AI Integration Services for Websites & Business

Transform your AI vision into production-ready software. Applied LLMs, agentic workflows, and AI automation deeply integrated into your website and enterprise business tools, engineered to ship and stay running.

What I build

  • → LLM-powered website features: intelligent chat, content generation and summarisation
  • → Autonomous agentic workflows that automate complex multi-step business processes
  • → RAG (Retrieval-Augmented Generation) pipelines over your own documents and data
  • → Integration with OpenAI, Anthropic (Claude), Google Gemini and other model providers
  • → AI-assisted internal tools and dashboards that connect your existing data and workflows
  • → Production deployment, monitoring and ongoing optimisation of AI systems

How an engagement runs

Most AI projects fail at scoping, not at implementation. The interesting question is rarely whether a model can do the task, it is whether the task is worth automating and what happens on the cases the model gets wrong. So the work starts there.

  1. Scoping. We agree what the feature does, which cases must never fail, and how success is measured. You get a fixed scope, a timeline and a projected monthly running cost before any code is written.
  2. Proof of concept. A narrow version running against your real data, usually inside two weeks. Real data is the point: demos built on sample content tell you nothing about your edge cases.
  3. Production build. The engineering that separates a demo from a system: structured output validation, retries and backoff, rate-limit handling, caching, spend caps, logging and evaluation.
  4. Launch and iterate. Shipped behind a flag, monitored on cost and quality, tuned against what users actually ask rather than what we guessed they would.

Stacks I integrate with

Model providers are called over HTTPS, so integration is rarely limited by your stack. In practice the work lands in one of three places: a PHP or WordPress backend where a small custom plugin holds the server-side calls, a JavaScript or TypeScript application on Node, Astro, Next.js or similar, or a standalone service that your existing site calls. Whichever it is, keys stay on the server and never reach the browser.

For retrieval, that means a vector store sized to the corpus rather than to the marketing material: for many business document sets, Postgres with pgvector is enough and avoids another vendor in the stack.

A worked example

A common shape: a company has several hundred product documents, specifications and installation guides, and a support inbox answering the same twenty questions about them every week. The build indexes those documents, retrieves the handful of relevant passages per question, and has the model answer strictly from them with a link back to the source document.

What makes it work in production is the boring part: refusing to answer when retrieval finds nothing relevant, showing which document an answer came from so the reader can verify it, and routing anything about pricing or warranty to a human. An assistant that says "I do not know" accurately is more useful than one that is confidently wrong, and considerably cheaper than one that creates support tickets instead of closing them.

Production-ready, not prototype

Most AI demos are impressive. Most AI in production is fragile. I build with the full software engineering discipline that makes the difference: structured outputs, error handling, rate-limit resilience, observability and cost management. Your AI features stay reliable as usage grows.

Available worldwide. Working remotely with businesses and enterprises of all sizes.

Frequently asked questions

How long does an AI integration take?

A scoped feature such as document search or a support assistant over your own content is usually four to eight weeks from kickoff to production. A proof of concept that answers whether the idea works at all is often two weeks. Multi-step agentic workflows take longer because most of the work is in the failure paths, not the happy path.

Can you add AI to an existing PHP or WordPress site?

Yes, and it is one of the more common requests. Model providers are called over HTTPS, so any stack that can make an outbound request can integrate with them. On WordPress that usually means a small custom plugin holding the server-side calls rather than a page builder widget, so the API key never reaches the browser.

Which model provider should we use?

It depends on the task, and the answer changes every few months. I build against an abstraction so the provider is a configuration choice rather than a rewrite, then benchmark two or three candidates on your actual data before committing. Being locked to one vendor is the expensive outcome.

What does it cost to run once it is live?

Model usage is billed per token, so cost scales with traffic and with how much context each request carries. I size this during scoping with a projected monthly figure, and build in caching, prompt trimming and a spend cap. Retrieval done well usually cuts per-request cost because it sends less irrelevant context.

Will our data be used to train a model?

Not on the enterprise and API tiers of the major providers, which exclude API traffic from training by default. Where data cannot leave your infrastructure at all, the options are a self-hosted open-weights model or a provider with a regional processing commitment. This is a scoping decision, not an afterthought.

What if the model gets something wrong?

It will, so the design has to assume it. Retrieval grounds answers in your own documents and cites them, structured outputs are validated before anything downstream trusts them, and anything with real consequences goes through a human confirmation step. The engineering question is not whether the model errs but what happens when it does.

Ready to start?

Tell me about your project, whether it's a new site, a shop, or improving what you already have.

Let's talk