Custom AI for problems off-the-shelf tools don't solve.
Document understanding, predictive models, computer vision, decision support. We design, build, and deploy AI systems tailored to your specific business, then hand them over running, with documentation and a path to bring them in-house if you want to.
You've probably tried the obvious things first
You've looked for off-the-shelf software. You've talked to a SaaS vendor or two. Maybe you've tried a generic AI tool to see if it would work. And you've come to the conclusion that nobody has built exactly what you need, because what you need is specific to your business.
Your documents have a structure no one else's do. Your decision process depends on factors no public model has been trained on. Your data is yours, and the value is in the patterns inside it that no off-the-shelf tool can see.
That's where custom AI is genuinely worth it. The remaining problems (the ones that are specific to your business) are exactly the ones where custom AI now has the strongest ROI, because the foundation models are good enough that you no longer need to spend a million euros to apply them to your problem.
Four things we build
Document understanding
For documents that off-the-shelf OCR doesn't handle: industry-specific forms, contracts, technical drawings, scanned legacy archives. We build models that extract the data you actually need into structured fields you can use.
Predictive models
Demand forecasting, churn prediction, equipment failure forecasting, fraud detection, customer lifetime value. Trained on your historical data, validated against business outcomes, monitored for drift.
Computer vision
Quality control, asset recognition, inventory tracking, safety monitoring. From a few hundred labeled examples to deployed-on-the-shop-floor in weeks.
Decision-support systems
For complex decisions that happen often and matter a lot: pricing, capacity allocation, route optimization, scheduling. The AI shows you the recommended decision and the reasoning; you decide whether to take it.
Every model we build is monitored after deployment. We watch for accuracy drift, retrain when needed, and tell you when something's stopped working, before you find out from a customer.
What it actually takes
Problem definition
We sit with you to understand exactly what you're trying to predict, classify, or decide, and what success looks like in business terms (not model accuracy).
Data and feasibility
We look at your data and tell you honestly whether what you want is feasible, what accuracy is realistic, and what data we'd need that you don't have yet.
Build and validate
We build the model, validate it on held-out data, and pilot it on real workload alongside your existing process. You see whether it's actually better.
Deploy and monitor
Once it's proven, we deploy into production, set up monitoring, and write the documentation. Most clients keep us on a retainer for monitoring and improvement; some bring it fully in-house after 6–12 months.
Our team
1 lead · 1–2 ML engineers · 1 MLOps engineer for deployment
Your side
A sponsor · a domain expert who can tell us when the AI is wrong · a data owner
When custom AI makes sense, and when it doesn't
The problem is specific to your business
The data is yours
A small accuracy improvement has high value, because volume is high or stakes are high
No off-the-shelf product comes close enough
A SaaS solution already exists at a reasonable price and works on your problem
You don't have data
The value of the decision is small
You're trying to use AI because everyone's using AI, not because the problem needs it
We'll tell you which case you're in. If the answer is "buy the SaaS," we'll say that. It's cheaper for everyone.
What changes for you
A 70-person Hungarian industrial services company spent significant time pricing complex maintenance contracts. Their senior estimators used a mix of historical data, gut feel, and customer-specific factors to arrive at a quote, but the process was slow (3–5 days per quote), inconsistent across estimators, and the win rate was hard to predict.
Average quote turnaround
Down from 4 days
Win rate on competitive bids
First six months after deployment
We built a pricing model trained on 5 years of past quotes and outcomes. It now suggests a price range with a confidence interval and a probability of winning at each price. Senior estimators still make the final call, but they have a data-backed starting point. The model identified contracts where they were systematically overpricing.
Free 30-minute discovery call
Tell us what you'd want AI to do. We'll tell you honestly whether it's a strong fit for custom work, whether off-the-shelf would actually serve you better, or whether the data isn't there yet.
Book a discovery call