AI as infrastructure. Not as a marketing slide.
We integrate AI where it pays back: support deflection, lead qualification, content systems, internal ops. Measurable, not theatrical.
Most AI initiatives die in pilot.
The use case was thin, the data was not ready, or the success metric was never agreed. A team launches a chatbot, runs it for six weeks, cannot point to a number that moved, and quietly retires it. Meanwhile the genuinely high-value applications (revenue ops, support deflection, internal knowledge) sit untouched because nobody wanted to do the unglamorous work.
Pick the use cases that pay back. Ship to production. Measure.
We start with a short discovery: where is the team spending hours on tasks an AI system could handle, and what is the dollar value of automating them? Then we ship to production with proper evaluations, monitoring, and a fallback for when the model is wrong. AI is not a shiny object here. It is a colleague that needs a job description and a performance review.
Four pillars, one engagement.
Every engagement is shaped to your business. The pillars below are where we focus when we run ai implementation.
Use-case discovery & ROI
A two-week assessment of where AI pays back in your business. Output is a ranked backlog with dollar-value estimates, not a 60-page strategy deck.
Pilot to production engineering
Real implementations in Anthropic Claude or OpenAI, with retrieval, tool use, and evals. Production-grade, not demo-grade.
Evaluations + monitoring
Every AI feature ships with offline evals, online sampling, and drift monitoring. You can answer the only question that matters: is the model doing the job?
Data infrastructure
Most AI projects fail because the data layer is not ready. We fix the underlying ingestion, retrieval, and quality pipeline so the model has something to stand on.
Realistic ranges, not promises.
Indicative ranges based on similar engagements. Actual outcomes depend on your starting point, scope, and how aggressively the team can ship the changes the audit recommends.
They did not start with the model. They started with our worst process. Six weeks later it was 80% automated and the team had time back.
COO · scale-up B2B SaaS
See the audit before you commit.
A 30-minute call. We run a live audit of your site against the metrics that matter for ai implementation.