AI Alignment Infrastructure
Agents elicit the judgment your experts hold, engineer the benchmarks that represent your work, and pursue improvements to AI behavior. Your team operates the system around its own expertise, development objectives, and existing tools.
The shared foundation
Expert judgment and benchmark representation develop together. A review can reveal a missing condition; a coverage gap can reveal a question only an expert can settle.
Uncover the judgment your AI has yet to learn.
Give expert judgment a form teams can apply.
Control what your benchmark’s conclusions rest on.
Agents prepare and interpret contributions. Experts retain authority over judgment. Rubrics and policies give that judgment a form teams can apply; coverage defines the situations and proportions their benchmarks should represent.
Three products, one foundation
Weave, Trialground, and Coevolve perform distinct work within the platform. Each uses the same expert standards and coverage design, so a finding can inform the next experiment or the next training dataset.
Coverage-directed case, world, and training-data synthesis
Weave synthesizes cases and worlds from ontology tuples, following coverage designs informed by product managers’ objectives and experts’ experience. The same foundation supports expert review, benchmark trials, and targeted dataset augmentation for weights training.
Managed harness runtime and rubric evaluation
Trialground runs harnesses in managed sandboxes and evaluates their behavior against your rubrics and coverage. Compare candidates under defined conditions, inspect the evidence behind a result, and identify which failures deserve the next development effort.
An AI agent for harness prototyping and optimization
Coevolve develops and optimizes harness prototypes in a sandbox. It investigates failures, tests competing changes through Trialground, and iterates against the behavior you want to improve and preserve. Engineers and coding agents receive tested prototypes and experimental evidence for further development.
Two paths to better behavior
A failure does not prescribe its own remedy. The evidence may call for a change in how the harness uses a model, a distinction the weights need to learn, or a standard that needs clarification.
Trialground exposes candidate behavior under defined conditions. Coevolve investigates and tests harness prototypes against a benchmark goal. Your engineers and coding agents carry tested ideas into further development.
Weave augments training datasets around missing distinctions and underrepresented conditions. Expert rubrics guide what the examples should teach. Training execution and compute connect through the environment agreed for your program.
When neither route has a clear target, return to the judgment or the coverage. Further elicitation can clarify a criterion; new cases and worlds can test whether an apparent gain holds beyond familiar examples.
Start from the work you have
You can bring existing rubrics, a benchmark, production examples, training requirements, or a candidate harness. The first scope can address one unresolved part of the process.
Begin with expert elicitation. Agents prepare reviews and adaptive conversations, then develop findings into proposed rubrics, policies, and coverage. Your team gains a standard it can use in development.
Explore expert elicitation ↗Begin with representation. Examine the dataset across ontologies, identify missing combinations and imbalanced slices, and direct case or world synthesis toward the gaps that matter.
Explore coverage design ↗Begin with trial evidence. Compare behavior against your rubrics, then pursue harness changes with Coevolve or target missing learned distinctions through Weave’s training-data augmentation.
Explore managed trials ↗Within your development stack
Teammately’s role is to organize expert elicitation, benchmark representation, and offline improvement. Your tracing, evaluation, coding, and training tools can remain part of that process. The starting scope establishes the actual exchange of data, artifacts, and execution responsibilities.
An output rating supplies a judgment. Adaptive elicitation investigates its reasoning and conditions, so the result can inform reusable rubrics, policy boundaries, coverage, and training examples.
Ingested interactions help identify new intents and shifts in the case mix. Coverage updates can represent those changes while preserving rare, consequential situations that recent traffic may not contain.
Experts establish judgment, and AI teams set objectives and choose which changes to adopt. Agents undertake preparation, synthesis, and experiments within that scope.
A concrete starting point
Bring the work you have and the decision you need to make. We can identify the expert participation, product scope, and evidence needed for a useful first engagement.