Turns a raw Zoom or Teams transcript into an audited set of decisions, blockers and owned action items, plus a ready-to-send executive email — reclaiming the 20 to 45 minutes a PM spends on this after every meeting.
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Remote teams generate hours of transcript across Zoom, Meet, Teams and Otter. The text is high entropy and low signal: filler, small talk, tangents, speaker crosstalk, and — buried somewhere in it — the 3 decisions that actually matter and the 2 commitments nobody wrote down.
A project manager then spends 20 to 45 minutes after every meeting pulling out action items, working out who actually owns each one, and writing the stakeholder email. Every week, for every meeting.
2 outputs from a single paste:
A visual audit dashboard — quantified blockers, confirmed decisions, explicit action items with assignees and deadlines, and a separate bucket for tentative items that need clarification. That last category is the one I care most about. A transcript contains plenty of “I could probably look at that this week”, and the difference between a commitment and a maybe is exactly what a PM is paid to notice.
Tentative items carry a stated reason for being flagged — “exploratory cost-saving idea; uncommitted pending preliminary pricing” — rather than just a label. Without the reason, the reader has to re-litigate the judgement, which costs more time than it saves.
An executive email draft in clean markdown, editable inline, refinable in natural language (“make it shorter”, “lead with the blocker”), and one click to the clipboard.
It ships as a React SPA and as a standalone Python Streamlit script, so it can live either as a web tool or as an internal data-science utility without maintaining 2 behaviours.
This is a system that drafts a message sent to executives under a human’s name. The failure modes are not “the summary is a bit vague” — they are attributing a commitment to someone who never made one, or drafting something defamatory about a colleague who spoke badly in a meeting.
Controls run client-side before anything reaches a model, and again in the system instructions:
The model is also constrained against revealing its own system instructions.
Users can supply their own Groq API key for a direct client-to-API call, or use a server-managed proxy. Both use strict JSON schema mode. Offering both matters for adoption: a data team will happily bring its own key, and a PM will not.
“Tentative” is a first-class category. The first version sorted everything into decision, blocker or action. Real transcripts do not divide that cleanly, and forcing a maybe into “action item with owner” produces a confident email that is wrong about what someone agreed to.
Guardrails belong where the output goes, not where the input comes from. The anti-harassment and anti-fabrication rules exist because the artefact is an email from a person to their leadership. Same model, different stakes, different controls.
Draft, never send. Everything is editable and nothing is transmitted. The tool removes the blank page, which is the expensive part; the judgement about what leadership should read stays with the human whose name is on it.
Keep looking
An LLM assigns every support ticket a team and an urgency; deterministic rules decide whether it can be routed automatically or must reach a person. Category accuracy 95–98%, urgent recall 13 of 13, and a confidence score rebuilt after the first one turned out to be meaningless.
A generative ad suite that renders the same product across a billboard, a newspaper spread and a social post without the packaging quietly changing between them — solving visual brand drift with image conditioning rather than better prompts.
Turns a PDF, a set of notes or a plain prompt into a teachable slide deck. The interesting part is what happens when every model endpoint is down — it still produces a deck, and tells you it did.