We design retrieval and fine-tuning systems that cut inference cost and improve reliability — for teams shipping AI at scale.
Windows grow unchecked while token costs climb, with no clear return in accuracy.
Off-the-shelf vector-store templates ignore your data's actual structure and retrieval needs.
Teams ship prompting systems without measuring what each token actually costs them.
Without evaluation baselines, quality erodes silently as usage and data scale.
Pulling the right signals from your source systems, not everything they contain.
Retrieval systems designed around your data's actual structure, not a default template.
Targeted training that replaces bloated prompts with models that know your domain.
Full visibility into what every token costs, before and after every optimization.
Drops into your existing stack — any framework, any model provider.
Governance and evaluation frameworks for teams moving from pilot to production.
We instrument your existing pipeline and map every source feeding the model — measuring where tokens, latency, and accuracy are lost.
We redesign retrieval, prompting, and training strategy around the findings — grounded in evaluation, not guesswork.
We tune, benchmark, and hand off a system your team can maintain, with clear cost and performance baselines.
Shipping AI features into production products.
Owning infra cost, reliability, and scale.
Balancing quality, speed, and unit economics.
CTOs and VPs accountable for AI spend and risk.
We start with an audit of your existing pipeline, not a product demo. Every recommendation is tied to a measured baseline — token counts, latency, and retrieval accuracy — captured before and after. No case studies with invented numbers, no anonymous testimonials: just the methodology, applied to your system.