We put people in front of your LLM, agent or voice assistant before your customers do, and tell you what broke, for whom, and why.
The assistant had passed every internal accuracy benchmark. None of those benchmarks tested by the language the applicant used.
Multi-step applications that included a self-employment income declaration stalled. Neither the applicant nor a human reviewer was told.
Here is the arithmetic that catches teams out. If each step of a workflow is right 95% of the time, ten steps in a row are all right about 60% of the time. That is 0.95 multiplied by itself ten times, which is 0.599.
A benchmark that scores one response at a time reports the first number. Your customer lives with the last one. The bank's loan assistant on this page is the same story at real scale: it cleared every internal benchmark, and still broke for applicants who used a second language.
So we do not stop at single answers. We run the whole task, in the languages and conditions your users actually bring to it.
We also evaluate vision and multimodal models and AI features inside apps. Pick the closest match below.
We put verified reviewers in front of your model's answers. They rank responses, score them for accuracy, tone and safety, and flag hallucinations and bias. Reviewers are matched by language and domain, so an answer about lending is judged by someone who knows lending.
We run your agent through the whole task and not one reply at a time. Does it finish? Does it call the right tool with the right inputs? Does it hand over to a person when it should? We watch where it goes wrong and write down exactly where.
People who speak the language talk to your voice agent in their own accent, dialect and surroundings. We record whether it recognised them, understood what they wanted and finished the task, including when the background is noisy.
These are the steps from the European bank engagement above, in the order we took them.
The assistant was due to launch across the EU. We set out to pressure-test it against the bank's own accuracy benchmarks, and not simply repeat them.
We worked from EU AI Act Article 13 (transparency) and Article 14 (human oversight), the two obligations an automated lending decision exposes most.
A single pre-launch audit, with a six-week turnaround ahead of the bank's relaunch date.
Verified testers across 22 countries, weighted toward second-language applicants and self-employed profiles, the people a benchmark could not see.
Testers ran the complete multi-step application, exactly as a real applicant would.
Each anomaly, from the routing bias to the silent step failure, was logged and mapped to the requirement it touched.
A list tied to article-level obligations, and not a raw pass or fail score.
Six weeks after the first flagged finding, the bank relaunched across the EU. Wrongful denials for second-language applicants fell 47%. The self-employment failure was rebuilt so it now triggers mandatory human review. The post-launch compliance review closed with zero open findings. Read the full case study
Results land in a dashboard, with the evidence behind each score. You see hallucination, groundedness and safety scores, how reviewers ranked responses, and bias and policy-violation rates, split by language and group.
Figures from Oprimes-validated deployments.
Why now: Gartner forecasts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls. Source: Gartner forecast, June 2025
We use automated scoring for volume, because it is quick and cheap. It is weaker on tone, ambiguity and cultural fit, and it can share the blind spots of the model it is marking. Where automated scoring is unsure, a reviewer who speaks the language and knows the domain makes the call, and we record why.
LLMs, AI agents, voice and chat assistants, vision and multimodal models, and AI features inside apps. If it gives an answer a person has to trust, we can put people in front of it.
Yes. Pre-release and proprietary models are fine. Reviewers work under NDAs, in private task environments with secure access.
It gives your compliance team documented, human-validated evidence, organised against frameworks such as the EU AI Act and the NIST AI Risk Management Framework, as we did for the bank above. It is evidence, and not legal advice.
Send us the model, agent or workflow, the languages and markets it will serve, and your launch date. We will come back with a scope and a timeline.
Book a 30-minute consultation with an Oprimes AI Trust Specialist. We will map your use case, recommend the right service pillar, and give you a delivery timeline before you commit to anything.
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