AI development & integration
AI features built into
your product — not bolted on.
Most businesses don't need an AI strategy. They need one specific thing to work better — smarter search, a support tool that helps their team respond faster, a process that used to take ten minutes and now takes one. The useful AI work is usually smaller and more practical than the hype suggests.
I build AI features into existing products and tools — integrating them properly with your data, your systems, and the way your business actually works, rather than dropping in a generic solution that doesn't quite fit.
- WhatsApp bot — 100+ daily messages reduced to under 10 per week
- AI search, support tooling & image processing pipelines
- Direct API integrations — no middleware, full client ownership
The hard part isn't
the AI. It's the integration.
Getting a language model to produce an output is fairly straightforward. Getting it to produce the right output, based on your actual data, connected to your existing systems, in a way that's reliable and doesn't break when something changes — that's where most implementations fall short.
The AI features I build are designed around your specific context: your content, your users, your workflow. That usually means less off-the-shelf and more considered — but it also means it actually does what you need it to do, rather than approximately what a demo suggested it might.
You might be here because
one of these sounds familiar.
Your search doesn't understand what people are actually looking for.
Users search for one thing and get something adjacent, or nothing at all. The listings or content are there — the search just isn't smart enough to surface them. AI-powered search that understands intent and expands queries based on your actual data can make a significant difference to how useful your platform feels.
Your support team is spending too long on repetitive work.
Reading through long ticket threads to understand the context. Writing replies from scratch for issues they've solved a dozen times. Tickets that could be resolved quickly getting held up because the right information isn't easy to find. AI can handle a lot of that groundwork — so your team can focus on the decisions, not the admin.
You want AI features in your product but aren't sure where to start.
You know there's something useful here — a smarter recommendation, a content summariser, an automated workflow — but it's hard to know which approach is worth pursuing and which is more trouble than it's worth. A short conversation usually helps clarify that quickly.
You've tried AI tools but they don't connect to your actual data.
Generic AI outputs based on generic training data are only so useful. The more valuable implementations are the ones that understand your specific content, your specific users, and your specific domain. That requires integration work, not just a plugin.
You want to improve consistency and quality in your team's work.
Tone scoring, quality checks, suggested improvements — AI can act as a quiet layer of guidance that helps a team produce better, more consistent output without adding a heavy review process on top of everything.
You have a process that's slow and repetitive, and it shouldn't be.
Summarising, categorising, drafting, routing — tasks that take time but don't require judgement are often good candidates for AI automation. If there's something in your workflow that feels like it should be faster, it probably can be.
If any of these sounds like your situation, it's probably worth a conversation.
How this usually goes.
A call
30 minutes. You tell me what you're trying to improve and how things work now. I'll give you an honest sense of whether AI is actually the right approach, and if so, what a practical implementation would look like.
A plan
If we're a good fit, I put together a clear scope — what gets built, how it connects to your existing systems, and what it'll take to get there. Realistic about timelines and what the output will actually do.
The work
The same person you spoke to is the one building it. We stay in communication throughout — AI integrations especially benefit from iteration, so the process tends to be collaborative rather than a single handoff.
What this looks like in practice
Smarter search for a job board with 15,000+ live listings.
Standard keyword search on a large job board surfaces obvious matches and misses everything else. A user searching for "account manager" might never see a "client success" role that would have been a good fit — because the terms don't match, even though the jobs are similar.
The implementation detects what a user is actually looking for, expands the query based on related roles and skills within the site's own listings, and surfaces results that would otherwise be invisible. The search understands the platform's content rather than just matching strings.
The result was a meaningfully better search experience — fewer dead ends, more relevant results, and a platform that felt like it understood what its users were looking for.
Discuss a similar projectSecond example
AI tooling built into a support workflow — summaries, suggestions, and quality scoring.
Support teams spend a lot of time on work that isn't really decision-making: reading through long ticket threads to catch up on context, drafting replies for issues they've handled before, checking whether a response is the right tone before sending.
The tooling built here handles that groundwork automatically — summarising ticket history, suggesting a reply based on previous resolutions and documentation, and scoring the tone and quality of a drafted response before it goes out.
The team spends less time on admin and more time on the tickets that actually need thinking. Response consistency improved, and new team members got up to speed faster because the suggestions gave them a starting point rather than a blank page.
Discuss a similar projectThird example
Automated product photography — from photo to finished image, inside the app.
Product photography typically involves taking a photo, sending it somewhere to be edited, waiting, and getting something back that may or may not be quite right. For businesses processing a lot of products, that adds up quickly in both time and cost.
This implementation handled the whole process inside the application: a photo is taken, AI removes the background, the image is resized and processed to spec, and the finished product image is ready to use — without leaving the app or involving a separate editing step.
What used to take a separate workflow and manual effort became something that happened automatically as part of the normal process. The output was consistent, the turnaround was immediate, and the team didn't have to think about it.
Discuss a similar projectFourth example
A WhatsApp support bot that turned a daily flood of messages into a revenue channel.
A business receiving 100+ WhatsApp messages a day — order status, delivery times, product questions — was handling all of it manually. The volume was unmanageable, and the team was spending most of their day on questions that had the same answers.
Rather than an off-the-shelf bot with ongoing per-message fees and limited control, the solution was a custom integration with the Meta WhatsApp Business API — connected directly to the client's order data and inventory — so the bot could answer accurately and autonomously. No middleware, no recurring platform costs, full ownership of the channel.
An AI layer was added on top to identify opportunities and recommend relevant products during support interactions. What had been a cost centre became something that generated revenue while it resolved queries.
Read the full case studyThings people usually ask.
What kind of AI work do you take on?
Integrating AI into existing products and workflows — search, content summarisation, automated suggestions, quality scoring, classification, and data-aware features that connect to your actual systems. I'm not building foundation models; I'm building useful things on top of them, integrated properly with your business.
Do I need a large budget to get something useful?
Not necessarily. Some of the most useful AI implementations are fairly focused in scope — a smarter search, an automated summary, a single workflow improvement. The best place to start is usually the thing that's causing the most friction right now, not a broad overhaul.
What platforms and tools do you work with?
I work primarily within the WordPress ecosystem but the AI layer itself is platform-agnostic — using APIs from providers like OpenAI and Anthropic, connected to whatever systems you're running. If you have a specific setup, let's talk about it.
How do you make sure the AI output is reliable?
Careful prompt design, proper integration with your specific data, and building in validation and fallback behaviour so things don't quietly fail. AI integrations need more ongoing attention than a standard feature — I build them with that in mind from the start.
Are you available right now?
I keep a limited number of active clients so the work stays focused. Book a call and I'll give you an honest picture of availability and lead times.
What actually happens on the first call?
You describe what you're trying to improve and how things work now. I'll tell you honestly whether AI is the right tool for it, and if so, what a practical implementation would look like. No pitch, no deck — just a useful conversation.
Let's talk about
what you want to improve.
30 minutes. No pitch deck, no obligation. If there's something in your product or workflow that AI could genuinely help with, I'm happy to talk it through and give you an honest take.