SERVICES
We help define, design, and build applications and workflows around a specific task. The technology follows the need.
For a new product or a change to an existing one, we help turn an idea into a clear, testable release. That may mean a web application, a mobile experience, or the systems behind it.
The first conversation is about the audience and the job the product needs to do.
When information lives across spreadsheets, messages, and disconnected tools, a purpose-built workflow can make the next decision easier to see and act on.
We start by mapping the current steps and exceptions before choosing what to automate.
AI can help with repetitive, information-heavy tasks when the source material is dependable and the result can be checked. We look for the right boundary between automation and human judgment.
A useful AI feature starts with a defined task, evaluation criteria, and a plan for review.
An AI harness connects an agent to the context, tools, instructions, checks, and human review it needs for a defined workflow. We design these pieces as a system, then test how the system behaves with real inputs and failures.
Our own product, Gluno, runs a version of this pattern today: a coaching agent with a fixed context dossier, strictly read-only tools, and data boundaries enforced in code rather than in the prompt. It is currently in early-access testing.
WHERE OUR AI WORK CONCENTRATES
Across engagements, and in our own products, the same underlying capabilities keep showing up.
Pulling data from many places into one normalized, de-duplicated, clustered picture — instead of a dozen disconnected feeds.
Agents that answer questions from your own data, with citations back to the source — and a clear "no" when the evidence isn't there.
Agents that run a recurring operational workflow, plus the dashboard that shows whether the run actually succeeded.
Turning manual, paper-and-spreadsheet workflows into software, with AI doing the repetitive reading, matching, and structuring.
WORKING APPROACH
Each engagement needs its own scope. These are the decisions we expect to make together, not a one-size-fits-all package.
Who is involved, what happens now, and where the friction sits.
What the first version must do, what can wait, and how to assess it.
Make the system, test the important paths, and explain the tradeoffs.
Use feedback from real work to decide what to refine next.
WAYS TO START
Early-engagement pricing, while we build our track record. Each stage is scoped before it starts.
A short, fixed-price engagement to map your data and workflows and identify the one agent worth building first. Credited toward a Pilot if you move forward within 30 days.
One agent, one metric, running on your real data within a few weeks — draft mode, with a human reviewing every output at first.
A retainer to run, monitor, and improve the agent as your data and needs change.
When a pattern repeats across engagements, we turn it into reusable software — sometimes built with a client, sometimes as one of our own products.
