Scope
We design empirical safety studies with explicit assumptions, documented limitations, and reproducible evaluation logic. Work may include protocol design, benchmark critique, taxonomy development, or safety-case evidence synthesis.
Service
AI Safety Research & Methodology
Evaluation methodology, evidence synthesis, and safety research for teams building and assessing advanced AI.
What we do
Deliverables
- Evaluation protocol and measurement plan
- Metric definitions and failure taxonomy
- Evidence memo for technical and governance audiences
- Recommendations for follow-on testing
Engagement structure
Week 1
Research question framing
Weeks 2-5
Method design and evidence review
Week 6
Protocol and briefing
When to engage us
Your metrics are unstable or contested
We clarify what is being measured and what the evidence can support.
You need defensible evidence for leadership
We translate research uncertainty into decision-relevant claims.
Your team is moving beyond ad hoc testing
We help create repeatable, reviewable methodology.
You are preparing public or partner-facing evaluation reporting
We shape claims so caveats and interpretations are explicit.
Related services
Safety methodology often pairs with technical writing.
Good evaluation work needs clear interpretation layers for executives, researchers, and external reviewers.
Method package
Protocol, metric definitions, failure taxonomy, limitations memo, and a reusable structure for future evaluations.
FAQ
We can design studies, review evidence, or execute scoped tests depending on access, timeline, and safety constraints.
Yes. Most work is collaborative and designed to strengthen internal capability.
Yes. Outputs can be formatted as internal protocols, technical reports, public white papers, or executive summaries.
Strengthen your evaluation approach.
Start with the question you need to answer.
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