AI Development
Custom applications built on modern LLMs — retrieval over your own data, structured extraction, evaluation harnesses and the unglamorous plumbing that keeps it reliable.
RAG pipelinesEvalsAPIs
Freelance AI development & consulting
Ship real AI, not another proof of concept.
I design, build and deploy production-grade LLM applications, autonomous agents and automation for teams who need working software — not a slide deck.
Agentic AI · Context Engineering · Harness Engineering ·
Built to run in production
5+ yrsSoftware engineering
3 YrsAI systems shipped
4 wksTypical first release
100%Transparent pricing
Services
What I build for you
Four ways to put AI to work — scoped tightly, priced up front, and delivered as software your team actually owns.
AI Consulting
Strategy with a technical spine: where AI pays off in your business, what it will cost to run, which model fits, and an honest read on what to skip.
RoadmapsModel choiceCost modelling
Automation & Agents
Autonomous workflows that read, decide and act across your tools — with human checkpoints, retries and audit trails wherever the stakes call for them.
Tool useWorkflowsGuardrails
Chatbots & LLM Integration
Assistants that answer from your documentation, your product and your policies — embedded in your site, your app or the chat tools your team already lives in.
Support botsSlack & webVoice
Why smartai4u
A senior engineer, not an agency layer
You talk to the person writing the code. That is the whole difference.
Ten years of shipping
A decade of production software before the AI wave, so the systems I hand over are tested, observable and maintainable long after launch day.
Weeks, not quarters
Small scope, fast feedback. Most engagements put something usable in front of real users inside the first fortnight, then iterate from evidence.
Custom, never templated
No resold boilerplate. Every build starts from your data, your constraints and your users, and the source is yours to keep and extend.
Transparent pricing
Fixed-price milestones agreed before work starts, with running model and infrastructure costs estimated up front. No hourly surprises.
Process
How an engagement runs
Four stages, each with a clear exit point. You can stop, extend or hand the work to your own team at any boundary.
-
Discover
A working session on the problem, your data and your constraints. You leave with a written scope, a fixed price and an honest verdict on whether AI is the right tool.
Week 0 · free call -
Build
Short iterations against a real dataset, with an evaluation set from day one so improvements are measured rather than felt. You see progress weekly.
Weeks 1–4 -
Deploy
Shipped to your infrastructure with monitoring, cost controls, rate limits and rollback in place. Full handover of source, docs and architecture notes.
Launch week -
Support
Optional retainer for model upgrades, prompt and accuracy tuning, and new features as usage teaches you what people actually need.
Ongoing · optional
Selected work
Case studies
Placeholder projects showing the shape of a typical engagement — swap in your own once the first clients are live.
Sample case study
Support copilot for a B2B SaaS team
A retrieval assistant over 4,000 help articles and past tickets, drafting replies inside the existing helpdesk with a human approving every send.
42% of tickets deflected
Sample case study
Document intelligence for legal ops
Structured extraction across contract PDFs into a reviewable table, with confidence scores and a side-by-side source view for every field pulled.
9 hours a week returned
Sample case study
Autonomous research agent for sales
An agent that enriches inbound leads from public sources, scores fit against the ICP and writes a briefing note into the CRM before the first call.
3× pipeline coverage
Contact
Let’s scope your project
Tell me what you are trying to build. You will get a straight answer on feasibility, timeline and cost — usually within a day.