Most AI demos break in production. I build the ones that don't.
A flashy AI demo takes a weekend. An AI feature your users trust takes engineering — retrieval that returns the right context, prompts that hold up under edge cases, guardrails against hallucination, evals that catch regressions, and cost controls so a viral week doesn't bankrupt you.
I build AI products end to end: the model layer, the retrieval and data pipeline, the product around it, and the measurement that proves it works. Model-agnostic by design, so you're never locked to one provider's pricing or roadmap.
What's included
Everything you need, built properly — no shortcuts.
Chat & copilots
In-product assistants and copilots that understand your domain and take real actions, not just answer questions.
RAG over your data
Retrieval-augmented generation grounded in your documents, database, and knowledge base — with citations.
Autonomous agents
Multi-step agents that plan, call tools, and complete workflows with human-in-the-loop checkpoints.
Workflow automation
Classification, extraction, summarisation, and routing that removes repetitive ops work at scale.
Evals & guardrails
Automated evaluation suites and safety guardrails so quality is measured and hallucination is contained.
Cost & latency control
Caching, model routing, and streaming so responses are fast and your token bill stays predictable.
The full scope
Every engagement is fixed-scope and fixed-price — you know exactly what you're getting before we start, and you own all of it when we're done.
- AI use-case discovery & feasibility review
- Prompt engineering & system design
- RAG pipeline: ingestion, embeddings, vector store
- Model integration (OpenAI, Anthropic, open models)
- Tool-calling, agents, and orchestration
- Evaluation harness + guardrails
- Cost, caching, and latency optimisation
- Full source code + handoff & support
How it works
A clear, collaborative process — you always know what's happening and what's next.
Use-case & feasibility
Week 1- Define the job to automate
- Feasibility + model selection
- Success metrics & eval plan
Data & retrieval
Week 2- Data ingestion pipeline
- Embeddings + vector store
- Retrieval quality tuning
Build & orchestrate
Weeks 3–4- Prompt & agent design
- Tool-calling integration
- Product UI around the AI
Evaluate & harden
Week 5- Eval suite + guardrails
- Cost & latency optimisation
- Edge-case hardening
Launch & monitor
Week 6- Production deployment
- Monitoring + cost dashboards
- Handoff & support window
Built with a modern stack
Proven, well-supported technologies — chosen for reliability, not novelty.
SaaS teams
You want to add a genuinely useful AI feature that retains users — not a bolted-on chatbot.
Ops-heavy products
You have repetitive, language-based work (support, review, extraction) ripe for automation.
Support automation
You want to deflect tickets and speed up your team with AI grounded in your own knowledge base.
Common questions
Everything founders usually ask before we start.
I'm model-agnostic — GPT, Claude, Gemini, and open-source models like Llama. I pick based on your quality, latency, privacy, and cost needs, and design so you can switch providers later.
Retrieval-Augmented Generation grounds the model in your own data so answers are accurate and cite sources. If your AI needs to know about your specific docs, products, or customers — yes, you need it.
Through grounding (RAG with citations), guardrails, structured outputs, and automated evals that measure accuracy on real cases before and after every change.
No. I use enterprise API tiers and configurations where your data is never used for training, and can deploy open models in your own environment when privacy demands it.
Caching, prompt optimisation, model routing (cheap models for easy tasks), and streaming. You get a cost dashboard so spend is always predictable.