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AI agents · Seattle, WA

AI agents built the way Seattle would build them

Seattle teams don't buy AI hype — they buy measured systems. We build support agents, internal copilots and workflow automation with evals before launch, honest failure modes, and metrics your engineers will respect.

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Engineering-rigor AI, not demo-rigor AI

The gap between an AI demo and an AI system is everything Seattle engineers already know: evals, guardrails, observability, cost budgets, and a plan for when the model is wrong. That's how we build. Before launch, your agent passes an evaluation suite built from your real tickets and questions. After launch, every conversation is logged, sampled and scored — with accuracy reported weekly, not asserted.

What we deploy here: support agents trained on your docs and past tickets that resolve the routine majority and escalate with full context; internal copilots that answer employee questions from your wiki, codebase docs and runbooks; and workflow agents that handle the operational glue — triage, routing, summarization, data movement between your systems.

  • Support agents: docs-trained, eval-tested, escalation-aware
  • Internal copilots over your wiki, runbooks and knowledge base
  • Workflow agents: triage, routing, summarization, system-to-system glue
  • Eval suites built from your real data before launch
  • Full observability: logs, accuracy sampling, cost/latency dashboards
  • Human-in-the-loop for consequential actions — permanently, where it matters

Your reviewers are welcome

Send your engineers to our architecture review — Seattle teams that read our design docs are usually the ones who sign. We deploy into your cloud boundary when required, don't train shared models on your data, implement retrieval with proper access controls (the agent only sees what the asking user may see), and document every failure mode honestly.

Economics: fixed USD build pricing, monthly run costs tied to actual usage with budgets and alerts, and a W-8BEN for cross-border accounting. We're three hours ahead (Toronto) — an async-friendly offset that Seattle's distributed-work culture takes to naturally.

Deployment cadence

Week one: use-case selection and data audit — we pick the agent with the clearest measurable outcome. Weeks two through five: build, author the eval suite from your real data, and iterate until it passes. Shadow deployment, then supervised production with weekly accuracy and cost reports.

Expansion follows evidence: support agent first (fastest measurable deflection), then internal copilot, then workflow automation. Each scoped and priced fixed; each instrumented so the ROI case writes itself.

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AI Agents in Seattle — common questions

An eval suite built from your real tickets and questions, run before launch and continuously after — resolution rate, accuracy on sampled conversations, escalation appropriateness, and cost per resolution. You get the numbers weekly; we don't grade our own homework privately.

US$10k–25k to build depending on integrations and eval scope; US$400–1,500/month to run with usage budgets and alerts. Fixed quotes — no per-seat pricing that punishes adoption.

Yes — retrieval is permission-aware: the agent answers from what the asking user is allowed to see, nothing more. That's an architecture requirement we implement, not a policy promise.

We choose per use-case (quality, latency, cost) across current frontier models, behind an abstraction layer. You're not locked to a vendor — and when a better model ships, the eval suite tells us whether switching is actually an improvement.

Async-first: your end-of-day feedback is next-morning progress, with live overlap 9–noon Pacific. USD invoices, W-8BEN, contracts under US-friendly terms. Operationally a domestic remote team; economically a better one.