Home / Trust
The part most vendors won't show youA system that claims 100% accuracy is hiding something. This is Plant Cortex's actual, current performance on its core promise — grounded, cited, and honest when it doesn't know — from a real live deployment.
The remaining ~16% weren't guessed at — they were logged as genuine coverage gaps (a question the current manuals simply don't cover) and surfaced for review, the same discipline this app applies to every AI-assisted feature it ships. Each answer works by retrieval, not training: the relevant passages from your own manuals are looked up fresh and handed to the model alongside your question every time. No plant data, question, or manual is ever used to train or fine-tune the underlying model — every call is a stateless, one-shot request against your own documents, on infrastructure you or we control.
Receipts, not just claims: this deployment now spans 7 tracked operational registers plus a command-center dashboard, 1,270 engineering drawings and 35 manuals mapped to a real equipment hierarchy, and 248 automated tests that have to pass before any change goes live — the same bar every session on this build has been held to.
Most "AI for industry" today is one of two things: a general-purpose AI chat wrapper that sounds confident and cites nothing, or a heavy sensor/APM platform built around a data-science rollout.
The easiest thing for any vendor to do is claim near-perfect accuracy and let a prospect find out otherwise after signing. We'd rather show the real number — including the part where it doesn't work yet — because a system that can say "I don't know" is the only kind that's safe to actually rely on at 2am with a machine down.
A citation printed in an answer is verified server-side against a real retrieved passage — never taken on the model's word.
Every unanswerable question becomes a coverage-gap record for review — the same one this report's honest percentage comes from.
Every model call is a stateless, one-shot request: your manuals are retrieved fresh and passed in as context, never used to adjust the model's weights.
Downtime cost, ROI, savings — if a real rate wasn't entered by a real person, the number is left blank, never estimated to look more impressive.
Answer rendering is sanitized against injected content, accounts lock out after repeated failed logins, and every session's changes are documented — including the bugs found and fixed, not just the wins.
See a real trust report built from your own plant's usage.
Start a design-partner pilot →