AI Alignment Infrastructure
Make your enterprise’s expertise part of how your AI learns and improves.
A platform where AI agents elicit expert judgment to engineer rubrics and benchmarks, and to improve harnesses and weights.
Talk to our team
Agents prepare and adapt expert sessions to uncover the criteria, exceptions, and preferences behind a judgment. Experts contribute their reasoning while agents undertake the preparation and interpretation needed to put it to use.
Carry a judgment beyond the review in which it was made. Rubrics express benchmark criteria; policies organize their scope and precedence so teams can apply shared standards across the enterprise.
A benchmark should represent the work your AI must handle, including difficult and consequential conditions. Coverage across ontologies makes the intended situations and proportions explicit, guiding the construction of cases and worlds.
Use the same expert standards to guide harness changes and targeted training data. Evaluate the resulting behavior against planned coverage, keeping gains, regressions, and unresolved judgments visible.
“We have the technical expertise to improve AI, but how we improve it depends on business context and domain knowledge. Bringing that knowledge into the development process is a major challenge.”
Retail · Merchandising
A relevant product may still be the wrong recommendation. Customer intent, assortment priorities, availability, and acceptable alternatives change what an experienced merchant would choose.
“What I see in Teammately is a way to turn our experts’ knowledge into a lasting capability for AI development. It goes beyond collecting a dataset once—it gives teams the tools to keep building and improving their own.”
Software engineering · Coding-agent skills
Passing tests can leave architecture, maintainability, and local conventions unresolved. A useful skill must carry engineering judgment into unfamiliar tasks.
“A real product needs its own definition of success. Human reviewers have to explain what’s good or bad—and why. Then you have to check whether the AI judge agrees.”
Logistics & industrial operations · Agents in worlds
An action can be locally reasonable and still leave the next decision in a worse state. The benchmark needs to represent consequences, dependencies, and recovery.
“Teams need a way to see how far their evaluation dataset diverges from actual real-world usage—and when they need to update their test cases.”
Model development · Training data
Repeated failures can reveal a missing distinction or an underrepresented condition. Expert rubrics and coverage direct Weave’s training-data augmentation, with independent assessment testing what the resulting weights have learned.
Airlines · Customer support
Passenger support combines fare rules, connection dependencies, limited rebooking options, and service recovery. Capture how experienced teams weigh those constraints, then test whether agents can reach feasible resolutions, explain trade-offs, and recognize when an exception needs expert attention.
Financial services · Research
Citing a source does not establish that a conclusion is warranted. Expert judgment determines which evidence applies, what remains uncertain, and how far a recommendation can go.
“Teammately is addressing many of the problems we’re trying to solve. I can see how our organization could benefit.”
Research
Structured Supervision
We compare forms of expert supervision by the behavioral improvement they produce for equivalent human effort.
Explore the research →
Benchmark Science
We study how coverage, case construction, and holdout design determine what evaluation results can reliably establish.
Explore the research →
Behavioral Adaptation
We investigate how structured intent can improve prompts, harnesses, training data, and model weights without merely learning the test.
Explore the research →
Delegated Agency
We study how objectives and constraints change as work passes through agents, subagents, tools, and long-running processes.
Explore the research →Evaluating Teammately
Scale, Surge, and Mercor are reference points for expert-powered data, evaluation, and model improvement programs.
Agents prepare and adapt expert contributions, develop proposed rubrics and coverage, and investigate improvements. Your team can reuse the reasoning and standards in later benchmarks, training datasets, and harness experiments, with expert work directed toward what changes or remains unresolved.
Teammately can work alongside your tracing, evaluation, and development tools. It gives your AI team a system for organizing expert elicitation, designing benchmark representation, and connecting that work to offline training and optimization.
For teams assembling this process with scripts and coding agents, Teammately’s agents undertake the recurring work of planning expert contributions, interpreting responses, and developing findings into rubrics and coverage. The initial scope defines how your existing tools and materials connect.
Human evaluation supplies judgments about particular outputs or trajectories. Teammately’s agents also investigate the reasoning and conditions behind those judgments, adapting reviews, questions, and interviews as experts respond.
Findings become proposed rubrics, policies, and coverage, while unresolved distinctions guide further expert work. Your experts contribute judgment; agents undertake the preparation and interpretation needed to apply it across AI development.
Teammately ingests production interactions to examine where actual usage diverges from your benchmark’s planned representation. Emerging intents, conditions, and combinations inform coverage updates across ontologies and the construction of new cases and worlds.
These updates guide subsequent offline evaluation and improvement, keeping the benchmark connected to the work your AI encounters. Coverage also preserves rare, consequential situations that recent traffic may not contain.
Your AI team operates the platform and directs the development work. Agents undertake elicitation, construction, and experimentation; domain experts contribute judgment and approve proposed standards. Engineers decide which changes to adopt.
Bring a development objective and the materials you already have. You can begin with a new initiative, a training requirement, or an existing system; a finished benchmark is not required.
Together, we select a useful first output for your objective: reviewed rubrics and coverage, a benchmark, targeted training data, or a tested harness prototype. The choice follows your development needs and the materials you already have.
We define the expert participation, integrations, and execution or training environment the work requires. Responsibilities, usage assumptions, compute costs, and the evidence needed to assess progress are agreed as part of that scope. Review the pricing structure.
Built around your judgment
Share your development objective and what you have today. We’ll work out where Teammately can contribute, what a useful first result would be, and the expert participation, coverage, and integration it requires.
