Every business makes the same kinds of decisions every day - reading data, weighing it up and acting on it. We build systems that automate that process, tuned specifically to your workflow and consistent enough to trust.
AI models give different answers to the same question. Small changes to a prompt's wording can produce large changes in how the model behaves. For most consumer use cases this variation is fine. For decisions that feed business operations or regulated workflows, it isn't. A system that recommends "scale this ad" or "flag this transaction" has to be consistent before anyone can trust it.
Eval-first engineering, deterministic decision rules and full audit trails: the scaffolding that makes AI outputs consistent enough to act on.
Before any prompt ships, we score it against a test suite covering the decisions the system needs to make. A code grader checks output structure. A model grader checks reasoning quality. We iterate until the combined score meets the threshold.
Every extraction, score and decision is logged and traceable. Built for industries where accountability isn't optional.
Outputs mapped to your data model rather than left as free-form text. Every recommendation carries a confidence score and traces back to the input that produced it.
Built around your stack, not adapted from a template. We design to your data sources, existing tools and handoff requirements so the system fits how your team already works.
Whether it's a document pipeline, an ecommerce platform or something we haven't seen yet - we'd like to hear about it.