only when judgement is required.
Human in the loop
05

Assemble a tracker

Actions link into a structured board with ownership, scoping, and timing.

Output is an append-only graph, not a disposable list.
06

Commit only when coherent

A final controller pass checks the full graph before the tracker is published.

Invariant-based validation blocks incomplete or contradictory commits.

The technical bits

Append-only graph stateDataset-specific review passesJudge / escalate ladderInvariant-based commit validation
of AI calls are quality control reliable output by design
Demo
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Deployment

One workflow, three deployment models.

— 01
Hosted demo

Managed by Alex. Easiest way to evaluate the workflow. Suited for lower-sensitivity mandates or initial product evaluation.

— 02
Desktop executable

Runs locally on the user's machine. No hosted infrastructure required. Best for firms with strict confidentiality requirements.

— 03
Dedicated server

A separate hosted instance provisioned for a single client or team. Web access with stronger isolation, clearer access boundaries, and a more controlled rollout.

Note: in all deployment models, task data is sent to the selected AI provider — currently OpenAI. The more private options reduce data exposure by removing additional hosted layers, but they do not eliminate provider exposure unless you run a fully self-hosted model.

Request access

Try the demo.

Leave your details and Alex will get back to you with access. The hosted demo is available immediately for evaluation — or indicate a preferred deployment if you already know what fits your setup.