No model to defend
We didn’t train it, so we have no stake in pretending it works. We report what it does in your conditions.
The category we’re in
A control layer, not AI testing.
AI risk doesn’t live only in the model. It lives in the system of work around it — data, prompts, tools, APIs, human approvals, agents, business rules, fallback paths, regulatory duties, production drift, and the release decision itself. Test the model in isolation and you’ve checked one component of a system you still can’t account for.
That’s why we don’t describe ourselves as an AI-testing service. We operate a control layer across the AI lifecycle — discover what’s running, evaluate how it behaves, fix the control gaps, and produce the evidence required to sign off.
What this is not
- AI testing services
- LLM evals
- QA automation with GenAI
- Responsible-AI consulting
- Compliance documentation
Each of those is a component. None of them is sufficient on its own. The category is an AI assurance operating layer — and it’s the part nobody owns clearly yet.
Why independence matters
Independence is the product.
We don’t build the models. We don’t resell the platforms. We sell assurance — which means we have no commercial reason to look away from a failure. The vendor who shipped your model is the wrong party to certify it’s safe. Assurance only means something when the party giving it has nothing to lose by telling you the truth.
An assurance provider with an incentive to hide a failure isn’t assurance.
What makes us different
Three things competitors usually have one or two of.
Pure-AI consultancies can’t claim a testing heritage. Testing shops can’t evaluate non-deterministic systems. We bring three things together.
The beliefs underneath the work
- AI assurance will be continuous, not episodic — systems drift, call tools, and act, so one-time testing doesn’t survive agentic AI.
- The buyer is a coalition, not a person — CIO, CTO, CISO, Risk, Legal, Compliance, and the business owner all have to be able to sign off.
- Model evals are necessary but not sufficient — manipulation, bias, leakage, hallucination, unsafe agency, and drift aren’t solved by benchmarking the model alone.
- Domain-specific assurance beats generic governance — BFSI, fintech, and SaaS each carry different regulatory and risk shapes.
- The winning model is tools plus managed assurance plus evidence — platforms create reach; you still need an accountable operator running the cadence.
Who we’re for
For companies where AI is live, and no one can sign off.
We sell to companies where AI is already live — but no one can confidently sign off on it. If your AI is still a proof of concept, we’re early for you.
A fit
- AI already in production — chatbots, copilots, internal tools, workflows, agents
- Medium-to-large enterprise or a fast-scaling tech company
- Someone about to say “I can’t sign off on this” — and unsure what to do next
Not a fit
- Still in the AI proof-of-concept stage
- Asking how to start using AI
- Shopping for QA staff augmentation or AI exploration