Our assistant takes the tedious first pass on the work people review and sign off — grounded in your own data. It proposes; your team approves. No autonomous-agent claims, no decisions made without a human in the loop.
Six places the assistant removes busywork — each one a draft a person reviews before it counts.
Draft answers to DDQs and RFPs from your policies, evidence, and past responses — your reviewer edits and approves before anything is sent to a prospect.
Turn a failing automated test into a plain-language explanation of what broke, why it matters for the control, and a concrete remediation step.
Suggest which controls and requirements a policy or piece of evidence satisfies, so mapping is a review step instead of manual data entry.
Extract the auditor's opinion, scope, and exceptions from a vendor's SOC 2 report to speed up third-party assessments.
Summarize posture, open findings, and what changed for leadership — without rebuilding a deck from scratch each quarter.
Propose the evidence you need to collect for a control, ready to assign to an owner with a due date.
The same four steps run behind every AI feature. It is the difference between an assistant you can trust in a compliance program and a black box.
The assistant pulls the relevant slice of your own graph — policies, controls, evidence, past answers — so the draft is grounded in your reality, not the open web.
It produces a draft: an answer, a mapping, a remediation, a summary. Every draft is labelled as a proposal and shows what it was grounded in.
A person on your team reads, edits, accepts, or rejects it. Nothing skips this step — the human is the decision-maker, the AI is the drafter.
Only after approval does the result become evidence, leave your workspace, or update a record. Actions are gated; there is no silent auto-apply.
Our AI assists the work humans review and sign off. It proposes; your team approves. No autonomous claims, no black boxes.
Yes. Data is encrypted in transit and at rest. The draft cites your encryption policy and the passing control that proves it — ready for your reviewer to approve.
Every suggestion is reviewed and approved by a person before it leaves your workspace.
A generic chatbot will confidently invent a control number. Ours retrieves from your own graph and shows its work, so a reviewer can verify a draft in seconds instead of distrusting all of them.
“Data at rest is encrypted using platform-managed keys across production stores.”
Grounded in: Encryption Policy v3
Backed by: test “Encryption at rest” — passing
Satisfies: CC6.1, ISO A.8.24
Every suggestion is a draft a person reviews. We make no autonomous-agent claims and ship no action that mutates your systems without an explicit human gate.
The assistant — and the MCP server it powers — enforces the same org-scoping as the rest of the platform. One tenant's AI can never read another's data.
We describe capability and direction, not imaginary production agents or integrations that don't exist yet. If a feature is in progress, we say so.
No. The assistant drafts; your team decides. Every output — a questionnaire answer, a control mapping, a remediation, a summary — is a proposal a person reviews, edits, and approves before it is used. There is no mode where it silently changes your records or sends something on your behalf.
From your own data: the policies, controls, requirements, evidence, and past answers in your tenant. Retrieval happens over your graph, so drafts are grounded in what is actually true for your program, and each draft can cite the source it drew from.
It tells you. Rather than fabricating a plausible-sounding answer, the assistant flags that the supporting evidence is missing — which is itself useful, because it points to a gap worth closing.
We deliberately avoid that framing. Some competitors market self-driving agents; we ship propose-then-approve assistance. The AI can draft and surface, but applying anything to your systems is gated behind a human. This is a design choice, not a limitation we're hiding.
Yes — that's what the MCP server is for. Connect Claude, Cursor, or any MCP client to your tenant's compliance graph and work against it from your own editor, with the same tenant-scoping and review discipline.
Your compliance data is used to ground answers for your tenant, not to train a model shared with other customers. We treat your evidence as yours.
Connect Claude, Cursor, and other MCP clients to your tenant's compliance graph — securely, scoped to your org.