Uncover expert judgment
Adaptive inquiries uncover the priorities, trade-offs, and exceptions behind expert decisions. Experts review and correct the interpretation before it guides development.
AI Alignment Infrastructure
Make AI alignment
an in-house capability
An agent-powered platform that puts your enterprise's expert judgment to work shaping how AI behaves and improves.
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Aligning AI with your enterprise's context and taste can build a moat against frontier models, but sustaining it requires continuous effort, and relying on external teams for every improvement puts that advantage at risk.
Teammately equips your experts and AI engineers to align and improve AI themselves, at scale.
How in-house AI alignment works
Make judgment reusable
Turn reviewed judgments into rubrics and policies that preserve what matters, when it applies, and where questions remain unresolved. Your team approves shared standards.
Control what your AI is tested against
Choose the situations and conditions your AI must handle. Agents develop coverage and construct cases and worlds around those priorities, including consequential edge cases.
Engineer and test improvements
Use reviewed expectations to guide harness changes and targeted training material. Agents investigate candidates while your engineers inspect evaluation results and regressions before deciding what to adopt.
“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.”
Whitepaper: AI Alignment Infrastructure
A practical guide to connecting expert judgment, coverage, and AI improvement, with worked examples, team responsibilities, and guidance for a first assignment.
Read the whitepaper
Meet Lemon,
your AI agent for alignment.
Lemon undertakes the preparation, inquiry, construction, and investigation behind AI alignment. Your team reviews its proposals, examines the results, and decides what moves forward.
Get to know LemonLemon at work
Preparing coverage
Proposed dimensions and ontologies, with the reasoning behind them.
Learning from experts
Focused follow-ups and proposed rubrics for expert review.
Pursuing benchmark goals
Candidate harnesses and the evaluation results behind them.
Powerful AI is within everyone’s reach. Build your advantage beyond the model. Teammately makes your expertise the difference.
Specialty Retailers
When relevance alone does not make a good recommendation.
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.”
Business Software Companies
Acceptance standards for customer-specific AI work
Maintain acceptance standards across customer workflows as software changes. Assess product AI and internal customer-insight analysis, then develop better candidates.
“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.”
Travel Booking Platforms
Service judgment when standard booking flows stop resolving cases
Develop post-booking AI around specialist service judgment, supplier responsibilities, and useful handoffs. Construct exception coverage and assess existing responses.
“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.”
Airlines
Resolving complex passenger cases when disruption changes the options.
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.
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Vehicle & Equipment Manufacturers
Owner-support quality across product variants
Develop owner-facing AI around useful interpretation, clarification, and next-step guidance. Construct multimodal assessment cases across vehicle and product variants.
Read solution“Teammately is addressing many of the problems we’re trying to solve. I can see how our organization could benefit.”
Work with us: Forward Alignment
Put your expertise to work on a difficult AI behavior.
Work with Teammately’s builders and a Forward Alignment Scientist to define clear standards, build a useful benchmark, and test a change. Keep the development assets and the capability to use them.
We study how human intent becomes reliable AI behavior, and how AI agents can help that process scale.
Our research explores structured expert supervision, behavioral evaluation, and autonomous experimentation to understand what makes alignment effective, repeatable, and increasingly independent of manual engineering.
Where Teammately fits.
What starting involves.
How does Teammately compare with Scale, Surge, and Mercor?
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.
How does Teammately fit alongside LangSmith, Braintrust, or Langfuse?
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.
How does Teammately handle ambiguous or conflicting expert judgments?
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.
Do we retain ownership of what we build with Teammately?
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.
Who operates Teammately, and where do we start?
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.
Build AI that meets your standards.
Tell us about the challenges you’re facing with your AI. We’ll show you how Teammately can help.