Nick C.

I build AI systems that have to work beyond the demo. Over the past 3+ years I’ve built production AI across RAG, document intelligence, AI agents, automation and SaaS, backed by 7+ years of full-stack engineering. I work as a fractional CTO and hands-on engineer: I make the architecture calls and I write the code. I can come in at different stages. 1. Sometimes you need someone to step back and work out the right architecture before the team starts building. 2. Sometimes you need a technical lead to make the key decisions and guide implementation. 3. And sometimes you just need someone to get into the code and ship. I do all three. I tend to get involved when a project moves beyond “let’s build a quick AI prototype” and the real questions start appearing: How should this actually be architected? How do we make the AI reliable? What happens when the data is messy, usage grows, or the model says something it shouldn’t? How do we keep performance high while controlling AI costs? That’s where I’m strongest. I’m Top Rated Plus on Upwork, and a significant part of my work comes from repeat clients. One client has brought me back 5+ times, which says more about my work than a list of technologies could. RECENT WORK DocsFlow / SureCiteAI | Document Intelligence (open source) Every answer cites the exact page it came from, and the system says “I don’t know” rather than guessing Public benchmark across six test suites: 0 hallucinated citations in 297 cases Hybrid keyword and vector search with reranking, finding the right document 96% of the time on FinanceBench Four-layer tenant isolation, including per-tenant encryption 1,188 commits in my github LPA Analyzer | Legal Contract Intelligence Extracts the key clauses from 150 to 200 page fund agreements, with confidence scores and page references Delivered as a Microsoft Word add-in, so lawyers never leave their document Caching cut repeat AI costs by around 60% Multi-Agent Operations Platform | in daily production for a client Five specialist agents, with human approval before anything reaches a real contact Replaced 15 to 20 hours a week of manual work Redesigned the slowest step from 16 seconds to under one, and from $2 to $4 per post to near zero. The whole platform runs on about $200 a month Regulated Pharma SaaS | Production readiness on AWS Closed a cross-tenant data access gap, and built a tamper-proof audit trail with tracing on every AI call 22 of 22 deliverables signed off against evidence, followed by a two-month pilot I’ve also worked on multi-agent game development systems and real-time computer vision for fitness, so my experience isn’t limited to one type of AI application. WHY CLIENTS BRING ME IN A cheaper freelancer can often get an AI demo working. The harder part comes later. Real users arrive. Documents don’t look like the samples. Edge cases appear. Security matters. Costs climb. The architecture starts showing its weaknesses. That’s usually where I add the most value. I build systems that are practical, scalable and production-ready, without adding complexity or burning through the budget. I’d rather build the right system for the problem than the most impressive stack. CORE TECHNOLOGIES AI / LLM: OpenAI, Claude, Gemini, Llama, RAG, AI agents, multi-agent orchestration, LangChain, LangSmith, evals and calibration Agentic development: Claude Code, Codex, Cursor Backend: Python, Node.js, TypeScript, PostgreSQL, Supabase, pgvector, Pinecone Frontend: Next.js, React, Tailwind Infrastructure and integrations: AWS, Vercel, n8n, Stripe, APIs, webhooks Microsoft, Google and IBM AI certified. SPEAKING DeFi Summit London MDEC ImmerseKL Taylor’s University If you’re building something where AI needs to work in production, not just look good in a demo, that’s the kind of problem I enjoy solving.