Intelligent Systems for Real Operations
The productivity gains from AI haven't happened yet
Most AI deployments are layered on top of operations that haven't changed — ChatGPT, copilots, AI notetakers, toys like NotebookLM. Useful, but bounded.
The transformative gains come from intelligent systems built into operations: scheduling that knows the shop floor, health care that reduces complexity, quality systems that catch defects in real time, conversations that capture clean structured data.
That work is harder. It's also where productivity claims are earned.
What We Build
Operational AI systems Production software with intelligent components inside it. Scheduling, dispatch, quality, compliance, customer conversations, decision support — designed for the specific operational problem, not generic chatbots wrapped around your business.
Custom algorithms and architectures Off-the-shelf tools rarely fit operational work. When they don't, we build what does — production solutions in energy, healthcare, manufacturing, logistics, and the public sector.
Process redesign with AI in mind Many operations aren't ready for an AI layer because the underlying process is the problem. We rebuild the process and the intelligent system that runs it — Lean Six Sigma operating discipline with AI as the enabling technology.
End-to-end execution Discovery through deployment, by a small team that does the engineering itself. The people designing the system are the people writing the code.
How an Engagement Works
Discovery A conversation about your operation, the problem you want to solve, and what you've already tried. By call or on-site visit. No charge.
Assessment A written assessment of the problem, the approach we'd take, the systems involved, and the likely scope. Free if you proceed to a proposal.
Proposal Fixed scope, fixed price, fixed timeline where the work permits it. No open-ended engagements.
Build We deliver. The team that scoped the work is the team that builds it.
We've described three example engagements in detail.
Where this Work Applies
Our work is industry-agnostic. We listen and pay attention. What matters is whether the underlying problem is recognizable:
A process that can't scale
Decisions being made by spreadsheets
Data trapped in unstructured records
Customer interactions that produce nothing usable
Regulatory or compliance reporting that consumes weeks of staff time
Workflows that depend on one person's tribal knowledge
Quality issues that only get caught after they've shipped
Operational data your team has but can't query
AI tools your staff are using ad-hoc without a coherent strategy
If any of these describe your operation, the conversation is worth having.