Define the intended behavior
Agree the use cases, intended role, and expected behaviors for one AI application. Establish what the review will cover.
AI evaluation & red-teaming
Confidence should
have a foundation.
Evaluate everyday performance and probe adversarial failure modes to identify what needs 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 combines evaluation of ordinary use with red-teaming: deliberate attempts to provoke failures or bypass intended boundaries. Together, these methods help teams understand weaknesses and prioritize improvements.
What the review covers
Evaluate everyday performance
Test realistic scenarios for accuracy, relevance, consistency, and fidelity to sources. Examine unsupported claims, important omissions, and misleading framing against agreed expectations.
Probe adversarial failure modes
Use deliberately challenging prompts and source content to test resistance to prompt injection, safeguard bypasses, inappropriate disclosure, and departures from the intended role, as relevant to the agreed application.
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.
Workflow development
AI reasoning
workflow development
When an assistant repeatedly overlooks requirements or makes claims its evidence does not support, FarPoint can investigate the problem, develop an agreed workflow revision, and evaluate the revised version in selected scenarios.
The work starts with a defined application: what your team needs the assistant to do, what information it can use, and when it should ask a question or refer a decision for human review. We agree the development work and evaluation criteria around those requirements.
What you can commission
- A behavior specification describing the task, expected responses, and review points.
- A workflow prototype or revision, with implementation support agreed in scope.
- A scenario evaluation documenting successes, remaining failures, and tradeoffs.
- Handoff documentation covering the tested version and maintenance responsibilities.
Our Epistemic Alignment Distillations portfolio shows selected examples from FarPoint's development work and explains the results and limits of its synthetic evaluation.
For workflows governed by legal or policy requirements, the engagement can draw on Legal Engineering to translate client-approved requirements into a process, and Practical AI Governance to define oversight and review responsibilities.
Implementation responsibilities, access, deliverables, and reuse terms are agreed before work begins. Client counsel supplies or approves legal interpretations; your team retains policy and operational decisions. Evaluation findings apply to the conditions tested and do not certify compliance or guarantee truthfulness.
Discuss your assistant workflow with FarPointWhat 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 engagement defines the application, permitted testing methods, and scenarios upfront. Tool use and connected-system behavior are included only where explicitly scoped and authorized. Findings describe the tested conditions; 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