AI Consulting in Seattle

Both, depending on what the company already runs. Most Seattle-area tech companies are deep in approved cloud — model access, Lambda, secure object storage, Secrets Manager — and it usually makes more sense to build inside that perimeter than to introduce a competing vendor stack. When the existing architecture already uses model access for model access or has Lambda functions handling internal automation, we wire into those rather than layering something parallel on top. For companies that are approved cloud-agnostic or running multi-cloud, we typically use whichever model endpoint gives the best performance for the specific workload and keep the integration layer portable. The decision is always driven by what reduces operational overhead for your team after the build ships, not by a preference for one cloud over another.

your identity provider is the most common identity layer we work with in the Seattle market, and the integration pattern is well-established. We use OAuth 2.0 with your identity provider application registrations, scoped to minimum required permissions — usually read access to specific document repository libraries, video meeting channels, or Outlook mailboxes, not blanket Graph API admin consent. If the company's conditional access policies restrict third-party app registrations, we walk through that with your IT team before any credential exchange happens. For AI builds that need to surface internal knowledge from document repository or OneDrive, we index through the document API API using the existing AD permissions model, so the tool respects the same folder-level and sensitivity-label access controls already in place. Users can only retrieve content they already have permission to see, which matters a lot for companies where HR or legal documents live in the same tenant as engineering documentation.

Yes, and the ITAR requirement shapes the architecture from day one, not as an afterthought. Export-controlled technical data cannot be processed by AI processing endpoints where foreign nationals have access to the infrastructure, which rules out certain commercial API configurations. For ITAR-scoped builds, we either use U.S.-based enterprise endpoints with contractual data residency guarantees and verified access controls, or we deploy an on-premises model layer inside your network perimeter where you control who touches the hardware. Access controls at the knowledge-assistant level are enforced to mirror your existing ITAR access list — the tool cannot return export-controlled content to a user who isn't on that list regardless of how the query is phrased. We document the data flow, the access control implementation, and the model hosting configuration in writing before go-live, formatted for your ITAR compliance officer or legal team to review. We're not attorneys and don't provide legal opinions, but we can build the technical controls to match whatever your compliance program requires.

The distinction we draw is between documentation work and clinical or scientific judgment. There's a large surface area of biotech work that is documentation-intensive but not itself a clinical decision: drafting IRB submission narratives from existing protocol data, summarizing prior versions of an SOP to identify what changed, generating first-draft grant progress reports from lab notes and publication lists, or indexing a regulatory submission package so a team member can quickly find the section they need. Those are the workflows we build against. We don't build tools that interpret patient data, suggest treatment paths, generate scientific conclusions, or touch anything that functions as a medical device under FDA 510(k) or De Novo definitions. If a workflow sits in that gray zone, we flag it before scoping rather than after, because building the wrong thing in a regulated environment creates liability that no automation gain is worth.

Most companies start with the $99 AI readiness audit, which produces a written report within a few business days. The report maps the specific workflows where automation creates the most leverage given the company's current stack, headcount, and bottlenecks — not a generic framework, but a prioritized list specific to how the company actually operates. From there, if a single high-leverage workflow is clear, we scope a fixed-price build. For most Seattle-area engagements, that's two to four weeks from signed scope to a working tool in the company's environment. We don't run six-month discovery phases or require platform migrations as a prerequisite. If there's ambiguity about which workflow to attack first, a $497 Founder Review Call — ninety minutes, written prioritization memo at the end — gets that decision made before any build budget is committed.