Our approach
Scaling enterprise expertise for AI development
How AI-driven elicitation, governed rubrics, and deliberate coverage make expert participation a sustained part of developing weights and harnesses.
An enterprise can know its work deeply and still struggle to express what its AI should learn. Knowledge is distributed across people and teams. Preferences depend on context. Exceptions often become visible only when someone encounters a decision that feels wrong.
Bringing that expertise into AI development requires preparation, interpretation, and follow-through. Someone must decide what to ask, preserve the conditions behind an answer, and determine what it changes in a benchmark, a harness, or a training dataset. Repeating that work manually makes expert participation difficult to scale.
Teammately’s approach is to give AI agents responsibility for substantial parts of that work, within objectives and permissions set by your team. Expert contributions inform policies, rubrics, and coverage. Experiments put those foundations to use and reveal where further work is needed. We call the system supporting this ongoing work AI Alignment Infrastructure.
The work around expert judgment
A useful expert session begins with a learning objective. Some questions call for correcting an individual response. Others require comparing alternatives, examining a trajectory, or discussing the reasoning behind a decision. The format should serve the judgment being elicited.
Teammately’s agents plan and conduct case reviews, forms, chats, and voice interviews. Reviews support curation, pairwise and listwise comparison, and trajectory assessment. Adaptive sessions use expert responses to refine the inquiry, clarify a condition, or pursue a disagreement.
The division of responsibility matters. Agents prepare materials, organize the session, and develop the findings into proposed standards. Experts contribute domain knowledge and preferences; authorized people decide which standards represent the organization. Greater automation makes that judgment easier to obtain and apply while preserving human authority over it.
Rubrics that teams can govern
An expert’s preference is useful only if its meaning survives translation. A judgment may depend on the available evidence, the authority of a source, or an exception that changes the appropriate action. A rubric needs to retain the conditions relevant to its assessment.
Rubrics turn expert judgment into benchmark criteria. Teammately groups them under policies so enterprises can organize shared standards across teams, with explicit scope and precedence. Policies express requirements, preferences, and applicability; their rubrics make those expectations assessable. Agents can use intra-expert conflicts to investigate missing criteria or exceptions. Differences across teams can prompt further discussion of which expectation applies and why.
This structure provides continuity across development iterations. Teams can revisit the relevant policy and rubrics when requirements change, inspect which standards an evaluation uses, and build on previously established judgments. Reuse still requires checking that the standard applies to the new work.
Representation by design
A benchmark makes a choice about what to represent. Its mix of tasks, conditions, and combinations determines which conclusions its results can support. Dataset size and difficulty alone leave that choice unexplained.
Coverage engineering makes the intended representation explicit across ontologies. Coverage targets and combinations guide which cases to construct and how many to include. Your team can examine the case mix against the benchmark’s purpose, identify missing conditions, and decide where deliberate emphasis is needed.
That purpose matters when setting proportions. A benchmark intended to reflect routine use may need a different distribution from one designed to investigate rare, consequential failures. A difficult challenge set can be valuable without representing the frequency of events in production. Its interpretation should reflect the coverage it was designed to provide.
Weave synthesizes challenging cases, response variants, and supporting materials from ontology tuples within that coverage design. World synthesis extends the construction to agent environments, where tools, state, and context influence the behavior under examination. The resulting cases and worlds still need review for their intended coverage and relevance. Production interactions can then reveal new intents and changing conditions for subsequent benchmark updates.
Evidence that directs development
Trialground provides managed harness execution and rubric evaluation for offline experiments. Cases establish what the candidate encounters; applicable rubrics establish how its behavior is assessed. The evidence supports investigation of responses and trajectories, including failures that an aggregate score can obscure.
Coevolve is an AI agent purpose-built to develop and optimize harness prototypes in a sandbox. It pursues a benchmark goal through investigation and competing experiments, giving engineers and coding agents tested ideas to carry forward. Weave provides the separate route to weights improvement through targeted training-data augmentation. The training environment and compute are established for that work.
A failed trial does not by itself identify the cause. It may reveal a harness problem, missing context, a gap in the training data, or a preference the experts have not resolved. Findings can therefore direct a candidate change, additional construction, or another expert contribution. These connections let the program respond to what it learns.
Start from the work you have
The entry point follows your development objective and existing assets. A team with a working benchmark may begin by examining representation. A team preparing training data may need expert preferences and suitable comparisons. A team with a candidate harness may begin with offline trials and use the findings to identify the next work.
These starting points can use the same foundations without adopting a fixed sequence. Scope the initial work around the behavior you want to develop, the experts qualified to judge it, and the evidence needed for a decision. Identify the materials and integrations already available, along with the work agents may undertake and the decisions your team retains.
Assess the value of the system
A useful assessment should connect operational effort to development outcomes. Establish a baseline for a defined scope, then examine what the infrastructure changes:
- Preparation and coordination
- Track the work engineers and program owners spend preparing contributions, translating findings, and organizing experiments. Include review and correction of agent work.
- Expert participation
- Examine what expert time resolves: clarified preferences, settled conditions, or actionable disagreement. Count the effort required as well as the contributions completed.
- Representation
- Compare intended and constructed coverage. Inspect missing combinations, the proportions of cases, and whether difficult cases and agent worlds exercise the intended behavior.
- Improvement
- Compare candidates under defined conditions, retaining independent assessment where appropriate. Examine regressions and distinguish changes to rubrics or coverage from changes to the AI.
Agree on these criteria before the work begins so the scaling claim can be examined within your development program.
The capability an enterprise is buying is a way to operate alignment around its own expertise: agents undertake preparation and development work, experts establish judgment, and the system connects that judgment to coverage and improvement. Its value depends on whether that arrangement lets your team develop weights and harnesses with less repeated effort and better evidence.