Your AI Is Running. Is It Running Safely?
Most organizations only discover AI failures after they cause financial loss, regulatory exposure, or a board-level incident.
Your clients come to you because:
The AI risks keeping executives up at night:
Incorrect AI decisions are generating losses before anyone detects them
Regulators are demanding evidence of AI reliability — not assurances
AI has introduced new attack surfaces your current controls don't cover
One high-profile AI failure is now a board-level event
No one internally can prove the AI is behaving as intended
Engenious closes this gap with independent AI Audit as a Service.
We deliver board-ready assurance across data, models, security, fairness, and governance — under your brand, with zero conflict of interest.
Most Organizations Don't Know Where Their AI Will Fail
They only find out after it does. Standard reviews stop at the model layer — but failures originate much earlier.
01 Data Pipelines
UNAUDITED
02 Infrastructure Design
DRIFT DETECTED
03 Governance Gaps
NO COVERAGE
04 Model Layer
WHERE REVIEWS STOP
05 Output Layer
USER-VISIBLE
06 Failure Event
→ BOARD-LEVEL
Five Domains. One Complete Picture.
We audit every layer standard reviews miss — from data pipelines through governance — so failure never reaches the board room.
Data Integrity
Lineage, quality, drift, and privacy across the full data lifecycle
Model Performance
Accuracy, consistency, and error characterisation under real load
Fairness & Bias
Segment-level analysis and measurable business impact assessment
Security & Robustness
Adversarial resilience — prompt injection, exfiltration, tool abuse
Governance & Compliance
Documentation, monitoring, and incident response readiness
Every finding is a business risk — not just a technical flag
Your Brand. Our Expertise. Their Confidence.
You own the client relationship. We deliver the audit. White-label or co-branded — your practice expands instantly, with no new headcount and no conflict of interest.
Your Clients Are Already Being Asked About AI Risk
The window to lead is now — before competitors catch up:
Boards and regulators are demanding AI assurance today
CISOs, GCs, and CFOs are fielding these questions right now
The EU AI Act requires evidence of reliability and governance — not promises
Competitors are still building this capability from scratch
You can offer it immediately, under your brand, with zero new headcount
You already have the trust. AI auditing is a natural extension of your practice.
What Happens When AI Goes Unaudited
Air Canada — AI Created Legal Liability
Customer-Facing AI Without a Control Layer
Air Canada's chatbot gave customers incorrect refund advice — authoritative answers that weren't grounded in actual policy.
The court held the company legally responsible.
How an audit prevents this:
Ground responses in approved policy data
Enforce business rules before any output is shown
Adversarial testing for edge cases and policy violations
Lesson: AI output creates direct legal liability. If you can't prove it's controlled, you can't defend it.
Zillow — $500M Lost to Unvalidated AI Decisions
AI Decision System With No Real-World Validation
Zillow deployed a pricing model without stress-testing against real market conditions. Model errors scaled into thousands of purchasing decisions before detection — resulting in $500M+ in losses and a full shutdown.
How an audit prevents this:
Scenario simulation under real-world variability
Continuous post-deployment monitoring
Risk thresholds and automated fail-safes
Lesson: A model that works in testing and fails in production is an unaudited model.
iTutorGroup — $365K Settlement for AI Bias
AI Hiring System With No Fairness Validation
iTutorGroup's automated hiring AI systematically rejected older applicants. There was no audit trail, no visibility into decision logic, no bias testing.
The result: a $365,000 settlement, forced redesign, and reputational damage.
How an audit prevents this:
Bias testing across demographic segments
Transparent, traceable decision records
Regulatory compliance validation before deployment
Lesson: AI decisions carry the same legal weight as human ones — without the accountability layer unless you build it.
Chevrolet — AI Manipulated Into Breaking Business Rules
Customer-Facing AI With No Adversarial Controls
A Chevrolet dealership chatbot was prompted by a user into offering a vehicle for $1. The AI ignored constraints, produced out-of-boundary outputs, and made unintended commercial commitments.
How an audit prevents this:
Prompt injection and adversarial scenario testing
Hard output constraints and business rule enforcement
Response validation before delivery to users
Lesson: If your AI hasn't been adversarially tested, someone else will test it for you — publicly.
The right fit for firms that:
Serve clients who run AI in regulated industries
Want to offer AI assurance without building it in-house
Are fielding board or regulatory AI risk questions
Have cybersecurity, risk, or legal tech as their core practice
Need an independent audit — not a vendor selling the fix
Are responding to EU AI Act or other compliance mandates
Four Deliverables. Zero Ambiguity.
- Start with a Discovery Call
EXECUTIVE RISK REPORT
Board-ready. Decision-grade.
Every audit finding translated into business risk language, organized by the five domains. Designed for executive decision-making, not engineering review.
The result: Your client's board has the evidence they need to act — and the confidence that AI is under control.
- See What's Included
TECHNICAL DEEP DIVE + REMEDIATION PLAN
Layer-by-layer. Action-ready.
Full technical evidence with layer-by-layer detail — for engineering and AI teams to understand, prioritize, and act on. Paired with a remediation plan: quick wins, structural fixes, and long-term investments with effort and business impact estimates.
The result: Engineering teams know exactly what to fix, in what order, and why it matters to the business.
Know Exactly Where Your AI Will Fail — Before It Does
Let's assess your AI risk exposure and build a plan to close it.