Stream D - Fixed-price AI assurance audit
Know which AI outputs can be trusted before they reach a customer, regulator, or board.
Blu Wingu runs fixed-price AI assurance audits that test deployed LLM systems, generated artefacts, and implementation evidence against the failure modes benchmarks miss.
Buying situation
Start with the problem you are really trying to escape.
Use us when
- A consequential workflow depends on AI output.
- Internal audit needs evidence, not a model leaderboard.
- Your team can see the answer but cannot prove the grounding.
- A vendor claims reliability, but you need independent verification.
What you are choosing against
Model benchmarks
Measure generic capability, not trust in your domain workflow.
Eval SaaS alone
Useful dashboarding without corrected artefacts or architecture judgement.
Red-team only
Adversarial prompts without claim-level grounding and remediation.
Audit after deployment
Late discovery, weaker remediation window, and more operational risk.
Engagement shape
What Blu Wingu installs.
Pre-launch AI assurance audit
Defined artefact or deployment scope, evidence pack, and remediation priorities inside a 10 to 15 working-day window.
Karpathy-6 output verification
Claim extraction, source grounding, failure-mode classification, and corrected output set for Category-A findings.
Implementation verification
Requirements, design, and build conformance using Independent Extractor and Plan Baseline separation.
LLM-as-a-Judge selection review
Candidate model evaluation for your domain rather than generic benchmark position.
Continuous assurance
Monthly or quarterly sample, standing faithfulness dashboard, and board-ready remediation record.
Why Blu Wingu
Why Blu Wingu is the obvious fit.
Six failure modes, not one hallucination bucket
Fabrication, Misattribution, Inference Leakage, Severity Inflation, Phantom Consensus, and Omission each get a distinct test.
Adversarial isolation
Reviewers test claims without being allowed to see the material that would bias their finding.
Corrections, not just findings
Reports include corrected outputs for Category-A defects, not just a risk register.
Regulator-aligned evidence posture
The audit trail is designed to support conformity assessment and materially reduce audit risk.
Proof, translated
Each proof point has a job: show what risk comes down.
Karpathy-6 has been applied at enterprise scale across multi-document AI-generated corpora.
What it proves
The method finds Category-A fabrications, misattributions, and severity inflations standard review misses.
Why it matters
You know which outputs can be trusted and which must be corrected before consequential use.
LLM-as-a-Judge work has run in the SAP HCSM context at 106,000 end users and 16 billion transactions per year.
What it proves
Evaluation can operate inside formal enterprise security and governance constraints.
Why it matters
Assurance is not confined to lab conditions or vendor demos.
Implementation Verification and Workshop-to-Design audits separate source evidence, plan, and built system.
What it proves
Blu Wingu can audit the chain from workshop evidence to design output to implementation.
Why it matters
The assurance scope can cover both what the model says and what the programme built.
Commercial posture
Fixed-price audit first. Continuous assurance when the system warrants it.
The standard engagement is scoped to a defined document set, model deployment, or implementation and priced in writing before work begins. Ongoing assurance can add monthly or quarterly sampling.
- No variable billing for findings volume.
- Per-claim metering available only for standing assurance.
- Reports are engineered for conformity assessment and internal governance evidence.
What this service is not
It is not a benchmark comparison service. Benchmarks tell you how a model behaves in general. Evaluation, Verification and Assurance tells you whether your system, your generated artefacts, and your implementation evidence behave within bounds your organisation can explain, evidence, and govern.
What the audit leaves behind
The standard audit produces a claim-level evidence trail, failure-mode classification, corrected outputs for Category-A findings, and remediation priorities where the architecture surrounding the model needs to change. For implementation work, the Independent Extractor and Plan Baseline pattern separates what was built from what was promised, revealing omissions that plan-anchored review can miss.
Evidence boundary
Karpathy-6 is the flagship methodology behind this stream, but this page is the buying door. The detailed method lives on the Karpathy-6 page; the service exists to turn that method into an audit, assurance cadence, or model-selection review. Regulatory language stays calibrated: the evidence trail is designed to support conformity assessment and materially reduce audit risk, not to guarantee a regulatory outcome.
Ready to engage?
Commission an Evaluation Audit
One conversation confirms the outcome target, the evidence we need, and the engagement shape. No generic contact form. No theatre.