LLM Integration Company in Seattle

It comes down to which of these you need: a scoped RAG copilot pilot for a Seattle enterprise, a production LLM integration for Boeing engineering documentation, Starbucks operations, Costco member services, or Expedia revenue management, or enterprise-wide deployments for Fortune 500 Seattle headquarters with multi-tenant routing, Washington Privacy Act and MHMD-aligned governance, and SOC 2 Type II audit support. Scope drivers include data audit, a baseline retrieval pipeline on Anthropic Claude (Bedrock us-west-2) or Azure OpenAI (West US 2), an evaluation harness, and a hosted demo. We quote fixed-fee against signed scope, not open T and M, so your CFO knows the number before the project starts.

The My Health My Data Act took effect for most entities on March 31, 2024 and is the strictest US state consumer health privacy law. It applies far more broadly than HIPAA — any business processing 'consumer health data' (including inferences, biometric data, wellness app data, fertility data, fitness data) of Washington residents falls in scope, regardless of HIPAA covered-entity status. For any Seattle LLM project we run an MHMD classification during discovery, scope opt-in consent flows for collection and processing of consumer health data, prohibit sale of consumer health data without explicit consent, and document a Consumer Health Data Privacy Policy distinct from the general privacy policy. Geofence restrictions apply within 2,000 feet of healthcare facilities. Washington Attorney General enforcement has been active since 2024, and we have shipped MHMD-compliant pipelines that survive an inquiry without rework.

Yes — both platforms are headquartered in the Seattle metro, and AWS us-west-2 (Oregon) and Azure West US 2 (Quincy, Washington) are the home regions. AWS Bedrock us-west-2 carries Claude Sonnet, Opus, and Haiku, Titan, Llama 3, Mistral, Cohere Command R+, and AI21 Jamba with sub-15ms latency to South Lake Union. Azure OpenAI West US 2 carries GPT-4o, GPT-4.1, o1, o3, and o4-mini with the Microsoft enterprise data-handling agreement, including the commitment that prompts and completions are not used to train OpenAI models. For Seattle clients we typically route across both — Claude for long-context document work and reasoning-heavy tool-use, GPT-4o or o-series for vision and structured output — with a thin routing layer that picks per task on accuracy, cost, and latency.

Yes, when reproducibility, transparency, or research provenance matters. AI2's Olmo 2 and Tulu 3 are some of the most open language model releases on earth — full training data, training code, intermediate checkpoints, and evaluation suites are public. For UW research clients, regulated health-tech work where every model decision must be auditable from training data forward, and publication-bound academic work, Olmo and Tulu are genuinely the right choice over closed APIs. We self-host on AWS us-west-2 GPU instances, fine-tune for the client task, and ship with the model card and reproducibility documentation AI2 provides. For standard enterprise work where capability ceilings matter more than provenance, closed APIs (Claude, GPT-4o, Gemini) remain the default — we will tell you up front which bucket your problem fits into.

Yes, scoped carefully. Boeing commercial aircraft work routinely involves export-controlled technical data under EAR (the 737, 777, 787 programs include components and documentation that fall under various Export Control Classification Numbers), FAA-regulated quality management under 14 CFR Part 21 and the Boeing Quality Management System, and supply-chain sensitivity across Spirit AeroSystems and the global tier-1 supplier base. We staff personnel with appropriate access where required, segment export-controlled tiers across AWS GovCloud (US-West) for restricted workloads and commercial AWS or Azure for non-restricted plumbing, and produce the technical artefacts your empowered official and export-compliance counsel actually need. Final EAR, ITAR, and quality-system responsibility sits with the contracting officer and Boeing's compliance organisation — we provide the engineering compartmentalisation, not a legal opinion.

Seattle retail clients face an unusually layered consumer-data regime — Washington Privacy Act (no general consumer privacy law yet but MHMD applies to health data), California Consumer Privacy Act and CPRA for the substantial California customer base, the FTC Act on unfair and deceptive practices, and brand-specific membership terms (Costco's member agreement, Nordstrom's loyalty terms, REI Co-op's member-owner framework). For LLM-driven personalisation, customer service, and merchandising copilots, we keep raw consumer data inside the client's existing customer data platform (mParticle, Segment, Snowflake, Databricks), tokenise PII before any model call, log every inference for a documented retention period, and run consent and preference checks at the edge. Brand legal review on LLM-generated consumer-facing copy happens before launch, not after a regulator inquiry.

A typical Seattle production LLM integration — a RAG copilot for Boeing engineering documentation, a customer-service copilot for Starbucks or Costco, a revenue-management LLM for Expedia or Zillow — takes twelve to twenty weeks from kickoff to production. Week one to four is data audit, ingestion pipeline, and baseline retrieval. Week five to ten is iteration on chunking, embedding, reranking, and prompt design, with an evaluation harness scoring every change. Week eleven to fourteen is integration into Salesforce, ServiceNow, SAP, Snowflake, or a proprietary stack. Week fifteen onward is shadow mode, A/B rollout, and audit documentation for Washington Privacy Act, MHMD, SOC 2, or EAR as applicable. Self-hosted Llama 3 or Olmo 2 fine-tuning adds four to eight weeks. We have shipped to this cadence inside Fortune 500 Seattle environments.

When a project requires genuine research — novel reasoning architectures, rare-event detection, alignment work beyond current state-of-the-art, sparse retrieval at scale — we scope collaborations with UW's Paul G. Allen School of Computer Science and Engineering, the UW Medicine clinical AI groups, or AI2-affiliated researchers. UW's NLP and ML research groups feed Amazon, Microsoft Research, and AI2 directly, and the talent density inside a one-mile radius of South Lake Union and the UW campus is unmatched outside the Bay Area. Our core team handles applied engineering, RAG pipeline design, evaluation harness construction, and productionisation, which is where most Seattle LLM projects actually stall. For standard work we do not bring an academic partner in — it adds cost and timeline without proportional value.