AI consulting - San Francisco

AI consulting in San Francisco

Nobody in San Francisco needs to be told what a language model is, which changes what an outside partner is actually for.

The usual consulting pitch fails here. These teams have engineers who have shipped models, read the papers and built their own tooling. What they have less often is the operational layer around the thing they built, because it is unglamorous work that competes badly for internal headcount against the next feature.

The prototype-to-production gap

A demo that works on twenty hand-picked examples and a system that holds up on ten thousand real ones are separated by work almost nobody wants to do. Error taxonomy. A held-out evaluation set with real cases including the ugly ones. Regression testing against that set on every prompt or model change. Fallback behaviour when the provider degrades. Timeout and retry policy that does not amplify an outage. Observability that lets you answer why a specific request produced a specific answer, three weeks later.

That list is the actual gap. It is also the part where an outside team can move fastest, because it is well-defined work that does not need deep product context.

Evaluation is the deliverable

Teams shipping AI features usually cannot answer "did last week's change make it better" with a number. They can answer it with an impression. Building a real evaluation harness, meaning a maintained golden dataset, a scoring method the team agrees is fair, and a run on every change, converts that impression into evidence. It is the single highest-leverage thing to build early and the thing most often deferred until something breaks publicly.

Agent operations

Once agents take actions rather than produce text, the operating questions get sharper. Who is on call when an agent misbehaves at 2am. What is the kill switch and who can pull it. How do you replay what an agent did, in order, with the inputs it saw. What is the per-tenant spend ceiling and what happens when it is hit. Most teams answer these after the first incident. Answering them before is cheaper.

Cost governance

Fast-growing AI features develop a cost curve that outruns revenue quietly. The fix is architectural more than commercial: route by difficulty, cache the stable context, trim over-broad retrieval, and track cost per completed task rather than monthly total so regressions surface in hours.

How we work with Bay Area teams

Digiton is Lisbon-based, which puts our afternoon against the San Francisco morning and gives a genuine overnight cycle on well-specified work. We build and operate production AI systems, with deployments across 8 countries, and we run our own product Parci, which analyses 308 Portuguese municipalities and returns a report in 47 seconds. An AI audit here usually looks like an evaluation and operations review rather than a strategy deck.

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