Helping teams move from scattered documents and unclear blockers to confident compliance decisions. CompliancePilot AI identifies missing, expired, weak, or insufficient evidence, explains why a control is blocked, drafts the next request, and keeps humans in control before any action goes out.
It answers one question across the whole workflow: what is blocking compliance readiness, and what evidence is needed next?
Compliance teams work with long control lists, scattered documents, unclear evidence quality, deadlines, comments, and review statuses. Even when documents are uploaded, reviewers still need to manually understand whether the evidence actually proves the required control.
The main friction isn’t evidence storage — it’s evidence interpretation. Reviewers spend hours deciding whether an uploaded policy actually demonstrates that MFA is enforced, whether a one-year-old certificate is still acceptable, whether a backup policy without test results closes the control. That repeated interpretation work creates confusion, slow reviews, repeated follow-ups with evidence owners, and weak confidence in compliance readiness.
CompliancePilot supports five roles around a single compliance review. Each one needs different language, depth, and surface area. The MVP focuses on the Compliance Manager.
CompliancePilot is structured around six things AI helps users understand. Each one resolves to a clear next action — the AI never just describes a problem.
Required-but-not-uploaded items rise to the top of the home page, ranked by control priority and audit deadline.
Certificates, reports, and policies carry an expiry date that flips them to action-required before they silently age out.
When a policy is uploaded but doesn’t demonstrate enforcement, CompliancePilot calls that out with a plain-language explanation.
“Missing screenshot” or “Conflicting comment from owner” instead of a generic status badge.
Control requirement, uploaded documents, reviewer comments, evidence status, audit deadline — chips on every important answer.
Evidence requests, clarifications, remediation tasks, and manager summaries are AI-drafted and human-approved before sending.
From role onboarding to manager summary — every screen earns its place by answering one of the user’s questions about evidence or action.
Five role cards (Compliance Manager, Risk Reviewer, Auditor, Evidence Owner, Decision Maker) + priority chips. Drives what rises to the top of the home page.
AI briefing + top compliance blockers, ranked by evidence gaps, control risk, deadline, and review status.
Readiness score, KPIs (controls complete, missing evidence, expired docs, insufficient evidence), and the controls table.
Requirement, plain-language explanation, acceptable evidence, current upload, AI assessment, and approve / reject / clarify actions.
Structured replies: Recommendation, Why, Evidence used, Confidence, Suggested next action — with action buttons that wire back into the app.
Tabs for Evidence, Comments, History, and Sources. Source chips (control requirement, uploaded documents, reviewer comments, evidence status, audit deadline) on the trust tab.
Editable AI-drafted message with recipient, related control, framework, priority, due date, and the specific evidence needed.
Plain-language explanation, requested items with examples, upload + comment + submit. Deliberately shorter than the reviewer surface.
Status, linked control, owner, expiry, AI match confidence, and the next review action — sortable and filterable.
AI-generated readiness summary in plain language, with blockers and recommended next steps. Copy, save, send to manager.
Every AI answer follows the same five-part structure. It’s the contract that lets a reviewer trust the assistant without checking every recommendation against the source data themselves.
In a compliance context, an assistant that can’t be audited is worse than no assistant. CompliancePilot enforces five rules on every AI surface so the reviewer can defend any recommendation.
Every recommendation lists the controls, documents, and comments it drew from.
Stated explicitly — high, medium-high, medium, low — never hidden.
Each AI answer links back to the controls and frameworks it touches.
The exact files the assistant judged, with status and freshness visible.
Sending a request or approving evidence always requires a human click.
Each decision was a trade-off — cleaner cognitive load against feature breadth, AI utility against reviewer accountability.
The home page leads with what needs review and what to do next. KPIs are supporting context; charts only appear when they answer a specific question.
Every document carries status, linked control, owner, expiry, and AI match confidence. The Evidence Hub stands alongside Audits and Controls in the IA — not buried under a single control’s tab.
The AI assesses. The reviewer approves, rejects, or asks for clarification. The two never blur, even visually — AI cards have their own background, review actions have their own region.
Summary first. The reason on demand. The full source trail one click away. The reviewer chooses when to go deep.
No floating claims. The five-part reply contract (Recommendation / Why / Evidence used / Confidence / Suggested next action) is consistent across screens.
All five personas inform the IA, but the MVP commits to the Compliance Manager flow end to end — better to ship one deep path than five shallow ones.
This is a design exploration, not a shipped product — so the value sits in what I’d expect it to unlock and what I learned by building it.
A compliance platform that stops at “document uploaded” isn’t finished. The real question is whether the document actually proves the control — and that’s where AI can earn its place, as long as it cites its work.
CompliancePilot is a sketch of what that contract looks like in product form.