Features
The AI is in the work, not only in the chat window.
It runs the drafting, not the deciding
The same models draft your threat model, fill your submission documents, triage your SBOM, and read FDA guidance and hold letters. What they never do is close a question. Every one of those surfaces stops for a person.
It reads your documents, not just the internet
Answers are retrieved from your own uploaded evidence alongside FDA guidance, real deficiency letters, and current vulnerability feeds. The question you ask is answered against your device, in your documents, with the passage it came from attached.
There is no third 'trust me' category
A statement is either backed by a verbatim quote we can find in a real document, or labeled a model inference with its reasoning recorded. Nothing reaches you as fact on the model's confidence alone. In a submission, a confident fabrication is worse than no answer.
How the grounding works
Three steps, and the model does not get the last word in any of them.
The worry about AI in a regulated submission is not that it will be unhelpful. It is that it will be confidently wrong in a document someone signs. Here is the machinery that makes that detectable rather than a matter of trust.
The question goes to your evidence.
You ask the question you would have asked a consultant. The platform pulls the passages that bear on your device from your own documents, the FDA corpus, and current vulnerability data. There is no query syntax, and no result list to sift.
Regulatory Intelligence
What are the most common cybersecurity deficiencies in 510(k) submissions?Suggested questions
The quote is verified against the document, by string matching.
When the model quotes a source, the platform checks those words against the document itself, character by character. The check is deterministic, so you or an auditor can re-run it and get the same verdict. A quote that cannot be found is flagged as unverified, not shown as evidence.
Search results
Summary
Applicable requirements
Evidence from regulatory documentsNothing becomes fact because the model said it.
The run stops for review, and what your team approves is recorded as your decision, not the model's. Once you have approved something your judgment stands. A later run proposes changes rather than overwriting them. If the underlying document changes, the claim is re-checked against the new text rather than left to go stale.
Threat model run
Assets extractedThreats identifiedRisks scoredControls mappedAwaiting reviewTraceability reportRequest changesApprove stage
What you get
What you can hand a reviewer.
A claim-by-claim evidence trail
- Every claim listed with its evidence and its verdict: verified against a real quote, unverified, or an honest inference. The three stay visually distinct, so a weak claim can never hide among the solid ones.
A record of what the agent actually did
- For any multi-step task, the exact steps the agent took and what each one returned. You are auditing the work rather than reading a summary of it.
Provenance on every fact
- Every fact the platform holds about your device carries its origin: set by a person, traced to a document, inferred, unverified, or not established yet. Gaps are stated rather than filled in.
Findings that show what is missing
- Audit findings carry their evidence inline, including the evidence the auditor expected to find and did not. An absent artifact is surfaced as a finding in its own right rather than passed over in silence.
Your documents stay yours
- Your data never trains our AI. Documents are encrypted and scoped to your workspace, and the models that read them run inside our own cloud account, not a public chat service.