Define the intended behavior
Agree the use cases, intended role, and expected behaviors for one AI application. Establish what the review will cover.
AI response quality & risk review
Confidence should
have a foundation.
Examine how your AI assistant responds, where it falls short, and what to improve.
The challenge
A convincing response
is only the beginning.
An AI response can sound convincing while overlooking important context, making unsupported claims, or drifting beyond its intended role.
FarPoint evaluates AI assistants in realistic and challenging conversations to help teams understand those weaknesses and prioritize improvements.
What the review covers
Test realistic and challenging conversations
Develop scenario-based tests, including adversarial testing where relevant. Examine unsupported claims, important omissions, misleading framing, and failures to stay within the assistant’s intended role.
Prioritize improvements
Document findings with concrete examples. Recommend changes to instructions, knowledge sources, and review workflows, with implementation responsibilities agreed with your team.
Evaluate agreed revisions
Review agreed changes in a follow-up round and document how they performed in the tested scenarios.
What you take away
Observed weaknesses.
Evidence for the next decision.
A clear account of observed weaknesses, prioritized recommendations, and evidence of how agreed revisions performed in the tested scenarios.
Scope & responsibilities
Findings grounded
in the scenarios tested.
The review covers agreed scenarios for one application. Adversarial testing is used where it serves that review; the engagement is not exhaustive security testing or a compliance certification.
Your team implements changes unless specific support is agreed in scope. Follow-up findings describe observed performance and do not guarantee truthfulness or error-free operation.
Discuss a review