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System Intake & Risk Classifier

Describe your AI system. We map it to an EU AI Act risk tier, NIST RMF, ISO 42001, and GDPR obligations — instantly and deterministically.

ⓘ How to use this tool

What it does: you answer a short questionnaire about your AI system and we instantly classify its legal risk and list the obligations that apply — no files needed.

What you need: just knowledge of the system. Pick the closest option for each field.
Unsure on a field? Choose the safer (higher-risk) option — you can re-run anytime.
You'll get: an EU AI Act risk tier, a mapped obligation checklist, and the exact regulations that apply across authorities (EU, US states, UK, Canada, ISO/NIST, and more).

System name Your email (optional, for saving)

What does the system do?

Primary domain System type

Autonomy level Impact of its decisions

Does it make decisions about people? Affects vulnerable groups? Personal data used

Deployment region Generates synthetic content? Interacts directly with people?

Does it involve any of these practices? (EU AI Act Art. 5)

Social scoring of individuals Subliminal / manipulative techniques Exploiting vulnerabilities Untargeted facial-image scraping Emotion inference at work/school Real-time biometric ID in public Predictive policing by profiling

Fairness & Bias Scanner

Upload a CSV of model outcomes. We compute real demographic parity, the 4/5ths disparate-impact ratio, and (if you have labels) equal-opportunity gaps — entirely in your browser.

ⓘ What is a “CSV of model outcomes” & how do I get one?

What it is: a simple spreadsheet saved as a .csv file where each row is one decision your AI made about one person or case. You need at least two columns:

A protected-attribute column — the group to check for fairness, e.g. gender, race, age_band.
An outcome / prediction column — what the model decided, e.g. 1/0, approved/denied, hire/no-hire.
(Optional) a ground-truth label column — the correct answer. Add it to also measure equal-opportunity (accuracy parity across groups).

Where to get it: export your model's prediction logs, batch-scoring output, or a table of past decisions from your data warehouse, BI tool, or notebook — most teams already log this.

How to create it: in Excel / Google Sheets add the columns above and Save As → CSV; or in Python: df[['gender','prediction','label']].to_csv('outcomes.csv', index=False).

gender	prediction	label
female	1	1
male	0	1
female	0	0

🔒 Parsed entirely in your browser — the file never leaves your device. Download a blank template ↓

Need a sample? Load demo data

Drift & Data-Quality Detector

Compare a baseline dataset against a current one to compute PSI & KS per feature, or scan a single dataset for missingness, constants, outliers and duplicates.

ⓘ What to upload & where it comes from

What it does: compares two snapshots of your data to detect drift — when live inputs have shifted away from what the model was built on (a leading cause of silent accuracy decay).

Baseline CSV — your reference data: the training set, or a known-good period (e.g. last quarter).
Current CSV — recent production data with the same column names. We compute PSI & KS for every shared feature.
Upload only the baseline to run a data-quality scan (missing values, outliers, constants, duplicates).

How to create: export two same-schema CSVs from your feature store / warehouse — one older, one recent. Numeric and categorical columns both work. Download a template ↓

Load demo baseline + drifted current

Documentation & Control Gap Scanner

Paste your model card, system card, or governance documentation. We score it against the control areas an EU AI Act technical file (Annex IV) and a credible model card should cover.

ⓘ What to paste here

What it does: scores your documentation against the 14 control areas auditors expect, then shows exactly which are present, weak, or missing.

Paste: your model card, system card, datasheet, or any governance write-up — even a rough draft or a product spec.
Don't have one yet? Paste whatever description exists; the scan tells you precisely which sections to write next.
You'll get: a completeness score and a present / weak / missing breakdown per control.

Load a thin example

Governance Copilot

Ask the agent anything about your assessment or AI governance. It reads your findings and gives grounded, A-to-Z direction.

ⓘ How the Copilot works

What it does: answers your AI-governance questions and proposes step-by-step fixes, grounded in the results from the other tools (your risk tier, fairness/drift findings, and documentation gaps).

Run the Intake and scanners first for tailored advice — or just ask general questions about the EU AI Act, NIST RMF, ISO 42001, bias, or drift.
It helps you prepare and evidence compliance; it never claims to make a system “compliant” on its own.

Compliance Dossier

Your full assessment, compiled into an audit-ready report with a prioritised remediation roadmap.

ⓘ About this report

What it does: compiles every result into one audit-ready report — risk classification, applicable regulations across authorities, findings, and a prioritised remediation roadmap (each action has an owner, effort, framework reference, and acceptance test).

Print / PDF to share with stakeholders; Save to get a link to resume or share the assessment later.
Run more tools, then click Rebuild to refresh the report and score.