Know how your AI system actually behaves, before your customer finds out.

No. We can evaluate any AI agent, chatbot or automation in production, whoever built it. The audit is independent by design. In fact, an outside read is often more useful precisely because we did not build the thing and have no reason to flatter it. If the system came from another vendor, you get a neutral verdict you can take back to them with the exact prompts that failed.

That is not what this is. The audit is assessment, not a sales funnel for a rebuild. You get a scored report and a ranked fix list, and what happens with it afterward is entirely your call: fix the findings in-house, hand them to whoever built the agent, or decide to rebuild. We score the system the same way even when we built it.

It means a specialist deliberately tries to break your agent the way a bad actor would. We use the recognized attack families from current practice: jailbreak and role-play prompts that try to override its rules, injected instructions hidden inside ordinary-looking messages, off-topic and inappropriate bait, and attempts to make it leak information or misuse anything it can act on. The OWASP Top 10 for Large Language Model Applications ranks prompt injection as the number-one risk (OWASP, 2025), so it is where the testing pushes hardest on any agent that can do more than talk.

It is not eyeballed. We build an evaluation set from your real questions paired with your own approved, correct answers, then run it through the live system with each response scored against the truth. That produces a measured rate of right, wrong and invented answers, not a vibe. Independent 2026 analysis puts customer-support chatbot hallucination in the range of roughly 15 to 27 percent (SQ Magazine, LLM Hallucination Statistics, 2026), which is exactly why measuring your specific system matters more than trusting an average.

No. A technical specialist designs, runs and interprets the evaluation. Tooling helps execute large batches of test prompts, but a person builds the ground-truth set, chooses the attacks, judges the results and writes the findings. What you get is a human verdict on a machine, not a machine grading itself. Nothing in the report ships without that specialist review.

This service is the most checkable thing on offer. Every finding in the report comes with the exact prompt that produced it, so you can reproduce and confirm it as real. The named company behind the work is RavenGroup Global Tech Private Limited, and billing is in USD. If a failure is in the report, you can watch it happen firsthand.

That a technical specialist designs, runs and reviews the evaluation, that every finding is reproducible with the prompt attached, and that the verdict does not change depending on who built the system. There is no guarantee an agent will pass, and no promise of a specific accuracy or safety score, because that depends entirely on the system under review. A clean audit also does not mean your agent can never fail. Accepted 2026 practice is to re-test on every release, because a single prompt edit or model swap can reopen a risk (StingrAI, AI Red Teaming for LLM and Agentic Apps, 2026).

More often than most owners expect. A system that tested clean can fail after a model update, a prompt change or a new tool is connected, because each of those reopens the attack surface. Current practice is to test before and after deployment and again on every meaningful release. If your agent changes regularly, the Continuous Assurance scope re-runs the evaluation on an agreed cadence so a quiet update does not quietly break something.