// ai · integration
AI Solutions & Integration, AI That Does the Work
Most AI projects die as a slide deck. A flashy demo, a pilot that never ships, a model nobody wired into the tools people actually use. The hard part was never the model. It's getting it into your workflow, against your data, in a way the business can trust.
That's the part we do. We build machine learning and AI into the systems you already run: predictive analytics on your numbers, custom models for your specific problem, and automation that quietly removes the manual grind. Not a science project, a working piece of your stack.
This page is for teams who want AI to earn its keep, not generate a headline. We're straight about where it helps, where it doesn't yet, and what it actually costs to run.
Integration into systems you already run
The fastest win is rarely a brand-new AI product. It's intelligence added to the tools your people are already in: the CRM, the support desk, the internal admin panel, the spreadsheet that runs half the company. We wire models into those, through your own APIs, so the value shows up where the work happens.
That might be a copilot in your support tool that drafts the reply, a classifier that routes incoming tickets, an assistant that reads a document and pulls out the fields you need. Small, specific, and measurable, rather than a vague platform you have to learn from scratch.
We build against your existing stack instead of asking you to rebuild around ours. The goal is that the AI feels like a feature of the software you have, not a separate thing your team has to remember to open.
Predictive analytics and custom models
When an off-the-shelf model can't answer your question, we build one that can. Demand forecasting, churn prediction, anomaly detection, scoring, whatever your data actually supports. Trained on your numbers, tuned to your problem, and honest about its limits.
The groundwork is data, not magic. We help you find, clean, and structure the relevant sources first, because a model trained on messy inputs gives confident wrong answers, which is worse than no answer. We're upfront when the data isn't there yet and tell you what it would take to get there.
Where deep learning genuinely fits, we use it: neural networks for the problems that need them, like complex pattern recognition over images, text, or signals. Where a simpler model does the job cheaper and more reliably, we use that instead and say so. Bigger isn't automatically better.
Automation that removes the manual grind
A lot of office work is a human doing what software should: copying fields between systems, reading the same kind of document over and over, triaging the same inbox every morning. AI is genuinely good at that now, and it frees people for the work that actually needs a person.
We target the repetitive, rules-light tasks that used to be too fuzzy to automate: extracting data from invoices and contracts, summarizing long threads, drafting routine responses, tagging and routing. The kind of thing that quietly eats hours a week and nobody enjoys.
We scope it so a human stays in the loop where the stakes warrant it. Automation that's wrong 5% of the time and unsupervised is a liability; the same automation with a quick human check on the edge cases is a real saving. We design for the version that holds up in practice, not the demo version.
MLOps: keeping it running after launch
A model in a notebook is a prototype. A model in production that you can monitor, update, and roll back is a system, and that gap is where most AI initiatives quietly fall apart. We build the second kind.
That means proper deployment, monitoring for when a model's accuracy drifts as the real world moves, retraining when the data shifts under it, and a way to roll back when an update misbehaves. The unglamorous plumbing that decides whether the thing still works in six months.
It also means observability you can actually read: what the model predicted, how confident it was, and where it's starting to struggle, so your team can trust it or correct it rather than treating it as a black box. AI you can't inspect is AI you can't rely on.
GDPR, data control, and models we can stand behind
AI runs on data, often your most sensitive data, so where it goes matters. We build with data minimization, a clear processing basis, and a design that keeps your information inside boundaries you control. GDPR is part of the architecture, not a checkbox bolted on at the end.
We're deliberate about which models we use. We build on providers like Claude, and on open-weights models we can host ourselves when the data needs to stay close. We will not quietly route your data through OpenAI; if a customer is migrating away from US-cloud AI for data-residency or trade-secret reasons, that's exactly the kind of constraint we design around. For the strictest cases we run the model on EU infrastructure or on-premise so it never leaves.
We're engineers, not your lawyers, and we won't pretend otherwise. But we hand your team a system whose data flows are documented and inspectable, which is what an honest AI deployment under EU rules actually requires.
// benefits
What AI actually gives you
Less manual grind
Repetitive, rules-light tasks get automated so your people spend time on the work that needs a person.
Decisions from real data
Predictive analytics turns your numbers into forecasts and signals you can act on, not just dashboards.
Fits your tools
Built into the CRM, support desk, and admin tools you already use, through your own APIs.
Runs after launch
MLOps for monitoring, retraining, and rollback, so the model still works in six months, not just on demo day.
GDPR by design
Data minimization, a clear processing basis, and the option to keep everything on EU or on-premise infrastructure.
Honest scoping
We tell you where AI helps, where it doesn't yet, and what it actually costs to run, before you commit.
// faq
Frequently asked questions
How do we get started with AI without wasting money on a dead pilot?
We start narrow. We look at where you actually spend manual hours and pick one task with a clear before-and-after, not a sprawling 'AI strategy'. We build that, measure it against real work, and only scale what proves out. A small thing that ships beats a big thing that stays a slide.
Which AI models do you actually use?
We build on Claude for most language work, and on open-weights models we can host ourselves when the data needs to stay close or on-premise. We pick the model for the job and the constraints, and we're upfront about that choice. We don't route your data through OpenAI; if avoiding US-cloud AI matters to you, that's a constraint we design around rather than ignore.
What data do we need for this to work?
It depends on the problem, but the honest rule is: the model is only as good as the data behind it. We help you find, clean, and structure the relevant sources, and we tell you plainly when the data isn't there yet and what it would take to get it. We'd rather flag a gap early than train something that gives confident wrong answers.
How long does it take to put an AI feature into production?
A focused integration into an existing tool can be live in one to three months. A larger custom model with serious data work behind it runs longer, often four to twelve months. We work in short cycles with something usable along the way, so you see progress instead of waiting for one big reveal.
How do we measure whether the AI project paid off?
We agree on the numbers before we build: hours saved, error rate down, response time down, whatever maps to your goal. Then we set up the measurement so you can see the before-and-after on real work. If it isn't moving the metric, we'd rather find out fast and change course than keep polishing a demo.
Want AI that does the work, not another demo?
Tell us where you spend manual hours and what data you have, and we will point out where AI does real work and where it doesn't yet.