AI & Machine Learning Company in Seattle

Three delivery shapes drive the range: a scoped AI proof of concept at Seattle rates, a custom production machine learning model (forecasting, recommendation, document extraction, fraud or anomaly detection), or full production AI systems with retrieval augmented generation, multiple models, fine-tuning, and enterprise integrations. Scope drivers include data audit, a baseline model, evaluation harness, and a hosted demo. Seattle rates sit above Portland and Phoenix because of the Microsoft, Amazon, and AI2 talent premium, but typically below San Francisco for equivalent depth. We give fixed-fee proposals rather than open time and materials estimates.

Washington's My Health My Data Act, effective March 2024, is the strictest US consumer health-data law in force and applies far beyond HIPAA covered entities. For AI projects touching any consumer health data linked to Washington residents (wellness apps, fitness trackers, mental health platforms, reproductive health tools) we build consent capture into the data ingestion layer, geofence training and inference inside AWS us-west-2 or Azure West US 2 with explicit no-egress controls, store model inputs and outputs in immutable audit logs (S3 Object Lock or Azure Blob immutable storage), and implement thirty-day data subject access and deletion workflows that propagate to training datasets and vector stores. A private right of action and Attorney General enforcement mean retrofit is expensive, so we design these controls into the pipeline on day one rather than bolting them on before launch.

Yes. Most Seattle enterprise AI work we ship runs on Azure OpenAI Service (for Microsoft-aligned clients with existing Azure footprints and Microsoft Enterprise Agreements) or AWS Bedrock (for AWS-native clients in us-west-2). Azure OpenAI engagements include private endpoint configuration, customer-managed keys, content filter tuning, abuse monitoring opt-out where the Microsoft Responsible AI review approves it, and integration with Microsoft Purview for data governance. AWS Bedrock engagements use Anthropic Claude, Amazon Nova, Meta Llama, or Cohere via VPC endpoints with model invocation logging into a dedicated audit account. Both platforms support Knowledge Bases, Guardrails, and Agents primitives, and we will recommend the right primitive (custom orchestration versus managed agent) based on your evaluation harness results, not vendor marketing.

When a project requires genuine research (novel model architectures, rare-domain reasoning, multimodal fusion, robust evaluation methodology), we scope collaborations with AI2 affiliates or University of Washington Allen School graduate labs rather than overselling in-house capability. Our core team handles applied engineering, MLOps, and productionisation, which is where most Seattle AI projects actually stall after the demo lands. For standard work (retrieval augmented generation, fine-tuning on documented architectures, classical machine learning, computer vision on known backbones) no academic partner is needed. We use AI2's open-weight OLMo models, Semantic Scholar API, and published Aristo and Tulu work directly in client builds where appropriate, and we will tell you up front which bucket your problem fits into.

Most Seattle enterprises (anchored by Microsoft's published Responsible AI Standard and Amazon's Responsible AI practices) already expect a defined documentation package. We deliver a Responsible AI Impact Assessment modeled on Microsoft's template, a model card covering intended use, training data provenance, evaluation results, and known limitations, a transparency note suitable for end-user disclosure, a fairness evaluation across protected attributes using Fairlearn and Microsoft's Responsible AI Toolbox, a red-team evaluation report for generative systems, and a monitoring plan with drift detection and incident response procedures. The package aligns with NIST AI RMF and is structured to satisfy internal Responsible AI review boards at Microsoft, Amazon, Boeing, and Bellevue SaaS firms without a rewrite. For health and defense clients we add FDA SaMD or ITAR-specific addenda.

A typical Seattle SaaS AI feature (in-product semantic search, smart summarization, document extraction, anomaly detection, conversational assistant) takes twelve to twenty weeks from kickoff to production behind a feature flag, assuming clean historical data and a defined evaluation rubric in scope. Week 1 to 3 is data audit, evaluation harness design, and baseline. Week 4 to 10 is modelling, prompt engineering or fine-tuning iteration, and red-team evaluation. Week 11 to 16 is MLOps, monitoring, hallucination evaluation, shadow mode deployment, and Responsible AI review. Week 17 onward is gradual rollout via feature flags with continuous evaluation. If you are pre-data or need labelling, add four to eight weeks. We have shipped to this cadence inside AWS us-west-2 and Azure West US 2.

Yes. For Boeing supply chain, Microsoft Federal, and other defense-adjacent Seattle clients we ship AI systems that respect ITAR and EAR controls, typically self-hosted on AWS GovCloud (US) or Azure Government with no commercial frontier API access. Open-weight models (AI2 OLMo, Llama 3.1, Mistral) run on dedicated GPU instances with FIPS 140-2 validated cryptography, customer-managed keys, and audit logging into a dedicated security account. CMMC 2.0 Level 2 controls apply where the contract requires, with a System Security Plan aligned to NIST SP 800-171. Training data classification is handled at ingestion with explicit controlled-data tagging, and model artifacts inherit the same classification through the lifecycle. This work is heavier than commercial AI engagements and is scoped accordingly with dedicated cleared engineering capacity where required.

Yes. For Microsoft-aligned clients we ship Copilot Studio agents, Microsoft 365 Copilot extensions via declarative agents and connectors, Power Platform AI Builder flows, and Azure OpenAI integrations into Dynamics 365 and Power BI. This is a deep specialization given Redmond's gravitational pull on the local enterprise market. Engagements include grounding on Microsoft Graph data with proper permission scoping, content moderation via Azure AI Content Safety, evaluation harnesses that run before each deployment, and Responsible AI documentation aligned with Microsoft's published standard. We have shipped to this pattern for Bellevue enterprise clients embedding Copilot extensions inside their internal Microsoft 365 tenants, and we will tell you when a custom application outperforms a Copilot extension and when Copilot extensibility is the right answer.