Is the code production-quality?

Yes. AI accelerates typing, not judgment. Architecture, tests, edge cases, and review standards are the same as on any senior engagement.

Does my IP go to OpenAI / Anthropic?

No. I use enterprise-tier tooling with zero-retention policies and codebase-scoped contexts. Your code stays your code. NDAs apply.

What if the AI hallucinates something?

Senior review catches it. Every diff is reviewed before merge — by me, plus an AI auditor as a second pair of eyes. Hallucinations die in the PR, not in production.

Can you work with my existing codebase?

Yes. Claude Code reads the whole repo before suggesting changes. I take over Flutter, native iOS, and native Android codebases regularly — AI just makes the onboarding phase faster.

Home

AI-augmented delivery

Ship 2× faster.
Without the vibe-coder discount.

I use AI tooling everywhere it earns its keep — and stay in the driver's seat for everything that matters: architecture, edge cases, production quality.

Send a message See case studies

2–4 wks

Faster MVPs

Typical mobile MVP timeline, down from 6–12 weeks.

−50%

Lower budget

Less manual scaffolding, fewer billable hours.

100%

Same quality bar

Every line reviewed by a senior engineer — me.

How AI changes the math

Same scope, same quality bar, less of the work that doesn't need a senior brain.

Dimension

Without AI

With AI

MVP timeline

6–12 weeks

2–4 weeks

Scaffolding effort

Days of manual setup

Hours, AI-generated

Code review

Manual, after the fact

AI audit + senior review on every PR

Design-to-code

Pixel-pushing by hand

Figma MCP straight to Flutter / SwiftUI

Quality bar

Same

Same — AI accelerates, doesn't replace judgment

The tools I rely on

Each one earns its keep. No tool here exists for the demo — they all save real hours every week.

Repo-aware coding partner

Claude Code

AI that reads your whole codebase before writing — refactors, migrations, and feature scaffolds in minutes instead of days.

AI IDE for tight loops

Cursor / Windsurf

Inline generation and multi-file edits with project context. Where Claude Code does the heavy lifting, Cursor handles fast iteration.

Design-to-code, no copy-paste

Figma MCP

Designs come straight from Figma into Flutter / SwiftUI screens — pixel-faithful, with the project's tokens and components reused.

PR audits before merge

AI code review

Every pull request gets an automated senior-level review — security, regressions, dead paths — before a human reviewer touches it.

How it works in practice

A repeatable rhythm — AI handles velocity, I handle judgment.

AI scaffolds, I architect

Boilerplate, models, API clients, basic screens — generated in hours. The architecture, the data model, the tradeoffs — decided by me upfront, not improvised by an LLM.

AI iterates, I review

Feature work happens in tight AI-assisted loops. Every commit goes through senior review and AI-powered PR audit. Production-grade tests, not toy demos.

AI accelerates, I ship

Release pipelines, store listings, privacy manifests — automated where it's safe, hand-checked where it counts. The last 10% to launch stays human.

Real projects, AI-accelerated

Three live App Store apps where AI tooling earned its keep — without lowering the architecture bar.

Flutter · iOS & Android

Boyfi — AI provider abstraction

Dual subscriptions (App Store IAP + Stripe-driven web funnels) and two AI providers (OpenAI + Grok) behind one interface. AI tooling scaffolded the boilerplate; senior judgment owned the abstraction. Eight months later, a third provider was added by touching one module.

Read the project notes

Flutter · iOS · Cloud Functions

AI Music — generation queue at scale

Text-to-song generation with FoxAI and MusicAPI behind a provider-agnostic interface, backed by Cloud Functions that orchestrate the generation queue and webhook callbacks. AI tooling shaped the screens and the queue glue; the architectural call — single source of truth, retry semantics — stayed human.

Read the project notes

Flutter · 49K LOC · NDA

Companion — billing version migrations

Three monetization models running in parallel — tokens, tiered subscriptions, moment packs — with zero billing errors during migrations. AI generated the boilerplate webhook handlers; the version-routed PaymentEvent architecture was designed deliberately, not improvised.

Read the case study

The pattern is the same across all three: AI accelerates the implementation, but the architectural decisions — where the abstraction lives, how to route between providers, how to version a payment model — stay with a senior engineer. That's the line between AI-augmented and vibe-coded.

What stays the same

AI raises the floor, not lowers it. Here's what doesn't change when you hire me.

Senior judgment on architecture and tradeoffs.
Code I'd happily inherit a year from now.
Direct line to the engineer — not a prompt-engineering team.
NDA-friendly: AI tooling configured for your privacy needs.

Frequently asked

The questions founders ask before signing off on AI-augmented engagements.

Is the code production-quality?: Yes. AI accelerates typing, not judgment. Architecture, tests, edge cases, and review standards are the same as on any senior engagement.
Does my IP go to OpenAI / Anthropic?: No. I use enterprise-tier tooling with zero-retention policies and codebase-scoped contexts. Your code stays your code. NDAs apply.
What if the AI hallucinates something?: Senior review catches it. Every diff is reviewed before merge — by me, plus an AI auditor as a second pair of eyes. Hallucinations die in the PR, not in production.
Can you work with my existing codebase?: Yes. Claude Code reads the whole repo before suggesting changes. I take over Flutter, native iOS, and native Android codebases regularly — AI just makes the onboarding phase faster.