Can you build an MVP and then scale it?

Yes — I build MVPs end-to-end fast, then evolve the same codebase to production scale instead of throwing it away. Several of mine grew to acquisition or millions of users.

Can you build an MVP and then scale it?

Yes — I build MVPs end-to-end fast, then evolve the same codebase to production scale instead of throwing it away. Several of mine grew to acquisition or millions of users.

AI consulting & engineering

From MVP to scale — building it lean, then growing it

Q: How do you actually implement AI in a business?

I find where AI creates real value, design the architecture, and build it end-to-end — running systems, not slideware.

Q: Can you build AI into a healthcare product?

Yes — I have architected clinical decision support, AI meal analysis and LLM health assistants on a custom model + vector database, with the privacy and reliability healthcare demands.

Q: Can you build a production mobile app in React Native?

Yes — I ship native iOS and Android apps from one Expo/React Native codebase, with payments, provisioning, offline-first flows and clean native integrations.

Q: How do you scale a Node.js backend?

With the right architecture: services split by load, event streaming, queues and caching, observability, and databases chosen per workload — proven on a platform that grew to 3M+ users.

Q: How do you build a project with agentic AI development?

I drive AI coding agents (Claude Code, Codex, Cursor) with custom rules, hooks and context, spec-first — then gate everything with tests and AI review so speed never costs quality.

Q: How do you use CodeRabbit for code review and quality?

CodeRabbit reviews every pull request automatically — catching bugs, security issues and smells — as a gate alongside human review and a real test suite, so nothing weak reaches main.

Q: Can you build a custom AI model for my product?

Yes — self-hosted models on Ollama (Llama, Mistral, DeepSeek), fine-tuned or RAG-grounded, when you need privacy, control or a task an off-the-shelf API can’t do well.

Q: How do you build a RAG system?

Chunk and embed your data into a vector database, retrieve and rerank the right context per query, ground the model in it, and evaluate — so answers are accurate and cite your sources, not hallucinations.

Q: Which AI model should you use — Claude, GPT, Gemini, Perplexity or Grok?

It depends on the task — and the real answer is usually more than one. I pick and route across frontier models by strength, with evals and fallbacks, so you get the best result without lock-in.

The trap with an MVP is building something so throwaway you have to rebuild it the moment it works — or so over-engineered it never ships. I aim for the narrow path: an MVP you can put in front of users in weeks, on foundations that grow instead of collapse.

Ship the smallest thing that proves the point

I have architected MVPs end-to-end many times — OptAI’s AI-search visibility platform, Sumus’ product-scoring engine (from scratch with a team of five), and Amygdal’s own products. The discipline is cutting scope to the one thing that must be true, and shipping it with weekly releases so you learn from real users fast.

Foundations that do not need a rewrite

An MVP still deserves a real architecture — a monorepo, typed end-to-end (TypeScript, Zod), sensible service boundaries and a database you can grow into. That is the difference between “add a feature” and “start over” once traction arrives. I pick boring, proven building blocks so the exciting part stays your product, not your infrastructure.

Then scale the same codebase

myzone.ai went from MVP to acquisition; aichatapp.ai went past three million users on the architecture it launched with. Because the MVP was built to grow, scaling meant hardening and extending — not rebuilding. That continuity is where most of the value is.

Key points

MVP shipped in weeks with weekly releases to learn from real users.
Real-but-lean architecture so growth means hardening, not rewriting.
Proven path: MVPs that grew to acquisition and to millions of users.

Tools I reach for

TypeScriptNext.jsNode.jsNestJSPostgreSQLZodVercelRailway

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From MVP to scale — building it lean, then growing it

Ship the smallest thing that proves the point

Foundations that do not need a rewrite

Then scale the same codebase

Related work

Have something like this in mind?

More questions I'm asked