AI consulting - US
AI consulting for United States firms
The bottleneck on a US enterprise AI project is almost never the build. It is the security questionnaire, and most AI vendors are not architected to answer it.
Digiton works with US firms remotely from Lisbon, with production deployments across 8 countries. Overlap with the US East Coast covers a working morning, and West Coast teams get a same-day window in their morning. The rest of this page is about the thing that actually decides whether a US project ships.
Design for the security review before you need it
If your buyer has SOC 2, or sells to anyone who does, the architecture questions arrive before the contract. Answer them in the build:
- Where does data physically sit, and can it be pinned to a region. This is a deployment decision, not a policy statement.
- What is sent to a model provider, what is retained, and for how long. Zero retention and standard retention are different products with different model availability, so the choice has engineering consequences.
- Who can see what. Row-level security enforced in the database, so a bug in the application layer cannot leak across tenants.
- What is logged. Every model call with its inputs, retrieved context, model version and output, retained long enough to reconstruct an incident.
- Access revocation. When someone leaves, what stops working, and how fast.
Model portability is a procurement requirement
Buyers increasingly ask what happens if your model provider changes terms, raises prices or deprecates a version. The defensible answer is that the model is a swappable component behind an interface, with an evaluation set that gets rerun on any swap. That is an architecture decision made on day one and expensive to retrofit. Own the prompts, own the evaluation sets, own the retrieval index.
What US firms tend to build first
Document processing with human review, internal retrieval over policy and product documentation, and agents that handle the deterministic portion of a support or operations workflow. The pattern that consistently underperforms is a general-purpose chat assistant with no defined job, because there is no baseline to measure against and no obvious owner.
Working across the timezone gap
Remote delivery across five to eight hours works when the written record is good. Decisions recorded in writing, a shared environment the client can inspect at any hour, and a demo cadence that does not depend on both teams being awake. Where that discipline is missing, distance genuinely hurts. Where it exists, the overnight cycle is an advantage: work lands while the US team sleeps and is waiting at the start of their day.
A short AI audit maps one process against the data, the retention rules and the security review questions before any code is written.
Frequently asked questions
How does AI consulting for United States firms work remotely?
Digiton delivers from Lisbon with a working-morning overlap for US East Coast teams and a same-day morning window for the West Coast. It works when decisions are recorded in writing, the client can inspect a shared environment at any hour, and demos do not require both teams awake at once.
Can you support a SOC 2 security review?
The architecture is built to answer those questions: data region pinning, defined retention with the model provider, row-level security enforced in the database, full logging of model calls with inputs and versions, and access revocation that actually removes capability. Digiton is not itself a SOC 2 certified entity.
What happens if our model provider changes pricing or deprecates a model?
The model sits behind an interface as a swappable component, with an evaluation set that gets rerun whenever it changes. A swap becomes a config change plus a regression run rather than a rebuild. This is a day-one architecture decision and expensive to add later.
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