HOW WE BUILD

How we build

Eight steps from problem to shipped product. AI does the volume; we own the decisions.

THE PROCESS
Strategy & spec
Design & architecture
Build & test
Ship & learn

Stress-test the problem with AI-led research and user simulation so you don’t ship something nobody needs.

What we do
  • Simulate user conversations to see if the pain is real
  • Use AI to surface blind spots and competitor context fast
Why it matters

Skipping this is how teams build the wrong thing confidently.

MissionVisionStrategyGoalsRoadmap → Tasks

AI drafts; we refine a single source of truth—problem, out-of-scope, metrics, and users.

What we do
  • Turn a rough idea into a tight written plan
  • Reconcile the plan with Step 1 so bets stay coherent
Why it matters

This page steers every later decision—get it crisp early.

ProblemNon-goalsSuccess criteriaUser stories

Translate the plan into architecture—what to store, how pieces connect, and key tradeoffs—before build starts.

What we do
  • Produce a technical design doc from the plan
  • Document major boundaries and why they exist
Why it matters

Cheap to change on paper; expensive to unwind mid-build.

UI LayerAPI / LogicDatabaseExternal APIsdata model · api surface · components

Ship interactive prototypes from the plan so stakeholders align on what “it” is early.

What we do
  • Generate clickable prototypes with AI design tools
  • Walk stakeholders through to kill ambiguity pre-build
Why it matters

Clicks surface misunderstandings decks hide.

Use AI coding tools against the plan; experiment, cut losers, double winners—most shops stop at mockups.

What we do
  • Brief AI coding tools from the written plan
  • Iterate in small loops instead of a rigid assembly line
Why it matters

Cheap experiments beat big bets on the wrong path.

claude_code — zsh$ claude "build auth flow"✓ Scaffolding routes...✓ Generating components...✓ Writing tests...→ 12 files created→ Ready to iterate

Replace vibes with tests: accuracy, consistency, tone—and monitoring so regressions surface early.

What we do
  • Define pass/fail checks for outputs (incl. AI features)
  • Instrument monitoring for live quality drift
Why it matters

Hope isn’t a QA strategy.

Eval ResultsCorrectness — 89%Consistency — 80%Brand alignment — 95%✓12 passed✗2 failed

Launch with analytics on, decisions logged, and notes feeding the record—go-live is the start line.

What we do
  • Instrument before launch for real usage signal
  • Keep a written trail of shipping calls and rationale
Why it matters

You can’t improve what you don’t measure.

LaunchDAUTime →

Run the cycle again with what worked, what didn’t, and sharper AI boundaries—each lap gets faster.

What we do
  • Re-enter Step 1 with usage and test results
  • Tighten bets on what AI handles well vs. not
Why it matters

Compounding process beats one-off heroics.

DiscoverBuildEvalShipAIsense

We use AI to do the work, not just give advice.

AI handles drafting, code, and research—you keep the calls and tradeoffs.

Each step ships a deliverable; we don’t improvise the playbook mid-flight.

Most consultants give you a slide deck. We give you a working product.

Ready to build something?

Walk your idea through the same eight steps.

Work with us →