Goal Positioning System for Execution
Just keep talking. We'll execute the rest.
Every decision, owner, risk, and dependency — plotted live on the GPS, the moment it's spoken.
The problem
The most expensive hour in business is the one nobody writes down.
Decisions get made, owners get implied, risks get mentioned once — then the call ends and the record is a memory. By the next meeting, the room is re-litigating what it already decided.
Decisions evaporate
Verbal commitments leave the room with no owner, no priority, and no trail back to who said what — or when they said it.
Execution drifts off-strategy
Work accumulates that maps to no objective. Ghost work consumes quarters while the OKRs it ignores quietly slip.
Accountability blurs
Without attribution to the source conversation, ownership becomes negotiable after the fact — and follow-through becomes optional.
The platform
One continuous system of record for execution.
From the first spoken word to the executive portfolio view: every item is typed, owned, mapped to the objective it serves — and traceable back to the moment it was said.
In the room, in the open.
A named bot — your name, your tile — joins Google Meet, Zoom, or Microsoft Teams as a visible participant. There is no covert mode: disclosure, participant consent, and data-processing acknowledgement gate every capture, enforced at dispatch.
Also captures without a bot: in-room microphone, typed notes, or transcript upload.
Goal DiggerStructure, as it's spoken.
The model reads the conversation in rolling windows and emits typed, deduplicated items — not summaries. Each one carries an owner, a priority, an effort size, and a confidence score; near-duplicates are merged, not repeated.
Window size, cadence, and confidence threshold are tunable per workspace — no redeploy.
“…then let's lock the usage-based model for Q3 — Kim, can you own the rollout?”
Every item lands on the objective it serves.
Items flow into objective lanes — sized by effort, flagged by priority, linked by dependency. One glance shows where execution concentrates, what's blocked, and which work maps to no objective at all.
Off-OKR drift is measured, not suspected: ghost work is surfaced the moment it appears.
A portfolio, not a transcript.
Across every meeting: owner load, action-item status, execution risks, off-OKR drift, and cost per session. The Overview reads like an operating review — because that's what it is.
Session cost and model usage are metered and visible — the system accounts for itself.
How it works
From spoken word to system of record.
Join
A consent-gated bot joins the call as a named participant — or capture runs from an in-room mic or an uploaded transcript.
Transcribe
Speech becomes a speaker-attributed transcript stream, flowing in while the conversation continues.
Extract
The model reads rolling windows and emits typed items — deduplicated, merged, and confidence-filtered.
Map
Each item lands on the GPS under the objective it serves, with owner, priority, effort, and maturity attached.
Govern
The room corrects anything, live. Every inference is editable; every edit syncs across map, inventory, and overview.
The Goal Positioning System
Watch the board assemble itself.
This is the live surface your leadership sees during the meeting — objectives as lanes, items as cards, dependencies as threads.
Athlete Onboarding & AI Profile Engine
Trust Foundation — Security, Privacy & Compliance
Governance & trust
Built to be trusted in the rooms where it matters.
Capture technology fails on trust before it fails on tech. Goal Digger treats governance as architecture, not policy — enforced in code, visible in the product.
Consent before capture
The bot cannot be dispatched until disclosure, participant consent — all-party where the law requires it — and data-processing acknowledgement are confirmed. The gate runs at dispatch, in code.
Visible, named presence
Goal Digger appears in the call as a participant with your chosen name and tile. Everyone in the meeting can see the meeting is being captured — there is no silent mode to misuse.
Human authority over AI inference
Every inferred objective, owner, mapping, and dependency is correctable by the people in the room — and corrections feed the learning loop, so extraction improves on your vocabulary, not a generic one.
Walled workspaces
Each company operates in an isolated workspace — its own mission, workstreams, objectives, and team. Nothing crosses the wall; isolation is designed for login-enforced auth.
§ Transcripts are processed under the applicable Data Processing Agreement. Capture is blocked for restricted meeting classes.
Architecture
Enterprise-grade plumbing. No theater.
Meeting
Meet · Zoom · Teams
bot or in-room mic
Ingest
webhook stream
speaker-attributed
Extract
LLM · windowed
dedup + merge
Store
durable state
per-workspace
Stream
SSE push
1.5s poll fallback
Board
map · inventory
overview
Provider and model are workspace settings, applied to the next extraction — no redeploy, no lock-in.
Token and dollar metering per session, per model — surfaced in the product, not buried in a billing console.
Window turns, window seconds, and confidence threshold are governed per workspace to keep noise off the board.
Zero-dependency Node runtime behind serverless handlers — a small, auditable surface area.
Capture is pluggable: meeting bot, browser mic, transcript upload, and replay all drive the same ingest path.
If push fails, polling continues. If extraction is unavailable, a rule-based fallback keeps capturing.
The minutes write themselves. The map draws itself. The room stays accountable.
Put your next conversation on the map.
Open the platform, join a live call, or replay a captured meeting — and watch the board assemble itself in real time.