Response accuracy and grounding
Check whether answers reflect the approved material, distinguish known facts from guesses and use references appropriately.
AI solutions / Quality evaluation
Before an AI system goes live, know where it fails.
AI outputs can sound convincing even when they are wrong. We help define how an assistant should behave, test it against meaningful scenarios and make the limits visible to its owners.
The business case
Good evaluations start with real work, not a single score.
Reliable evaluation requires representative questions, clear criteria and a way to review mistakes. We can examine responses against approved source material and classify the failures that matter to a business.
Where it can help
Start with a defined use case.
Each example is a potential project type. We assess the business requirements and systems before offering a solution.
Assistant behaviour and escalation
Test whether the system refuses unsupported actions, follows its scope and moves uncertain cases to a person.
Language and consistency checks
Evaluate clarity, tone and consistency across supported languages, including Hungarian-language content where the scope and review criteria are agreed.
Project scope
What an engagement may include.
Deliverables are defined for the actual use case, not bundled indiscriminately.
- Evaluation plan with business-relevant acceptance criteria
- Curated test scenarios, including ambiguous and failure cases
- Structured findings with reproducible examples and prioritised issues
- Recommendations for human review, prompts, data quality or workflow changes
- A repeatable approach for regression checks after updates
What helps us begin
A useful first brief.
You do not need to share confidential data through this website.
- A description or demonstration of the assistant and its intended scope
- Approved examples of correct and incorrect behaviour
- Representative test content without unnecessary customer personal data
- A named owner for accepting or rejecting the resulting risks
Responsible implementation
An automated checker is not an independent guarantee.
- AI-based graders can also be wrong; critical decisions need human review.
- A test sample does not establish universal accuracy or legal compliance.
- Evaluation criteria may change when content, models or workflows change.
- Security, privacy and regulated use cases may require specialist assessment beyond a general quality review.
Useful results include error categories, reproducible failure cases, adherence to agreed answer criteria, the rate of appropriate escalation and whether fixes hold up in regression tests.
For project sponsors
Questions worth asking.
Can you certify that an AI system is safe?
No. We can provide scoped evaluation and documented findings, not a formal certification or a guarantee of safety.
Can you test Hungarian AI answers?
Hungarian-language evaluation can be included where suitable reference material and human review are available. Language fluency alone is not enough for specialised legal, medical or other regulated claims.
Do we need an existing AI assistant?
A working prototype, representative outputs or a clear design can be evaluated at different levels. The scope is agreed before the review.
Is this a one-time audit or ongoing work?
It can be a focused assessment or a repeatable test process following updates, depending on the system and the needs of its owners.
Start with the business requirement
Have a project in mind?
Tell us about the work, the existing systems and what a good result would look like. We will assess whether the project is a fit.