Engineering · Trust

How we hold ComplyEdge to its own standard

We sell AI-Act compliance, and we build with AI. Here is a two-minute inspection a technical buyer can run — from public artifacts — without taking our word for anything.

ComplyEdge Engineering · Verified July 2026

Every compliance vendor says “trust us.” That is the wrong pitch for this buyer. Below: five checks you can run yourself. Steps 1–3 run against the public OSS repo github.com/ComplyEdge/complyedge. Step 4 points at public CI. Step 5 is about how we govern AI in our engineering process.

The two-minute inspection

01 The offline engine is real ~20s · no account

Install TrustLint from PyPI and score a prohibited-practice prompt:

pip install trustlint
trustlint check --verbose --text "implement a social credit score for all citizens"
Expect: exit non-zero · [CRITICAL] EU_AI_ACT_ART5_SOCIAL_SCORING_001 · citation to Regulation (EU) 2024/1689, Article 5(1)(c) · the published CLI corpus reports how many rules it evaluated (currently 52 on PyPI).

A real engine returns a rule ID and a statute cite, not a vague score. --verbose prints citation + remediation; without it you still see the rule ID and severity summary.

02 Published GPAI scores re-derive offline ~40s · no API key

From a checkout of the public repo:

python scripts/benchmark/benchmark_runner.py \
  --providers providers/ \
  --schema scripts/benchmark/provider_schema.yaml \
  --output /tmp/out.json
Expect: Scored 12 providers · Average compliance: 49.6% · best/worst labels match the committed provider YAMLs.
Scope: this command needs no network and no ComplyEdge key. Longer method notes live in the diligence pack (docs/dd/benchmark-reproducibility.md on the private platform), not in the thin public tree.
03 Audit exports are tamper-evident ~30s

The public sample export verifies with the same hash-chain helper we ship — and fails if you flip one field:

python3 - <<'PY'
import sys, json
sys.path.insert(0, "services/api")
import audit_export

e = json.load(open("docs/dd/sample-audit-export.json"))
print("verify:", audit_export.verify_audit_export_chain(e))   # True

e["events"][42]["allowed"] ^= True
print("tampered:", audit_export.verify_audit_export_chain(e)) # False
PY
Expect: verify: True then tampered: False on the public sample (100 events, sha256-v1 chain).
04 Enforcement ships as CI you can install ~20s to read

Three public surfaces — pick any:

Clarity: the Action’s end-to-end selftest workflow lives in our private monorepo CI. What is public on the Action repo is the installable @v1 surface + fixtures you can run locally.
05 We govern our own AI-assisted engineering ~10s to read

ComplyEdge is built with heavy AI assistance — and we measure it in git. In the engineering monorepo, over 40% of commits carry a Co-Authored-By: Claude trailer (224 of 534 commits as of this verification).

That AI-touched code still goes through:

Scope: commit % and the 1,500+ test count refer to the private engineering monorepo (data-room / diligence). The public OSS export is a thinner slice (~170 collected tests) — useful, but not the same tree.

Why an inspection beats a logo wall

A testimonial is unfalsifiable. An inspection is the opposite: deterministic cites, a hash chain that breaks when tampered, benchmark numbers that re-derive from committed YAMLs, and CI you can clone. Holding ourselves to that standard is the only honest way to sell runtime enforcement — and the fastest path for a technical buyer to get to “yes, this is real.”

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Public: OSS repo · trustlint-action@v1 · live seals at complyedge.io. Full data room: assembled within 48 hours of a request.