Agents that compound. People who decide.
We build AI around the work you already do: start where you are, change the shared process, keep people on the first and last mile, and write the rails so it still works next week.
Start where you are
A useful engagement meets the work, the people, and the tools you already have. Sometimes that means helping a leadership team use the current models well. Sometimes it means rebuilding a back-office process. We do not drop a long recommendation and leave you to implement it.
Change the shared job
Giving one person a better assistant can save time. It does not compound. The larger gain is a process the whole function runs: intake, context, a prepared next step, and a named owner. That is the difference between a chatbot on a desk and an agent in the workflow.
You own the first and last mile
You choose the job worth doing and the standard it has to meet. AI can research, draft, route, and check the middle. A person still sets direction and still reviews what matters before anything is sent, filed, or paid. Preparing a reply is not sending it.
Write it so it keeps working
Agents are fast. Long runs drift. The durable part of the work is the written instructions, examples, and checks that keep the next session on the rails. Code can be regenerated. The “why” and the operating rules cannot.
Show something real early
Get a working slice in front of the people who will use it. When they see the leverage, the next scope becomes obvious. Timing depends on the work; we do not sell a universal clock.
One workflow.
A scope you can evaluate.
A pilot should answer a business question. Agree the task, the evidence, and the decision before expanding the build.
- 01
Choose one task
Identify its trigger, current owner, inputs, and useful output. Bring an ordinary example and a case that is difficult today. Agree what is included and which actions need approval.
- 02
Check the foundations
Review available data, system interfaces, permissions, and the people needed to answer questions. Record missing prerequisites and operating costs before committing to a build plan.
- 03
Build and test the agreed scope
Prepare the workflow and compare its behavior with written acceptance criteria. Include incomplete inputs, conflicting sources, repeated requests, and failed dependencies where they apply.
- 04
Decide and hand over
Review the evidence and remaining limitations with the workflow owner. Agree whether to revise, launch to the initial audience, or stop. Document access, monitoring, recovery, and any continuing support.
Before we begin.
Is this just ChatGPT for the team?
A licensed model on every desk can help one person. We look for the shared job—reporting, intake, follow-up, review—where a better process helps the whole team.
Do you replace the people who know the work?
No. They choose the job and they review what matters. AI prepares the middle. If a task should not be automated, we say so.
What do we have to write down?
Enough that the next run knows the goal, the sources, the checks, and when to stop. That operating record is part of the handoff, not an extra slide deck.
How much does an AI or automation project cost?
Price depends on the workflow, source quality, integrations, permissions, and review requirements. A scoped proposal should separate implementation from recurring model, hosting, platform, and support costs. Describe one task first so the estimate can be based on the work and its dependencies.
How long will the project take?
The timeline is agreed for the specific scope. Access to systems, ready source material, unresolved business rules, and the availability of reviewers all affect delivery. The project plan should identify those dependencies and distinguish a feasibility prototype from a workflow ready for everyday operation.
Can we use the software and data we already have?
Assess the existing tools and sources first. APIs, exports, account permissions, data quality, and provider terms determine what can be connected. Begin with an outline or redacted examples; agree a suitable sharing method before providing credentials or sensitive records.
How will we know whether the result is good enough?
Agree test cases and acceptance criteria with the person who owns the workflow. Check expected outputs, evidence, incomplete inputs, and permission boundaries. Review the recorded failures as well as successful runs, then define the monitoring needed for the initial rollout.
Who owns and maintains the result?
Code ownership, licensing, hosting, access, and continuing support are terms to agree before work starts. The handoff should identify the owner for source updates, failures, credentials, and changes. Continuing maintenance is part of the engagement only when it is explicitly included.