Teammately Research
We research modern infrastructure for keeping human intent connected to increasingly open-ended AI behavior—without turning domain experts into full-time annotators.
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Research direction
Generic judges can test broad qualities. Enterprises still need a way to define the behavior their own specialists consider correct, including the exceptions and trade-offs that public benchmarks miss.
Our research treats expert judgment as development infrastructure: something that can be elicited, structured, tested, reused, and refined as the AI system changes.
01Expert-grounded correctness
We study methods for eliciting tacit preferences, exceptions, and disagreement from domain experts—and turning that evidence into policies and binary rubrics that development teams can use.
Discuss this direction ↗02Coverage engineering
Our work explores deliberate coverage design: deriving dimensions, topics, and case-construction patterns from requirements and domain context before synthesis begins.
Discuss this direction ↗03Human–AI learning loops
We investigate agent-assisted workflows that prepare comparisons and unresolved questions, so scarce specialists focus on the decisions only they can make.
Discuss this direction ↗04Auditable improvement
We connect benchmark cases, trajectories, rubric evidence, and expert decisions so improvement remains inspectable across model, agent, and harness iteration.
Discuss this direction ↗Our method
Find where domain intent becomes ambiguous or lost.
Represent decisions as coverage, policies, and rubrics.
Run the specification against challenging behavior.
Bring uncertainty back to the people qualified to resolve it.
Research collaboration