The Reality
The term AI developer has been diluted. There are now thousands of developers who can wrap an OpenAI API call in a Next.js app and call themselves AI developers. That's not what I do.
The difference is production experience. Building AI that works in a demo is trivial. Building AI that handles real data, real users, real edge cases, integrates with existing systems, and keeps working at scale — that's hard. That's what I've done.
I founded Cynfia, an AI platform built on retrieval-augmented generation that processes real business knowledge and serves it through a conversational interface. Not a side project or a proof of concept. A real product used by real businesses.
That's the level of AI development I bring to every project.
What I Build
You have an existing product — web app, mobile app, SaaS platform — and you want AI built into it. Not bolted on. Built in. I integrate Claude, ChatGPT (GPT-4o), and Gemini into your product in a way that actually makes sense for your users — conversational interfaces, image analysis, document processing, automated workflows, smart recommendations.
Best for: SaaS founders, app owners, businesses with existing products who want AI capability added by someone who's done it before.
RAG — Retrieval-Augmented Generation — is how you train an AI on your business data without the cost and complexity of fine-tuning a model. I build secure, production-grade RAG pipelines that connect your data to AI. Whether that's documentation, customer knowledge bases, product catalogues, legal libraries, or proprietary data — I architect it properly so it scales, stays accurate, and doesn't leak sensitive data.
Best for: Businesses with large bodies of knowledge (legal, medical, compliance, finance, ecommerce, SaaS) who want AI that actually understands their domain.
Building from scratch and want AI at the core from day one — not added later as an afterthought? I build AI-native mobile and web apps where intelligence is part of the architecture. Flutter for mobile, Next.js for web, Supabase for the data layer, and whichever AI API fits the use case. The stack is chosen for each project, not templated.
Best for: Founders building new products, startups, anyone who wants to build the AI version of something rather than the traditional version.
Not the generic chatbot that answers "what are your opening hours". I build AI chat interfaces that understand context, handle complex queries, integrate with your business systems, and actually reduce the load on your team. Customer support AI, internal knowledge assistants, sales qualification bots, document Q&A interfaces — built properly, with guardrails, with fallbacks, with logging.
Best for: Businesses spending too much on customer support, companies with large knowledge bases, SaaS products that need in-app AI assistance.
Have an AI idea and need to know if it works before committing to a full build? I take AI concepts from back-of-napkin to testable prototype in 4–8 weeks. Fast enough to show investors. Real enough to get actual user feedback. This isn't a clickable mockup — it's a working AI product with real data, real AI responses, real infrastructure, built to a standard that can be iterated into a full product.
Best for: Founders raising investment, businesses validating an AI concept, anyone who needs to show rather than tell.
Not ready to build yet? Or not sure what to build? I offer strategic AI consultations for businesses trying to figure out where AI fits, what it costs, and what it would actually take to implement. One session. No agency sales pitch. Just an honest assessment from someone who's built production AI and knows what works.
Best for: Business owners, CTOs, product leaders who want a straight answer on AI feasibility and cost before committing budget.
The Difference
Most AI projects fail. Not because the AI doesn't work — it does. They fail because of how it's built. Prompts that work in testing but not in production. No fallback when the API goes down. Data architecture that makes RAG inaccurate. Costs that spiral because nobody thought about token efficiency. I've built enough production AI to know all of these failure modes. I architect around them from the start.
What you get with a senior AI developer:
The Stack
Good Fit
You have an idea, you know AI is central to it, and you want someone who can build the whole thing rather than stitching together three different agencies.
You have an existing product and you want AI integrated properly by someone who understands both the AI layer and the product layer, not just one or the other.
A knowledge base that's not working, customer support volume you can't keep up with, or a manual process that AI should automate. You want it solved, not theorised about.
FAQ