Teammately provides AI alignment infrastructure that helps enterprises embed their domain expertise into how their AI systems learn and improve. The platform uses an AI agent called Lemon to elicit expert judgment, organize it into reusable rubrics and policies, and apply those standards to benchmark construction, harness development, and model training. This enables organizations to translate their specialized knowledge into measurable, ongoing improvements in AI behavior.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to translate their domain expertise into concrete guidance for AI development. The difficult work of uncovering relevant judgment, preserving its conditions, and making it usable in development often blocks progress, as experts cannot easily express what their AI should learn and engineers lack the structured format to apply that knowledge effectively.
Solution
Teammately provides an AI alignment infrastructure platform where an AI agent named Lemon leads adaptive expert elicitation, preparing and adapting sessions to uncover the criteria, exceptions, and preferences behind expert judgment. The platform organizes rubrics by policies, carries judgments beyond individual reviews, and controls what benchmarks represent through coverage across ontologies. Lemon Weave synthesizes cases and worlds from ontology tuples, while Trialground provides managed harness execution and rubric evaluation for offline experiments. The platform supports both harness improvements and targeted training-data augmentation, keeping gains, regressions, and unresolved judgments visible throughout development.
Target Audience
Primary customers are enterprise AI teams and generative AI engineers who need to bring business context and domain knowledge into AI development processes, particularly those building systems where expert judgment shapes correctness standards.
Features
- Lemon, an AI agent that prepares and adapts expert sessions to uncover criteria, exceptions, and preferences behind judgments
- Rubrics organized by policies that express requirements, preferences, and applicability with continuity across development iterations
- Coverage across ontologies that makes intended situations and proportions explicit for benchmark construction
- Lemon Weave synthesizes cases and worlds from ontology tuples, including supporting documents, workbooks, and code artifacts
- Trialground provides managed harness execution and rubric evaluation for offline experiments
- Lemon Code supports targeted training-data augmentation for weights improvement
- Research-backed approach studying how human intent transfers into model behavior across prompts, harnesses, skills, and weights