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.
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Lemon, Teammately’s AI agent, prepares and adapts expert sessions to uncover the criteria, exceptions, and preferences behind a judgment. Experts contribute their reasoning while Lemon undertakes 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.”
Teammately’s AI agent
Lemon undertakes the preparation, investigation, and experimentation around alignment. Follow its work, inspect its findings, and guide what happens next.
Get to know LemonProposed dimensions and ontologies, with the reasoning behind them.
Focused follow-ups and proposed rubrics for expert review.
Candidate harnesses and the evidence behind them.
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 Lemon 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.”
Teammately models
Meet Kestel and Bower. We fine-tune open-weight models for two demanding tasks: applying expert rubrics and constructing cases across planned coverage. Our objective is fair assessment and useful development material across model families.
Trialground Evaluation
Binary rubric evaluation
Assess whether candidate behavior meets each applicable criterion. Kestel is trained for binary rubric judgments, with consistent application across model families as an objective.
Lemon Weave
Coverage-directed case synthesis
Construct cases around ontology tuples and expert criteria. Bower is trained for coverage-directed synthesis, supporting benchmarks, expert elicitation, and targeted training material.
Research
Structured Supervision
We compare forms of expert supervision by the behavioral improvement they produce for equivalent human effort.
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Benchmark Science
We study how coverage, case construction, and holdout design determine what evaluation results can reliably establish.
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Behavioral Adaptation
We investigate how structured intent can improve prompts, harnesses, training data, and model weights without merely learning the test.
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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 help teams build expert-informed datasets, evaluations, and model improvement programs. Their offerings combine expert workforces, technology, and support for designing and executing these programs.
Teammately is an agent-driven software platform for making your organization’s expertise part of how your AI learns and improves. Its agents prepare review materials, organize questions and context, and adapt elicitation as experts respond. They investigate the reasoning, exceptions, and conditions behind judgments, then develop findings into proposed rubrics, policies, and benchmark coverage. Agents undertake the preparation, inquiry, and interpretation; experts contribute judgment. Your team can inspect proposed rubrics and policies alongside linked expert evidence and cases, revise them, and decide which standards to adopt.
The value compounds as those standards develop. When an expert clarifies an exception or decision boundary, that finding can guide related benchmark cases, training-data synthesis, and harness experiments. Shared policies organize how standards apply across teams, while further expert contributions focus on what has changed or remains unresolved. Teammately is especially relevant when your team wants an ongoing capability to maintain and apply its expertise throughout AI development.
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.
AI observability and evaluation platforms such as LangSmith, Braintrust, and Langfuse already help teams connect production observation with evaluation and dataset development. Teammately complements these workflows through coverage design across ontologies, helping identify where changing user behavior diverges from what your benchmark represents. Production evidence grounds that design in actual usage, while expert standards and coverage objectives guide how the benchmark’s cases, combinations, and proportions evolve. Coverage also preserves rare, consequential situations that recent traffic may not contain.
Stable benchmark versions give teams a shared basis for comparing improvements, while expert review and synthesis of neighboring scenarios help clarify decision boundaries, establish shared policies and rubrics, and determine how much each class of cases should influence the benchmark and the direction of development. Together, these capabilities support a continuous loop of production analysis, benchmark refinement, and evaluation, with data exchange scoped to your existing setup.
Different judgments can reflect missing context, competing priorities, or different expectations about where a rule applies. Teammately’s agents investigate those distinctions and prepare focused questions, comparisons, or reviews to clarify them.
Experts and accountable owners decide what should become a shared standard, where an exception applies, and what remains unresolved.
Your expertise, standards, benchmarks, and development assets remain yours.
We don’t use your workspace data to train shared models or optimize harnesses for other workspaces without your permission. See our Privacy Policy and Trust Center for more on data handling.
Teammately provides a development workspace for your AI team and a dedicated participation interface for domain experts. Your AI team works with agents to direct elicitation, review standards and coverage, and evaluate candidate improvements. Engineers decide which development changes to adopt.
Experts contribute through prepared reviews, comparisons, forms, chat, and voice interviews. Agents handle the preparation, adaptive elicitation, and interpretation, so experts can focus on the judgments and distinctions that need their expertise.
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 harness changes tested against agreed criteria. The choice follows your development needs and the materials you already have.
We agree on the development decision the first result should support and how to assess it. For example, a benchmark can establish a baseline against reviewed expert criteria, while a harness experiment can show which requirements improved, which regressed, and what remains unresolved.
We define the expert participation, integrations, and execution or training environment the work requires. Responsibilities, usage assumptions, and compute costs 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.
