Solutions · AI Evaluation & Red Teaming

Break it
before they do.

Qapitol evaluates LLMs, copilots, RAG systems, multimodal AI and agents for reliability, safety, accuracy and domain fit — then attacks them adversarially to surface the failure modes before your users, or a real attacker, find them.

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Adversarial evaluation, running

0 / 60 attacks got through · illustrative

The gap

Why off-the-shelf evals aren’t enough

There are open eval tools for the generic failure modes — toxicity, basic hallucination, a standard jailbreak set. They’re necessary. They’re also where everyone starts and where most stop.

The evaluations that decide whether a system is signable are the domain-specific ones: does this system give correct answers for your use case, under your edge cases, against your definition of right? A generic benchmark can’t tell you that. It tells you the model is broadly capable, not that your system is safe to ship.

The scope

What we evaluate

Four layers, from the systems we cover to the behaviours we re-check every time they change:

Systems covered

LLMs, copilots, RAG systems, multimodal AI, agents

Failure modes

hallucination, bias and fairness, sensitive-data leakage, prompt injection, unsafe agency, robustness under adversarial input

Domain correctness

scored against rubrics built for your use case, not a generic leaderboard

Regression

the behaviours you validated, re-checked when the system changes

Find out how your AI fails before your users do.

The method

Rubrics, scoring + human review

Evaluation that holds up combines automated checks, LLM-as-judge where it’s reliable, and human review where it isn’t. Qapitol builds the scoring rubrics with your domain experts, runs them repeatably, and keeps the human in the loop on the calls that matter.

Automated checksLLM-as-judgeHuman review

The attack

Red teaming

Separately from “does it work,” red teaming asks “how do we break it” — adversarial prompts, injection attempts, edge cases designed to surface unsafe behaviour before a real user or a real attacker finds it.

Where it fits

Standalone, or the evaluation layer in a Sign-Off Program

Sold standalone for teams that know they need evals, or as the evaluation layer inside a Sign-Off Program. Either way, evaluation feeds sign-off — it’s the evidence that a system behaves correctly.

Pricing: Evaluation and red-teaming engagements are scoped to your systems, use cases and risk profile — contact us for a quote.

Find out how your AI fails — before your users do.

Start with the Exposure Snapshot, or bring us the system you need evaluated and red teamed.