Applied AI & agent infrastructure
Model capability depends on the system it operates in. A search agent needs evidence it can locate and verify; an operational agent needs tools that express valid actions. We build that environment, connect it to enterprise systems, and make complete task trajectories available for evaluation and post-training.
Retrieval supplies current evidence. Typed tools, permissions and durable state determine which changes can be made and how interrupted work resumes.
Tool design changes what a model can learn. An overloaded API, incomplete search result, or ambiguous error can turn a simple task into an unreliable sequence. We design retrieval and tool interfaces around the decisions the model must make, and use the same contracts in evaluation, training environments, and production.
Each intervention has a purpose, a record of what changed, and a way to assess its effect.
Parse and index authorized sources with stable references. Evaluate hybrid retrieval and reranking against real questions, and enforce document permissions before content enters the model context.
Retrieval pipeline and evidence referencesExpose focused tools with typed arguments, useful error responses, and scoped credentials. Enforce validation and approval in application code, including the exact action a person has authorized.
Tool interfaces and execution policyPersist task state and record model actions, tool results, and final outcomes. Reconcile uncertain writes before retrying; turn reproducible failures into environment tasks and regression cases.
Task trajectories and recovery proceduresWe set the evaluation around the application’s requirements, including the failures an aggregate score can hide.
The source and retrieval design, tool contracts, access model, and task environment used to evaluate the system.
Integrated model endpoints, agent execution, and the application or conversational interface required by the workflow.
End-to-end task tests, model and tool traces, measurable completion criteria, and operating procedures for partial failure.
Yes. We work with existing checkpoints, private endpoints, and approved providers. The initial task evaluation shows which gaps belong in retrieval or tool design and which may justify model adaptation.
Yes. This includes enterprise search, document processing, tool-using agents, and conversational systems. We design their context and execution environment together with task evaluations, so model training can build on a well-defined system.
Work with the lab
Tell us where the model falls short and what better performance would mean for your team.
