Methodology

Vertical AI

Most enterprise AI work is built on horizontal foundations: generic models, generic prompts, generic evals. Vertical AI starts somewhere different. It begins with a specific domain, a specific decision class, and a specific set of entities, sources, and judgments that matter in that domain.

We build systems where the methodology, the data, and the evaluation harness are tuned to a vertical, and where the model layer is replaceable as the field moves. The defensibility lives in the vertical stack, not in the model choice.

Controlled methodology

Frontier models are improving fast. The methodology layer compounds faster.

We invest disproportionately in prompt patterns, retrieval discipline, and structured preambles that produce consistent behavior across model families. When the model layer changes, the methodology travels. When the methodology layer changes, the lift compounds across every model the lab uses.

Controlled methodology is what lets the lab make defensible architecture decisions without betting on a specific vendor.

Evaluation harnesses

We do not deploy systems we cannot evaluate.

Every workflow the lab builds has retrieval evals, generation evals, tool-use evals, agent trajectory evals, and business-outcome metrics. Evals are scored on multiple dimensions, and variance is treated as a first-class measurement, not a footnote on the mean.

The eval harness is itself the asset. It compounds across model generations and gives the lab a defensible basis for architecture decisions.

Field reports

The lab publishes field reports on what we observe. Methodology, findings, implications. Public-safe and vendor-neutral.

The work itself stays proprietary. The patterns and principles travel.