Governance as infrastructure, not paperwork.
Regulators don't audit intentions — they audit evidence. We engineer the evidence layer: risk classification, validation protocols, decision logs, and human-oversight mechanisms that live in your stack and survive an inspection.
✓ frames: EU AI Act · GxP / 21 CFR Part 11 · NIST AI RMF · ISO 42001
✓ ip transfer: complete · lock-in: none
✓ delivery: hyderabad · timezone overlap: US/EU
# every claim on this page is contractually testable
The gap between your AI policy and your AI systems is the audit finding.
Most organizations have a responsible-AI policy PDF and systems that can't produce a decision log. When the EU AI Act's high-risk obligations bite — or a GxP inspector asks how a model was validated — the PDF doesn't help. Compliance is a systems property. It has to be built.
Capabilities
Risk classification & gap analysis
Your AI portfolio mapped against EU AI Act risk tiers and sector rules, with a prioritized remediation plan — engineering tasks, not platitudes.
Validation engineering (GxP)
Computer system validation for AI: IQ/OQ/PQ protocols, performance qualification for models, change control that survives model updates.
Audit trails & decision logs
Immutable, queryable records of every model decision — inputs, version, confidence, override — designed for inspection, retained per policy.
Human oversight by design
Confidence-routed review, override mechanisms, and escalation paths designed into workflows — meaningful oversight, not a rubber stamp.
Bias & robustness testing
Disparate-impact analysis, adversarial probing, and degradation testing on your actual data slices — with thresholds wired into CI.
Model risk management
Tiered MRM frameworks adapted from banking practice (SR 11-7) for AI: inventory, validation cadence, monitoring obligations per tier.
The approach
A sequence, because the order is the point: each phase gates the next on evidence.
Inventory & classify
Every model and AI feature catalogued, risk-tiered against the regulations that apply to you, gaps made explicit.
Design controls
For each gap: the engineering control that closes it — logging, validation, oversight, documentation — specified and estimated.
Implement
Controls built into pipelines and platforms. Compliance artifacts generated by the system, not written after the fact.
Evidence & rehearse
Audit-pack assembly and a mock inspection. You face the real one having already passed a harder version.
Deliverables
- AI inventory with risk classification
- Regulation gap analysis + remediation plan
- Validation protocols (IQ/OQ/PQ for AI)
- Immutable audit-trail implementation
- Human-oversight workflow design
- Bias and robustness test suites in CI
- Model risk management framework
- Inspection-ready evidence pack