For founders, SaaS teams, agencies & businesses adding AI to a real product.
AI Development for Production Software
I build AI features inside real applications — not isolated demos.
From AI assistants and LLM integrations to AI agents and RAG-powered product features, I take AI work from architecture through production deployment so your team can ship, not experiment.
You don't need to know the AI model or architecture yet. Describe the problem first.
Stack
- OpenAI
- Claude
- Gemini
- Next.js
- FastAPI
- Node.js
- PostgreSQL
- AWS
Real AI features in real applications.
Architecture, model selection, prompt and retrieval design, application integration, deployment, and ongoing iteration — handled end to end under one engineer.
Senior
8+ years across full-stack and AI.
Practical
Architectures that survive real users.
End-to-End
From model layer to production UI.
Start with a brief
I'll respond with the simplest path forward.
Who this is for
AI development engagements fit teams that have a real product (or a real product idea) and need an engineer to design and ship AI features into it.
Founders building AI-first products end to end
SaaS teams adding AI features to existing software
Businesses extending an existing application with AI
Teams that need a senior AI engineering partner
Companies replacing manual workflows with AI automation
Problems I help solve
Most AI projects stall at the same step — getting past the demo and into a production system that real users can rely on.
Adding AI to an existing application
Embedding LLMs, embeddings, or retrieval into a system you already ship — without rebuilding the product.
Building AI assistants and agents
Designing assistants and agents that operate over your product data with sensible tool use and structured outputs.
Connecting LLMs to your data
Retrieval pipelines, document ingestion, embeddings, vector search, and metadata filtering for grounded answers.
Automating structured workflows
Replacing manual pipelines with AI-driven automation — qualification, drafting, enrichment, and routing.
Turning an AI concept into a production feature
Architecture decisions, model choice, prompts, evaluation, and rollout — handled end to end.
What I build
Concrete capabilities delivered inside products, presented as outcomes rather than buzzwords.
AI assistants
Conversational assistants grounded in product data, with prompts, retrieval, and tool-calling designed for production.
LLM integrations
OpenAI, Claude, and Gemini integrated into existing services, with structured outputs and stable contracts.
AI agents
Agents that plan, call tools, and operate inside bounded workflows — designed for reliability rather than novelty.
Structured AI workflows
Multi-step LLM pipelines that combine prompts, retrieval, validation, and business logic in a maintainable form.
AI automation
Lead qualification, drafting, summarisation, enrichment, and routing — wired into systems that already run.
RAG-powered features
Production retrieval-augmented generation with sensible chunking, embeddings, reranking, and citation where it helps.
Capabilities and stack
What you get when you engage me as the engineer, and the stack the work lives on.
- OpenAI / Claude / Gemini integration
- Retrieval-augmented generation (RAG)
- Prompt engineering and structured outputs
- AI agents with bounded tool use
- Next.js front-ends for AI features
- FastAPI / Node.js service layer
- PostgreSQL data modelling
- AWS deployment and operations
How AI development works
A repeatable engagement model — short enough to start quickly, structured enough to ship safely.
Problem definition
Define what 'good' looks like for the AI feature — what it should do, what data it has, and how success is measured.
Architecture
Decide on the model layer, retrieval strategy, integration boundary, and where humans stay in the loop.
Model and API selection
Pick models and APIs based on capability, latency, cost, and data-handling requirements.
Application integration
Wire the AI work into the existing product — frontend, backend, auth, and observability.
Data and context layer
Build ingestion, embeddings, retrieval, and structured context so the model gets what it needs to answer well.
Testing and evaluation
Evaluate outputs against real examples, build guardrails, and iterate before opening it up.
Deployment
Ship to production with the right deployment topology, secrets, and monitoring in place.
Maintenance
Watch for regressions, update prompts and retrieval, and keep the system reliable as data and traffic change.
Production architecture
The differences between an AI demo and production AI software are mostly engineering — and they matter.
Backend integration
AI work has to live inside a real service. FastAPI or Node.js layers handle auth, throttling, retries, and observability.
Data access
The model is only as good as the data it can see. Ingestion, embeddings, and retrieval are first-class concerns.
Structured outputs
Where it matters, models are constrained to JSON or tool calls — no parsing free-form text in production.
Tools and tool calling
Agents that need to act call well-defined tools with versioned contracts and timeouts.
Authentication and access control
AI features inherit the same auth model as the rest of the product — no new surprises for security teams.
Error handling and monitoring
Model failures, slow responses, and hallucinations have to be observable — otherwise they silently regress.
Deployment
Containerised services, environment isolation, secret management, and the ability to roll back quickly.
These are baseline engineering concerns, not exotic ones. Most AI projects fail because they're skipped — not because the model is wrong.
Relevant work
A real project where AI lives inside a production application.
Skannr — Conversational AI booking
A healthcare SaaS combining a conversational AI front-door with provider data, booking flows, and patient management.
Next.js · React · Node.js / FastAPI · OpenAI · PostgreSQL · AWS
Build Something SimilarTypical project scenarios
Scenarios I've shipped, scoped, or supported — useful as a starting point for a brief.
AI assistant inside a SaaS
An assistant that answers questions and takes actions inside an existing product.
AI feature inside an existing SaaS
A focused AI feature added to an existing product — drafting, summaries, recommendations.
AI automation of a manual workflow
Replacing a manual pipeline with an AI-driven automation that logs, escalates, and recovers gracefully.
LLM integration with structured outputs
An LLM powering a structured workflow — JSON contracts, tool calls, and validation rules.
RAG-powered product feature
A product feature that retrieves from your own data before generating a grounded answer.
AI scoring and qualification
AI scoring leads, tickets, opportunities, or content using a mix of rules and LLM judgement.
Frequently asked questions
Answers to the most common questions about adding AI to a real product.
Yes. Most AI work is integration work — wiring models, retrieval, and tool use into systems that already run. I do this without forcing a product rewrite.
Primarily OpenAI, Claude, and Gemini — selected by capability, latency, cost, and data-handling requirements. The model choice follows the problem; it doesn't drive it.
Yes. I can lead the AI work, collaborate with your in-house team, or step into an existing codebase — whichever model keeps velocity high.
Yes. I build assistants grounded in product data using retrieval, structured outputs, and tool use — designed for production rather than demos.
Yes. There's a dedicated AI & Software Engineering Support page for ongoing retainer work after a project ships.
Most start with a brief that describes the problem rather than the model. From there, I recommend the simplest architecture that fits, scope a project, and proceed.
Yes. RAG is one of the standard patterns I implement — see the RAG Development page for the full walkthrough.
Tell me what you're building.
I'll help determine the simplest technical path — a focused project, monthly AI support, or simply a second opinion on what's already been scoped.
You don't need to know the technical solution first. Describe the problem — I'll help identify the right path.