Your AI Partner
Custom agents, solution architecture, and forward-deployed engineering for teams putting Claude into production.
One platform understood deeply, instead of six understood adequately.
AI is moving so fast. I need someone to help us navigate this transition.
Agents where the work already happens
We map how your teams actually operate, then build the skills and agents each one needs — deployed into the tools they already open every morning.
Designed, shipped, and operated by the same team that built them.
Book a consultationHow an engagement runs
Three phases. The third is the one most firms skip, and it is the reason agents that launched fine stop working three months later.
Analyze
We work inside your systems and alongside your team until we understand a workflow well enough to automate it. Not a discovery deck. Real access, real data, real constraints.
Implement
We architect the system and build it on Claude: agents, tools, MCP servers, skills. Then we deploy it where your team already works.
Improve
Every agent ships with evals. We measure it before launch, watch it in production, and keep it from degrading as your business changes underneath it.
The whole Claude surface, not just the chat box
Most teams use a fraction of what Claude can do. These are the pieces we build with.
Agents that work in your repositories, not beside them.
The servers that connect Claude to the systems you already run.
Long-running agents with real tools and real permissions.
Reusable capabilities your team owns and can run without us.
Shared context, so an answer does not start from nothing.
Where most of your team will actually meet the work.
Six things we do, all of them on Claude
Most businesses end up juggling a consultant, a developer and a trainer. We are the engineering half, and we go deep on one platform rather than wide across six.
Custom agent development
Agents that do the work, not demos that prove it could be done. Built against your systems, your data, and the workflow your team actually follows.
Solution architecture
The decisions that determine whether any of this holds up: what is an agent, what is a tool, what is a skill, where a human stays in the loop, and what happens when the model is wrong.
Forward-deployed engineering
We embed. Our engineers work in your environment, in your repositories, next to your team, until the system runs in production.
MCP and integration engineering
Claude is only as useful as what it can reach. We build the MCP servers that connect it to the systems your business already runs on.
Data migration and cleanup
Agents are only as good as what you point them at. We move, reconcile and clean the data first, because the unglamorous half is usually what decides whether the rest works.
Evaluation and reliability
Every agent ships with evals. We measure it before launch, watch it after, and keep it from quietly getting worse.
Looking for training rather than engineering? AI at Work runs live workshops and coaching for individuals and teams.
State of AI Agents 2026
Before we committed to one platform we went through all of them — the major agent frameworks, architectures and models, seven pages of it. The report is the survey, including the part where we explain why we stopped hedging across the field and picked one.
Sent by email while the download is being prepared.
A narrow firm, on purpose
Most teams can get a demo working. Far fewer can get one running in production and keep it running. That gap is an engineering problem, not a strategy problem, and closing it is the whole of what we do.
We only build on Claude
One platform, shipped repeatedly, is how you learn where it breaks: context that silently truncates, tool calls that fail quietly, evals that drift. A team spread across six platforms finds those in your production system. We found them in ours.
We embed, we don't advise
No strategy deck and a handshake. Our engineers work in your repositories and your environment until the system runs, then stay on to keep it running.
Every engagement leaves you a skill
Each project exits with a reusable capability your team owns outright. A rebrand engagement became the naming system we now run on every client. The library grows as a byproduct of the work, and you keep your half of it.
We are an engineering team. We architect and build custom Claude agents, deploy them into your environment, and stay on to keep them running. Two things sit next to this and are deliberately separate: Cascadian Intelligence, our forecasting product, lives on its own site, and training for individuals and teams is AI at Work.
Because depth is the product. Agent work fails on details: how context is managed, how tools fail, how you know the thing still works six months on. Those details are platform-specific and they do not transfer. A firm spread across six platforms knows the first twenty percent of each. We would rather know one of them completely.
Honestly, probably not. What we sell is depth on one platform, and that depth does not carry over to another. If Claude is not on the table, we are the wrong firm, and we would rather tell you that on the first call than after the first invoice.
Most consultants hand you a strategy and leave before anything runs. We build the agents, deploy them into your systems, and stay on to keep them working. Strategy and implementation are one engagement here, done by the same people.
Evals, before and after launch. Every agent is measured against real scenarios from your business before it ships, then monitored in production. When it drifts, we fix it. An agent nobody is measuring is an agent quietly getting worse.
Thirty minutes with a senior partner about what you are trying to build, whether Claude is the right substrate for it, and what a first engagement would actually involve. No pitch deck.
Book a free consultation.
Tell us what you are trying to build. We will tell you whether it is worth building, and what it would take.
No pitch deck. No pressure.