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AI automation · Seattle, WA

AI automation built to Seattle's engineering bar

Seattle teams know the difference between a demo and a system. We build internal automation — data pipelines, support ops, workflow glue — with typed code, tests, observability and cost budgets your engineers can review.

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The internal work nobody staffs

Every Seattle company has the same backlog: the support-ops glue between the helpdesk and the product, the data pipeline half-built in someone's spare time, the internal tool that would save every team an hour a day, the integration debt from acquisitions and pivots. It never wins prioritization against the roadmap — so it never happens, and the manual tax compounds.

We're the team that builds it: pipeline and ETL work with proper testing and alerting; support-ops automation (triage, dedupe, macro-drafting, escalation routing); internal tools on your stack with your auth; and the AI layer where it earns its place — classification, extraction, summarization — with evals and cost budgets attached, not vibes.

  • Data pipelines and ETL with tests, alerting and lineage docs
  • Support-ops automation: triage, dedupe, drafting, routing
  • Internal tools on your stack, behind your auth, in your cloud
  • Integration debt cleanup: the systems that were never properly joined
  • AI classification/extraction/summarization with evals and budgets
  • Documentation and handover your team can actually maintain

Reviewable by your engineers

We work in your repos or hand over clean ones — typed code, tests, CI, infrastructure as code, runbooks. Your engineers review PRs if they want; the ones who do tend to become our champions. AI components ship with eval suites, cost/latency budgets, and honest failure-mode documentation.

Commercials: fixed USD pricing, W-8BEN, async-first workflow three hours ahead (your EOD feedback is next-morning progress). No per-seat rent — you own the system, and we hand over the keys with documentation that makes that real.

Engagement shape

Discovery is technical: architecture, data access, constraints. Builds run three to ten weeks depending on scope, with staged delivery and demos your team can poke at. Definition of done includes tests passing, dashboards live, and runbooks written — not just "it worked in the demo."

Seattle clients typically start with one painful pipeline or support-ops workflow, then fund the backlog from the measured hours returned. The automation pays; the engineers get their afternoons back.

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AI Automation in Seattle — common questions

That's the definition of done: typed code, tests, CI, IaC, runbooks and a handover session. We build in your idioms on your stack — a system your team owns, not a black box you rent.

US$10k–35k per system depending on integration depth and AI components. Fixed quotes after a technical discovery; the payback metric (hours, errors, latency) is defined before we start.

Deployment in your cloud boundary, your auth, your access controls, no shared-model training on your data, and precise questionnaire answers. AI retrieval is permission-aware — users only get answers from what they may see.

Budgeted and observable: per-feature cost dashboards, caching and model-tiering where it's safe, and alerts before spend surprises. The eval suite tells us when a cheaper model is genuinely good enough.

Same discipline, same stack fluency, a third lower rates, async-friendly offset, USD invoices and a W-8BEN. Seattle teams that review our code are usually the ones who expand the engagement.