AI-native MVPs, end to end

Founders who need a working product to put in front of users, not a model in a notebook.

Splitting an AI product between a “model person” and a “web person” creates seams where things break: data contracts, latency, error states, cost. One owner across the stack removes the seams.

• +Product scoping: the smallest version worth launching
• +Frontend in Next.js or React
• +Backend in Node.js or FastAPI with PostgreSQL
• +LLM features with structured output, validation and cost controls
• +Deployment, analytics hooks and documentation

• Next.js
• React
• Node.js
• FastAPI
• PostgreSQL
• Supabase

I'll work with your existing stack where it makes sense — these are defaults, not requirements.

Axo · Live · 2026

An AI product architect that turns an idea into a blueprint and build-ready prompts.

1. 01 · In / out

   Product ideaFree text, any level of detail
2. 02 · Model

   BlueprintFeatures, user flows, stack, architecture
3. 03 · Data

   Saved blueprintThe reviewable source of truth
4. 04 · Model

   Builder promptsGenerated from the blueprint
5. 05 · In / out

   ExportLovable · Claude · Cursor · Bolt

1. 01

   Intro call

   30 minutes, free. You walk me through the workflow; I ask about your data, your systems and what “done” means.

2. 02

   Written scope

   A short document: what gets built, what's explicitly out, milestones, and a price. No surprises later.

3. 03

   Build in the open

   A demo on a live staging URL every week. You see working software, not status reports.

4. 04

   Handover

   Your repository, deployment, documentation and tests. Support after launch if you want it.

• →Deterministic where it must be right. Money, permissions, routing and anything with consequences is decided by plain code that can be tested and audited. Models read, interpret and draft — they don't get the final say.
• →Guardrails are code, not prompts. A system prompt is a request. Validation, schema checks and post-processing are guarantees. Every model output is checked before a user or a database sees it.
• →It keeps working when the model doesn't. Providers time out, keys expire, outputs drift. I design a safe fallback path for each model call so the product degrades gracefully instead of failing loudly.