Behind every AI decision is a reason — your most valuable data. Annotation captures the label, eval captures the score; we capture that reason.
Every enterprise has experts correcting AI outputs daily — in Slack, spreadsheets, email. That work never becomes data or an audit trail. It evaporates, or leaks into public AI that trains your competitors. It's the most valuable signal in your business, and you have zero structured capture of it — reasoning commands roughly 100× the price of a label.
The window to own your reasoning is open now: regulation forces it, the data has never been more valuable, and agents are about to generate decisions faster than anyone can oversee them.
Nearly every enterprise runs AI in or near production — and the oversight layer is racing to catch up.
Regulators are asking who reviewed AI decisions and why — and no framework answers it for you. The record is on you.
Expert reasoning commands ~100× the price of a label. The market has priced the why — and almost nobody captures it.
Autonomous agents act at scale with no judgment layer above them — generating decision traces faster than any team can review.
Paste expert corrections into public AI and you train your competitors. The reasoning has to stay yours.
Capture is the point, not an afterthought. The expert scores fast; we ask for the reasoning only where they deviate — so it survives real volume instead of becoming busywork.
The expert scores against your rubric. Reasoning is asked for only on a deviation — a correction, a low score, a disagreement. Agreement is one tap.
The platform turns their written reasoning into a reusable rule — the principle, plus the exception only they know. It distills what they wrote; it never invents the why.
Every record lives on your infrastructure — hosted, your cloud, or fully air-gapped. Model-agnostic. Your reasoning never leaves the building.
Any model reads it back through the MCP reasoning server — so the next decision is made with your experts' judgment, not without it.
No fine-tune, no redeploy. The only difference is whether the model pulled your ledger at inference time. Flip it.
A private-wealth client declares $8.4M from a 2019 company sale, but tax filings show $6.2M. There's one 2020 adverse-media item. How should I handle onboarding?
Four parts, distilled from your expert's own words. The first three a good model can often reconstruct. The fourth is the one it can't — and the reason this is an asset.
The situation the expert was judging — the trigger for the rule.
The action their judgment calls for in that situation.
The generalizable principle behind the call — distilled from what they wrote, never invented.
The caveat only your expert knows — when the rule should not apply. The non-inferable judgment no foundation model can guess.
The largest enterprises already buy labeling and evaluation platforms — and their experts' reasoning still lives in spreadsheets. That's the gap. We sit a layer above those tools: import from them, and keep the why they throw away.
They record what the answer is. We import from them.
They judge the model — right or wrong. We don't judge the model.
The reasoning behind the judgment, exported as training data and a compliance record, on your infrastructure.
A trace is only an asset if it's owned and portable. Your reasoning lives on your infrastructure and stays model-agnostic — you decide exactly what any model, including the frontier ones, is allowed to see. We are the system that captures it; the asset is yours.
SFT · DPO · RLHF — to make your next model better, not just measure your current one.
Who decided, on what basis, against which standard — audit-ready, from the same capture.
Where your experts agree and disagree — about your people and your rubric, not a score on the model.
WhereforeAI is the answer to all three — the system of record for your AI decisions, produced from one capture event.
How do we improve our AI using our own experts, on our own data? Capture the why and export it as training data — SFT · DPO · RLHF. Your next model reasons like your best people.
How do we prove human oversight? Every review is an audit-grade decision record — who, what, why, when — from the same capture.
How do we do this without duplicate work? Captured once, reused everywhere — and pulled by any model via MCP at decision time.
Your experts' reasoning is the one thing a rival can't copy. Start capturing it.