The understanding that matters.
Your users, business priorities, existing work and constraints. The things a brief alone cannot tell us.
How we work
Your people.
Our craft.
One team.
When a first version can take minutes, the next question matters more: is it useful? We help your team use AI to explore quickly, learn from real work and turn promising ideas into products people can depend on.
Let’s talk about your teamFast learning. Shared decisions. Useful progress.
Fast learning. Thoughtful decisions.
Find what matters.
Make it real.
Keep making it better.
AI helps us explore and build quickly. Your team’s knowledge, real-world evidence and human judgment determine what deserves to move forward.
Possibilities take shape.
People give them direction.
Start from what you already have.
Bring an idea, a prototype or a live product. We start with the question that needs attention now.
These questions repeat as the work develops. Engineering informs the early choices; learning continues after release.
Thinking and delivery, together
Make room for ideas.
Keep the work moving.
“Mike and his team do as much ideation as possible, but then they also execute quickly”
A shared working relationship
You should be
part of the thinking.
Quick experiments give us something to react to together. Your knowledge of the people and the problem helps us decide what to try, what to change and what is worth building further.
Ways to move it forward.
Product questions, designs and working software that give us something concrete to consider together.
Inside a product decision · PRVNT
Start with the person.
Then connect the tools.
For Nsight Health, patient information, monitoring and calling needed to work together around the care team.
Patient overviewCalling controlsClinical notes
THE REASONINGPatient information belongs
Patient information belongs
with the conversation.
PRVNT brings the patient overview, calling controls, clinical notes and monitoring trends into a connected workflow.
The lesson: consider the work someone is doing before treating each feature as a separate task.
Read the full caseSpeed without sacrifice
Move quickly.
Know what you can trust.
An experiment and a live product carry different consequences. Match the depth of review to what people will rely on, then keep learning as the product grows.
EXPLORING AN IDEA
Can this help?
Test the useful task. Make assumptions visible. Keep experiments contained and easy to change.
DirectionPUTTING IT INTO REAL USE
Does it work here?
Check the actual workflow, important errors, access and usability with the people involved.
Working evidencePEOPLE DEPEND ON IT
Can we rely on it?
Agree the testing, privacy, reliability and operating responsibilities the product needs.
Confidence to operateThese are levels of responsibility, not three mandatory phases. A change to a live product may need all three questions from the start.
AI in the work
More possibility.
Human judgment.
AI can help explore alternatives, build and test. People define what matters and decide whether the result is good enough.
Agree the tools, data boundaries and review responsibilities before the work starts. Keep room to question, redirect or stop.
01 / PEOPLESet the purpose.
Define the problem and the boundaries.
02 / ASSISTED WORKExplore and build.
Use tools to help move the work forward.
03 / HUMAN REVIEWInspect the result.
Test it, question it and understand it.
04 / A DECISIONAccept or redirect.
Keep responsibility with people.
This frames the work; individual tasks do not always follow a fixed sequence. Review can send us back to the question.
We put this thinking into a product.
DevSwarm brings agent work and human review into the same environment. It is one concrete example of the practice we’re developing in our own work.
See DevSwarm in practiceAgree the responsibilities
Clarity is part of the work.
Security, privacy and accessibility requirements belong in the product decisions, alongside the people responsible for them. Make the boundaries explicit before work begins.
People and decisions.
Agree who is involved, who makes which calls, and when a tradeoff needs the sponsor. Use working software and tangible designs to review progress and decide when to change course.
Data and access.
Identify the systems and data the work needs, who can approve access, which tools may use it, and how access ends. AI and third-party services belong in that same agreement.
Review and release.
Agree what needs checking, what evidence supports acceptance, and who approves a release. Supporting a client’s compliance requirements is a scoped responsibility, not a certification.
After delivery.
Discuss repositories, documentation and ownership early. Agree who handles monitoring, incidents, maintenance and support after handover, and put those responsibilities in the engagement scope.
A useful first conversation
Where are you now?
What needs to change?
Bring an idea, a difficult question or a product that needs to evolve. We can start there.
Tell us where you’re starting