Workflow mapping
Where people spend time today, and which steps an agent can take on.
All services
What we design · Agentic Workflows
AI that fits how people work.
We design agentic workflows and AI-native flows around how people actually work, so automation earns trust instead of adding friction.
Why it matters
Automation people actually trust.
Adding AI to a product is easy. Making it useful is not. People need to understand what the AI did, correct it when it's wrong, and stay in control of the result.
We map the real workflow first, then design where AI should act, where it should suggest, and where a person needs to decide.
What’s included
What we design.
Human in the loop
Review, edit and approve patterns that keep people in control.
AI interfaces
Prompts, suggestions, summaries and generated content that are easy to trust.
Spec-driven builds
Specs and prototypes built with Kiro, Cursor and Claude Code.
How it works
Test every assumption, then build.
- MapCurrent workflow
- FindWhere AI helps
- DesignHuman-in-the-loop flows
- PrototypeTest with real users
Work in this area
Proof, not promises.
Healthcare AI · UX discovery
Freed AI
10,000+
clinicians use the AI scribe we explored a browser extension for.
Read the case study
Retail · AI search
Collagerie
AI search
Redefining product discovery with an AI-based search experience.
See all work
EdTech · 0-to-1
FlowMo
$600K
raised from VCs for the in-app LMS we designed from zero to one.
Read the case study
Ways to work with us
Flexible engagement, senior people.
Fractional
Senior product design and leadership inside your team, part-time, for as long as you need it.
Advisory
Focused guidance at the decisions that matter: audits, roadmap reviews and design critiques.
Retainer
Ongoing design and product support with a team that already knows your product.
FAQs
Good questions.
Do we need an AI product already?
No. We often start with an existing workflow and find where AI can take work off people's plates.
Which AI tools do you use?
We work with spec-driven development in Kiro, Cursor and Claude Code, rapid prototyping in Replit, and whichever models your team uses.
How do you handle AI mistakes?
We design for them: clear sources, easy edits, and a person approving anything that matters.