Useful intelligence. Real workflows.
An AI feature needs a clear purpose inside the product around it. We work on LLM tooling, retrieval pipelines, and workflow agents with evaluation considered as part of the engineering.
LLM features and tooling
Language-model features connected to a specific product task. We define the expected behavior, input boundaries, and surrounding interface before treating the model as a useful part of the system.
Retrieval pipelines
Retrieval-backed workflows that connect a model with relevant information. The work includes deciding which sources belong in the workflow and how the resulting output should be assessed.
Workflow agents and evaluation
Agents wired into real workflows, with evaluation harnesses used to examine behavior. We consider the system around the model, including permissions, failure cases, and how results reach the user.
How we work.
We start with the workflow and what a useful result would mean. A focused implementation helps test that behavior before broader integration. Evaluation and operational constraints stay part of the development process rather than being left until the end.
A good fit.
For teams exploring a specific AI-assisted workflow, adding retrieval to a product, or moving an existing model experiment toward an evaluated software feature.