Correctness Infrastructure

make your specialist AI trustworthy
with your in-house experts’ judgment,
scaled by AI Agents

Four foundations for trustworthy enterprise AI

Internal standards, made executable

Turn your experts’ tacit judgment, policies, preferences, and exceptions into clear standards your AI systems can be tested against.

More value from scarce expert time

AI agents prepare cases, comparisons and open questions, so your experts spend time only where human judgment materially changes the benchmark.

Benchmark coverage by design

Design what your benchmark should represent from requirements, domain context and existing cases, then fill important gaps with selected or synthesized cases.

Benchmarks that guide development

Carry the same expert-grounded benchmark across model, agent and harness iteration, whether you use Teammately or the development tools your team already trusts.

An impressionist cockatiel in the Australian outback

Generic judges are a starting point. They are not your correctness specification.

Quick start with LLM-judges to check faithfulness, relevancy and toxicity might be a good start for prototypes. Serious enterprises build their own benchmarks by inviting their own experts and defining rubrics at case level.

We help enterprises scale this expert-in-the-loop, at shorter time of scarce experts and higher Return-on-Expert-Effort.

Built for teams engineering AI behavior, not just software around it

Specialist AI needs dedicated infrastructure for shaping behavioral benchmarks, operationalizing internal domain judgment, and carrying those standards into development.

Core platform

Coverage Engineering

Benchmark coverage by design.

Turn requirements, internal materials, and existing cases into a deliberate coverage plan. Agents help reveal missing situations and difficult combinations while your team remains in control.

Core platform

Correctness Elicitation

Make in-house judgment executable.

Use focused comparisons, cases, and conversations to turn specialist judgment, preferences, exceptions, and disagreement into standards your AI systems can be evaluated against.

Built-in engine

Weave

Create the cases and worlds your benchmark needs.

Generate cases, response variants, multimodal artifacts and worlds the agents run from the structured coverage design rather than through undirected synthetic generation, to fulfill and strengthen when your own case pools might miss the challenging facet tuple.

Built-in engine

Trialground

A managed proving ground for harnesses and weights.

Run prototype harnesses and weights against benchmark cases and worlds in isolated managed environments, scale repeatable parallel trials without building bespoke testing infrastructure and combat against agent jailbreaks, and keep responses, trajectories and rubric results connected to the benchmark.

Optional add-on

Coevolve

Explore multiple improvement directions in parallel.

Let evolutionary agents pursue different harness hypotheses, create and test candidates, and continue from stronger branches instead of iterating one change at a time, to better instruct your coding agents on where to patch next based on the benchmark scores. Teammately Coevolve also finds the missing coverage and correctness during its iteration and suggests expert contribution opportunities, so it works as a bridge between human domain experts and coding agents.

Core platform