RegOps · The compliance layer for AI engineering
Classify. Implement. Ship.
Compliance runs where your code runs. CLIC scans your AI codebase, gives you an EU AI Act classification with the legal reasoning behind it, and emits the engineering tasks that follow.
// phase 01 · classify — fastest path to AI Act classification
CLIC finds what the AI Act requires.
CLIC identifies regulated AI systems in your repository, maps every obligation, and turns them into concrete engineering tasks.
01scan/repositoryacme/lending-app
// 1,284 files · 2 candidates found
▾app/
·layout.tsx
·page.tsx
▾lib/
·utils.ts
·scoring.ts
·recommend.ts
▾models/
·credit-risk.pkl
·fraud-detect.onnx
·embeddings.bin
▾pipelines/
·training.py
▸tests/
▸node_modules/
·package.json
● credit-scoring [HIGH RISK · Annex III §5(b)]
● fraud-detection [LIMITED · Art. 50]
→ 2 candidates · 1 high-risk · 1 limited-risk
02$ clic check --repo . --json● ready
"status": "warn",
"candidateCount": 2,
"candidates": [
"candidateCount": 2,
"candidates": [
{"label": "credit-scoring","riskClass": "high" ●,"evidenceSummary": "Annex III §5(b) · Art. 9 · Art. 12","tasks": [{"title": "Add transparency notice", "priority": "high"}{"title": "Implement audit logging", "priority": "high"}{"title": "Risk management hooks", "priority": "medium"}]},
{"label": "fraud-detection","riskClass": "limited" ●,"evidenceSummary": "Art. 50 · transparency obligation","tasks": [{"title": "Add transparency notice", "priority": "medium"}]}
]
✓ 2 systems · 4 tasks
// or connect via MCP — zero config
// phase 02 · implement
CLIC Agent implements it.
CLIC and CLIC Agent both ship with an MCP server. Connect once — classify, configure git hooks, manage tasks, and trigger implementation without touching a terminal.
01CLIC/scantasks.json
// 7 obligations · Annex III §5b
T-01
Add transparency notice
T-02
Log inference decisions
T-03
Risk mgmt hooks
T-04
Data governance check
02CLIC Agent/agent● ready
analysing repository
[plan] 4 tasks · 12 files affected
[T-01] insert components/AIDisclosure.tsx
mount in app/layout.tsx ✓
mount in app/layout.tsx ✓
[T-02] wrap lib/inference.ts
with audit logger · +34 lines ✓
with audit logger · +34 lines ✓
[T-03] add CI gate
.github/workflows/risk.yml ✓
.github/workflows/risk.yml ✓
[evidence] writing .clic/
artefacts → conformity bundle
artefacts → conformity bundle
[mcp] git hook configured ✓
[mcp] tasks.json → agent context ✓
committing branch clic/eu-ai-act
~4m avg. runtime · MCP server included
03repo/pull request+ ready to merge
app/layout.tsx+8 −2
return (
<html>
<AIDisclosure />
<Layout>
lib/inference.ts+34 −6
return model.predict(input);
const out = await audit.wrap(
() => model.predict(input),
{ article: 12, system })
return out;
.clic/evidence.md+1 file
.clic/tasks.md+1 file
// 01
Any stack, zero config
Python, TypeScript, Go, Rust. CLIC reads your repository as-is. Regulated AI systems surface from what's actually in your code.
// 02
Fact-Linked Evidence
Every finding is cited to an AI Act article or recital, creating verifiable claims your engineers can act on.
// 03
CI/CD & MCP Native
Run headless in your CI/CD pipeline as a git hook, or connect via MCP server for interactive control. Classify on commit, configure hooks, pipe tasks into your issue tracker — your workflow, your interface.
common questions
Can engineers use CLIC without legal expertise?
Yes. CLIC reads your codebase and outputs concrete engineering tasks — no legal background required. Your legal team reviews the findings.
How do you handle the AI Act's ambiguity?
There is no editorial layer. CLIC maps the current legal text directly to your codebase, and updates when the Commission issues new guidance. Every finding is grounded in the regulation.
How does CLIC fit into an existing pipeline?
CLIC runs via Git hooks or a single CLI command — no new infrastructure required.