Launch · Build Your Real AI Product
I build it with you.
All four phases built with your team. From the Big AI Idea through the architecture and the AI Build to features live in production, I build with your team. Not a black box your team can't maintain. A launched product, and a team that can keep building it.
The full arc — Idea to Launched.
Launch is the complete engagement: all four phases — Idea, Architecture, AI Build, Launched — over ten to sixteen weeks, with your team involved the whole way. I find or confirm the Big AI Idea, stand up the architecture, build the first real features through an AI pipeline your team learns by using, and land them in production with a playbook your team keeps.
It is not a dev shop that builds in a silo and hands you code you can't maintain, and it is not a strategy deck no one builds. It's the right thing, built the right way, alongside the people who'll own it — greenfield, building the right new thing rather than patching a broken pilot.
If you came through Clarity, Launch builds exactly what Clarity mapped, the Big AI Idea, taken all the way to production. Come straight in, and we scope it with you before anything's committed. The size of that build sets the range: a focused idea sits near the bottom, a full platform near the top. Either way, you leave with the product live and a team that can keep building the same way.
Pricing is to scope, because every build is. The complexity of your idea sets the number — that's what Clarity delivers, and what the proposal pins down for anyone coming straight in. Once it's scoped, it's fixed: fixed price, fixed timeline, no hourly meter running.
How Launch Works
Idea
weeks 1–2
We lock the Big AI Idea — the highest-leverage thing to build, scored on value and feasibility — and define what 'launched' means in concrete terms. If you came in through Clarity, this is already done, and we start week one in architecture.
End of phase: the target locked, with a definition of done your whole team agrees on.
Architecture
weeks 3–4
We stand up the Four Foundations the build has to hold — the data model, coding standards, component patterns, and API contracts — and document every decision with its rationale. This is the context the AI pipeline builds inside, and the reason the output stays coherent instead of diverging in week three.
End of phase: a documented architecture your team can extend without guessing — and onboard new engineers against in days.
AI Build
weeks 5–8
We build the first real features through an AI pipeline — your engineers writing alongside me, learning it by using it on your actual product, not on a tutorial. Assumptions get tested against production reality here, where they're cheap to fix, not after launch.
End of phase: the first real features working, and a team that can now run the pipeline themselves.
Launched
weeks 9–10
We take it to production — real features, live, used — and I hand off the playbook: the patterns, the pipeline, and the standards, documented so your team moves is productive without me. The handoff is the deliverable, not an afterthought.
End of phase: features live in production, and a playbook your team keeps building from.
What the weeks actually look like
We Map the Territory
Before solving anything, I need to understand what you’re actually building and why. This isn’t a requirements gathering exercise — it’s a deep investigation into your business context, your existing systems, your team’s strengths, and where AI creates genuine leverage versus where it creates noise.
Business Context — existing systems, team capabilities, technical constraints
Opportunity Mapping — where AI creates leverage versus where it creates noise
Success Criteria — what “working” actually means, in measurable terms
Your team stops debating what to build and starts agreeing on why.
We Design the System
This is where the real work happens. Product design and software architecture, sequenced so every decision in code is grounded in a decision about the product first. Every architectural decision is documented with its rationale — not just what we chose, but why alternatives were rejected.
Product Design· Weeks 2-3 — JTBD, use case definition, information architecture
Software Architecture· Weeks 4-5 — data model, API contracts, coding standards, component patterns
Your engineers open their AI tools with clear constraints instead of blank canvases.
We Build the Working Software
Architecture on paper is theory. Working software is proof. This is the build — real, production-grade features on the architecture and standards we defined, not a throwaway prototype. We build the hardest part first, so the assumptions that would sink you surface here, where they’re cheap to fix, instead of after launch.
Scoping· Week 6 — identify the core and riskiest parts to build first
Build· Weeks 7-11 — real, production-grade features on the defined architecture
Validation· Week 12 — prove the hardest assumptions against real use
Your stakeholders see working software, not slide decks. Your team sees the architecture in action.
We Test, Ship, and Hand Off
The last stretch is testing, QA, and go-live — then the handoff that makes it stick. We harden the software against real conditions, take it live, and capture every architectural decision, coding standard, and data model in documents your engineers reference daily — not a PDF collecting dust in Google Drive. The engagement ends when your team can build without me.
Testing & QA· Weeks 13-14 — harden against real conditions; fix what surfaces before it ships
Launch· Week 15 — features live in production, in real use
Handoff & Sign-off· Week 16 — system walkthrough and living documentation — your team builds without me
Your team builds production-grade AI features in weeks, not months — long after I'm gone.
What You Get
The Big AI Idea, locked
The target — scored on value and feasibility, with a concrete definition of done — found in phase one, or carried in from Clarity.
The standing architecture, documented
Data model, coding standards, component patterns, API contracts — every decision written down with its rationale, so your team can extend it without guessing and onboard new engineers in days, not months.
The AI build pipeline
Set up and run with your team — your engineers learn it by building on your product, so the way of building is theirs by the end, not a process they inherit cold.
The first real features, in production
Live, used, real — not a sandbox demo that impresses and stalls. The right first thing, shipped.
A launched product
The Big AI Idea, built right and live in production with the roadmap Clarity mapped, delivered.
The playbook
The patterns, the pipeline, and the standards captured in a handoff doc, so the team doesn't skip a beat without me in the room.
A team that owns the velocity
The capability, not just the code. When the engagement ends, your team keeps building at the same speed — because they built it with me, not after me.
A launched product, and a team that keeps building.
You get the product and the capability. The week-three wall never appears, because the architecture came first. You've de-risked the most expensive thing in AI — you built the right thing, the right way, with the people who'll own it — instead of discovering the mistake a quarter and six figures in.
And when the engagement ends, nothing stalls. The velocity is your team's, the architecture is documented, the playbook is in their hands. You're not on a retainer and you're not waiting on me — that was the point all along. ROI that actually arrives, and keeps arriving after I'm gone.
Build the right thing, the right way.
Launch takes you from idea to a product in production — ten to sixteen weeks, with your team owning it after. It starts with a 30-minute Discovery Call to surface the real problem and the options, and see if it's a fit.