You're looking
at it.
This site runs as a single Cloudflare Worker (∼800KB of JS), built by engineers using AI orchestration — no web design agency involved. We deploy the same stack for clients.
Your keys. Your data. Your infrastructure. Requests go directly from your environment to the model — nothing routes through our systems.
We audit your workflows, build the integration, and run it in production on infrastructure you own and control — not SaaS platforms with opaque data handling.
There's a lot of AI noise right now. Vendors are promising 10x productivity from tools most businesses aren't ready to operate. We take a different position: start with your workflows, identify what's genuinely repeatable and high-value, and then build something that works inside your existing operations.
Our clients in manufacturing, healthcare, legal, and professional services aren't running experiments — they're running businesses with real SLAs, real data sensitivity requirements, and real people who need to understand what the system is doing. Every integration starts with a workflow audit and runs on your keys, your infrastructure, and your data policies.
Ryan has been building Pacific Northwest infrastructure since the late 1990s. That background in network engineering and Linux systems means we think about AI the way infrastructure engineers do: latency, failure modes, access control, and who's on call at 2am when something stops working.
// Common Questions
We hear these a lot.
Honest answers to what Pacific Northwest businesses ask before starting an AI project.
ChatGPT is a tool, not a workflow. We build the integration layer that connects AI models to your actual business data, processes, and systems — with audit logging and access controls you own. The model is one component; the plumbing is the work.
We default to BYOK deployments and support local model hosting (Ollama, llama.cpp, vLLM) for clients with strict data requirements. Your data goes from your environment directly to the model — nothing routes through our systems or gets stored on our servers.
Most failed AI projects started with a tool, not a workflow. We start with your workflow audit — mapping what's actually repeatable, high-volume, and high-cost before recommending anything to build. If AI isn't the right answer, we'll say so in the audit.
You call the same people who built it. Managed support clients have a direct line — not a ticket queue. Most AI workflow issues are resolved the same day because the engineers who respond already know every component of what you're running.
Our AI clients range from a 12-person specialty clinic to a regional manufacturer. Smaller teams typically see faster ROI — simpler workflows, faster decisions, and less organizational friction between the audit and reaching production.
The workflow audit is a fixed-fee engagement with no strings attached. Most clients choose ongoing managed support — but you own everything we build. If you want to run it yourself after deployment, we document everything and hand it off cleanly.
// Data Ownership
Your keys. Your data.
Your infrastructure.
BYOK isn't a feature we bolted on. It's the default — because your data should never pass through a middleman.
What this means in practice
Every API-connected integration we build uses credentials you control — your OpenAI key, your Anthropic key, your Azure AI endpoint. Requests go directly from your environment to the model provider. Nothing routes through our infrastructure or gets stored on our systems.
For clients with stricter data requirements, we deploy local models on hardware you own or VMs you control. Sensitive documents — patient records, legal files, financial data — never leave your network boundary. The model runs on your hardware, processes your data, and outputs go directly into your workflow.
At the integration layer we configure full audit logging: every AI call, every input, every output, logged to storage you own and retain. Compliance teams can review the complete history of every query the system has made. Your keys, your logs, your data — no exceptions.
// How We Work
Three things we do differently.
Most AI vendors sell the tool. We sell the outcome — and we stay to make sure it keeps working.
Audit first, always
Before we recommend anything, we map your workflows — what's manual, what's repeatable, what's high-cost. The audit produces a prioritized list of AI-ready opportunities with ROI estimates for each. You decide what to build. If nothing passes the bar, we tell you that too.
Build on your terms
We design integrations that fit your existing stack — Microsoft 365 pipelines, custom API layers, local model deployments, agent frameworks. No new platform to learn. Your keys, your infrastructure, your data policies. We document every component you receive.
Stay and support it
AI models update. APIs deprecate. Prompts drift as your business processes evolve. We provide ongoing managed support — monitoring, prompt tuning, model updates, and direct phone access when something breaks outside business hours.
// Our Process
From audit to production
in four steps.
We don't run multi-year transformation programs. Most clients reach production within 60 days of the workflow audit.
Workflow Audit
We spend time in your operations — talking to the people who do the work, not just those who manage it. We identify high-volume, repeatable workflows and rank them by AI-readiness and cost-per-hour saved. Output: a prioritized build list with honest ROI estimates.
Solution Design
We design the integration architecture: which model, which deployment pattern (cloud API, local, hybrid), what data flows in and out, how access is controlled, what the failure path looks like, and what monitoring catches drift before your users do.
Integration & Deploy
We build and deploy on your infrastructure with your credentials. Every component is documented. You receive the code, the configuration, and the architecture diagram. No black boxes — your team can understand, audit, and modify everything we ship.
Train & Support
We train your team on the workflows — not just the tool. Then we provide ongoing managed support: monitoring performance, handling model updates, tuning prompts as your processes evolve, and being reachable when something needs attention.
// What We've Shipped
Production integrations.
Real results.
These aren't prototypes or pilots. They're running in production for Pacific Northwest organizations right now.
17-Rooftop Infrastructure Network
Designed and deployed a managed network spanning 17 commercial rooftops — multi-site routing, centralized monitoring, and AI-assisted fault detection that pages the team before clients notice a disruption. The engineers who built it answer the phone when something goes down.
AI Sales Ops Orchestration
End-to-end sales workflow automation built on Claude — lead intake, qualification scoring, email personalization, and CRM logging. Deployed on BYOK infrastructure with full audit logging. Replaced approximately four hours of manual work per rep per week from day one in production.
Inbox Triage Skill
A three-pass AI triage system for high-volume email inboxes — categorize, prioritize, and draft responses. Built with Microsoft 365 integration and local model fallback for sensitive communications. Running in production since Q1 2026, processing hundreds of messages weekly.
Modular Skills Library
A library of reusable AI workflow components — document processing, data extraction, communication drafting, task orchestration — that clients assemble into custom workflows without rebuilding from scratch. New use cases ship in days, not weeks, by composing existing skills.
What happens after deployment.
An AI integration that works on launch day can drift, break on upstream API changes, or produce degraded outputs as your data changes. We plan for that from the start.
We instrument every integration we build — API error rates, output quality signals, latency, and fallback trigger frequency. You get visibility into whether the system is performing, not just whether it’s running. Alerts route to us first; we triage before escalating to you.
Model providers update APIs, deprecate endpoints, and change pricing without much notice. We track these changes and test your integration against them before they reach production. If something breaks, you hear about it from us — not from a user.
As your data changes and your team’s expectations sharpen, we tune. Output quality isn’t a fixed point — it requires iteration. We build evaluation pipelines into every production system so we can measure drift and improve without a full rebuild.
Scope check
Our AI work runs best in organizations deploying 1–5 models in production — document triage, email routing, CRM enrichment, internal search, reporting pipelines. We’re not a hyperscaler AI platform provider. If you’re operating at a scale that needs dedicated ML engineering, we’ll tell you that and point you toward the right firm.
Not sure where to start?
Start with the audit.
A workflow audit takes 60 minutes and gives you a prioritized list of AI-ready opportunities — specific to your operations, with honest ROI estimates. No commitment beyond the audit itself.
// Get in Touch
Let's start with
a conversation.
Tell us what you're working on. We respond within one business day — usually faster.
The workflow audit is a fixed-fee, 60-minute engagement. We map your current processes, identify AI-ready opportunities, and give you a prioritized list with honest ROI estimates — no obligation to proceed with any implementation.