AI integration services are the work of adding AI features to software you already run — your CRM, your helpdesk, your internal tools, your product — without rebuilding any of it. Not a new system, not a rip-and-replace. The application you depend on keeps working exactly as it does today; specific parts of it get smarter.
Most businesses don't need a new AI product. They need the software they already trust to do a few things it can't do yet — draft the follow-up email, find the right document, classify the incoming ticket, take the next obvious step on its own. The gap is rarely the idea. It's the careful, unglamorous work of wiring a model into a system that real people already use every day, and doing it so nothing breaks. That's the work I do.
What can an AI integration do?
The point is always something concrete — a task someone on your team does by hand today that a model can do, or help do, inside the software they already have open.
Draft work, instead of blank pages — A sales rep is writing a follow-up; the model writes the first version from what's already in the CRM. A support agent is logging a ticket; the model categorises it and routes it. One model call, dropped into a screen someone uses all day.
Search that actually finds things — Your customers and your team can't find what they need in your catalog, your knowledge base, or your ticket archive. I add a search layer that understands what someone means, not just the words they typed. Same content, far easier to find.
Software that takes the next step — Not just answering, but acting: reading a new lead, classifying it, booking the meeting, writing the result back to your CRM. The routine steps run on their own; the ones that matter still wait for a person.
An assistant that knows your documents — An internal question-answering helper grounded in your real wiki, runbooks, and policies — with citations back to the source. It saves your senior people from answering the same question two hundred times.
What makes an AI integration production-grade?
The demo is the easy part. What separates a real integration from a toy is the discipline that keeps it working, predictable, and affordable once it's live.
Spending you can see and cap. Every AI feature gets a budget per call, a limit per user, and an alert when usage climbs. A cheap, fast model handles the bulk of the work; a stronger one is reserved for the hard cases — and the choice between them is made automatically. No surprise bill at the end of the month.
Speed designed in. AI calls are slow by nature. I build around that — doing work ahead of time, holding on to results that can be reused, and showing your users a fast screen instead of a spinning wheel.
Visibility into every call. Each AI call is recorded: what was asked, what information it used, what it answered, how long it took, what it cost. When something looks wrong you read the record, you don't guess.
A plan for when it fails. Models give wrong answers sometimes; provider services go down. A serious integration expects that — it retries, falls back to another model, hands off to a person, or simply declines to act. AI without a fallback is an outage waiting to happen.
What technology powers an AI integration?
Tools are chosen per project for the job, the budget, and how sensitive the data is — never picked first and forced to fit.
The models — Hosted services from OpenAI, Anthropic, Google and others for hard reasoning; open models you can run yourself (Llama, Qwen, Mistral) for bulk work or for data that shouldn't leave your walls. Often both, each used where it earns its place.
Self-hosted AI — When data is sensitive or volume makes per-use pricing expensive, I deploy open models on your own servers. The quality is close to the hosted services at a fraction of the running cost — and your data never leaves your control.
A layer that keeps you free to switch — The integration is built so you can change AI providers, or add a backup one, without rewriting your software. Being locked to a single vendor is a choice, and not one I'd recommend.
Whatever your software already speaks — The integration connects through the same interfaces your existing systems use, so it fits the stack you have rather than demanding a new one.
How does an AI integration project work?
First, a thirty-minute call. You tell me the specific task you want AI to help with. I tell you honestly whether AI is the right tool for it, and roughly what it would take in time, speed, and running cost. The output is a straight yes or no with real numbers — not "it'll be great". Free, usually this week.
Then a small working version. I build the smallest real version of the integration and measure it against your actual data — real examples, real speed, real cost. You see how it behaves before anyone commits to the full build.
Then the production build. The integration ships to a staging environment, then to production. For a higher-risk change I run it quietly alongside your live system first, so its behaviour is proven before it touches a real user.
Then we iterate, or I hand it off. I can stay on as a retained engineer for tuning, new edge cases, and model upgrades — or do a clean handover to your own team, with everything documented.
Is an AI integration right for you?
A good fit if:
- You have working software and want to add AI features to it without a rewrite
- You're paying for a SaaS AI tool and want to bring it in-house for cost or data reasons
- You have a specific job for AI in mind — search, draft assistance, classification, an agent — not a vague "AI strategy"
- You care about what it costs to run, how fast it feels, and owning what gets built
Not a fit if:
- You want AI as a marketing checkbox — that's a press release, not a project
- You don't have a specific use case yet, just a sense that "we should be using AI" — talk to me about scoping first, before any build
- You expect the model to be right one hundred percent of the time — no AI system is, and I engineer around that rather than pretend otherwise
- You're shopping for the cheapest bidder — that's a different kind of project, and not the one I do
Frequently asked questions
How is an AI integration different from building a custom AI product?
An AI integration adds AI to software you already have; custom AI development builds a new AI-first product from scratch. The two overlap, but for most existing applications the integration path is faster and less expensive — you're enhancing something that already works rather than starting over.
Which AI provider should we use?
It depends on the job. Hosted services like OpenAI and Anthropic are strong at hard reasoning; open models like Llama and Qwen are well suited to high-volume work or to data that can't leave your servers. Often the right answer is both, with each task routed to the model that fits it. I don't lock you to one vendor.
Can you run AI on our own servers?
Yes. For sensitive data, regulated industries, or to control cost at scale, I deploy open models on your own infrastructure. The quality is close to the hosted services, the running cost is much lower, and your data never leaves your control.
What about our data privacy?
Sensitive data never goes to an outside AI service without your explicit sign-off. The options are provider accounts with a contractual no-training agreement, or self-hosting the model entirely so nothing leaves your walls — chosen with you, depending on how sensitive the data is.
How long does an integration take?
A simple integration — one model call dropped into an existing workflow — is usually one to two weeks. A fuller build, where the software takes actions across several tools with full visibility into each call, is more like four to eight weeks. The first step, scoping, is a single week and tells you what the rest will involve.
Let's talk
Bring the specific workflow you want to make smarter — the email your team rewrites every day, the search that never finds anything, the ticket queue nobody can keep up with. A thirty-minute discovery call is free: no deck, no sales. I'll tell you straight whether AI helps here, which model fits, and what it would take to build.