Trust your AI before it talks to customers
We put your AI systems through the same rigor a tier-1 auditor would, accuracy evals, prompt-injection red-teaming, PII leak checks, and bias testing, then hand you a clear report your board can read and a prioritized fix list your engineers can action.
If your AI is going to answer customer emails, approve loans, or write code, someone should prove it's safe. We do that, so you can ship with confidence.
The business pain we actually solve
Before we talk about "how," here's the kind of problem this service is built for.
One wrong AI answer can make the news
Brand damage, regulatory attention, lawsuits. The cost of a pre-launch audit is a rounding error compared to the cost of a public incident.
Regulators are catching up fast
Singapore's AI Verify, EU AI Act, industry-specific rules, audits are going from 'nice to have' to 'required to operate.'
Prompt injection is real and cheap to exploit
A single malicious email can make your AI assistant leak internal data or take unauthorized actions. Most teams have never tested for this.
Your board wants a risk answer, not a slide of jargon
'Our AI has been evaluated against a 200-case eval set with 94% accuracy and 0 PII leaks', that's what a CFO can sign off on.
Outcomes, not hours billed
Every engagement ships these real things, not status updates or wireframes.
A plain-English risk report for leadership
Executive summary, risk matrix, residual-risk register, the kind of artifact your board, auditors, and insurers all want to see.
A technical remediation plan
Specific code/prompt changes, guardrails to add, infrastructure controls to implement, ordered by risk-to-effort ratio.
Reusable eval suites
We leave behind the test cases, eval harness, and CI integration so your team can rerun the checks on every model change, not just once.
A red-team report
Documented attempts, successful exploits (with proof), and a replay toolkit so engineers can verify fixes.
From first call to live in production
Scope the AI surface
Map every place AI touches your users or data, chat, summarization, classification, agents, internal tools. Prioritize by blast radius.
Build evals + red-team
Custom eval set for accuracy and bias, automated jailbreak harness, manual adversarial testing, PII leak probes, authorization bypass checks.
Report & walk-through
We present findings to exec + eng stakeholders, separately or together. Every finding comes with severity, evidence, and a recommended fix.
Re-test & certify
Once you fix the critical items, we re-run the evals and issue a go-live memo, or a monthly/quarterly continuous-audit retainer.
Under the hood
If you're the CTO, tech lead, or eng manager evaluating us, here's the level of rigor we bring.
Evaluation frameworks
LangSmith, DeepEval, Ragas, HELM, custom harnesses. Human-in-the-loop grading via Label Studio for subjective tasks.
Red-team & jailbreaks
Garak, PyRIT, custom prompt-injection taxonomies, indirect injection via RAG-poisoned docs, tool-use abuse scenarios.
Data & privacy
PII detection with regex + ML classifiers, Presidio for redaction, memorization probes, training-data leakage tests, right-to-erasure verification for RAG corpora.
Bias & fairness
Demographic parity, equal-opportunity, calibration tests across protected attributes, with sensible defaults for region (SG PDPC, EU AI Act Annex III).
Security controls review
Model API key scope, rate limiting, tool-call authorization, sandboxing for code-interpreter agents, supply-chain review for model weights.
Compliance mapping
We map findings to NIST AI RMF, ISO 42001, Singapore Model AI Governance, EU AI Act, so the audit artifact plugs straight into your compliance program.
You'll walk away with
- Executive risk report (10-20 pages, board-ready)
- Technical findings with severity, reproduction steps, and fixes
- Full eval suite + red-team harness, yours to keep
- CI pipeline that runs evals on every model or prompt change
- Mapping to relevant regulatory frameworks
- Post-remediation re-test and sign-off memo
This is a fit if…
- Companies about to launch a customer-facing AI feature
- Regulated industries (fintech, healthcare, insurance, legal)
- Teams that shipped AI quickly and now need to 'make it safe'
- Boards or insurers asking for documented AI risk posture
Most audits are fixed-fee by scope, from a focused 'ship-readiness check' for a single feature to a full multi-system AI governance engagement. Continuous audit retainers available for teams that ship AI every sprint.
Questions we hear most often
We haven't launched yet. Is this still relevant?
That's actually the best time. Pre-launch audits are faster and cheaper because you're not trying to patch production. We can fold the audit into your development cycle so findings get fixed as they appear.
Will this delay our launch?
A focused ship-readiness review typically takes 2-3 weeks. Most findings are fixable in days, not weeks. If something is serious enough to delay launch, you absolutely want to know before you ship, not after.
Can you work with our existing AI vendor?
Yes. We audit what you've built in-house, what's built on top of OpenAI/Anthropic/Bedrock/Vertex, and wrapped third-party AI products. The audit scope is your perimeter, not the underlying model provider.
Do we need a separate audit for each model update?
No, that's why we leave behind reusable eval suites. After the initial audit, your team runs evals in CI on every change. We only re-engage for new features or scheduled re-certification.
What about non-LLM AI, classification models, recommendation engines?
Covered. We test classical ML systems for accuracy, drift, fairness, and security (e.g., adversarial inputs, data poisoning), same methodology, different tools.