Custom AI development services

A custom AI development company builds AI features and products for your specific use case, rather than selling you a generic tool. Our custom AI development services take a scoped problem — a feature, an internal product, an AI agent — and ship it as software running in your environment, with evals, guardrails and a cost ceiling. The difference from generic 'AI development services' is that everything is built against your data, your systems and one metric you agreed up front.

Project-based, scoped and fixed before we write production code, not hourly staff augmentation. As a shape, most engagements start in the €15k–€50k range, smaller scoped builds below that, larger multi-feature products above €50k. Five things move the number: the state of your data, the number of systems to integrate, the quality bar and human review, the model path, and compliance work. The running cost ceiling for inference is a separate number we design to and enforce in code.

Yes, when the open question is whether a model can do the job on your data at all. A PoC with us is built against an eval set of real cases and ends in a written go or no-go against the metric we agreed — not a demo. If it passes, it becomes the first milestone of the MVP instead of throwaway code. If the answer is already obvious, we skip it and scope the MVP directly.

Hire a company when you need one AI feature or product shipped and measured, and don't yet know whether AI will be core to the business — it is faster than recruiting and you keep the code. Build an in-house AI team when AI is the product itself and the work is permanent; hiring comes first and takes months. A common middle path: an external team ships the first system, hands it over with a runbook, and you hire once the volume justifies it.

You see working software in your environment within the first weeks, with a demo every Friday, not a big-bang reveal at the end. A focused single feature is typically a few weeks to production; a multi-surface product is longer and broken into milestones you sign off on. We don't show notebooks and call it progress.

We don't do staff augmentation, ship demos as deliverables, edit prompts in a console with no version control, or build without evals and a cost ceiling. We don't put juniors on your project. And if a deterministic rule or a plain integration solves the problem cheaper than AI, we tell you and point you there.

AI Strategy decides what to build and the roadmap, no code. Integration wires AI into systems and data you already run. Automation strings workflows and agents together to remove manual steps. AI Development is where the production software gets written: the custom feature or product, with evals, guardrails, cost ceilings and an owned metric.

Ask to see the engineering, not the deck. Five things separate a serious vendor: a written eval plan — how quality will be measured on your data before launch; prompts and config in version control, not edited live in a console; a cost ceiling designed into the build rather than discovered on the invoice; a contract that says you own the code and the trained models; and at least one story of telling a client not to build something. Many buyers reach us after paying for AI that did not survive real work — the fastest filter is asking a vendor how they know when their own system is wrong.

Six things in writing: the metric the build will be judged on; the eval set and how it is scored; the model choice with the cost, latency and quality trade-off stated; the running cost ceiling at your real volume; milestones with working software at each one; and code ownership with handover terms. A proposal that only lists features and a day rate is selling effort, not an outcome — and effort is what stalls after the demo.

Fixed price for scoped builds. It forces the scoping work to happen up front and puts the risk of vague requirements on the vendor, who is best placed to remove it. Time and materials makes sense when the scope genuinely cannot be known yet — early research or a discovery phase — but used for delivery it rewards slow work. We price per project, fixed before production code is written, with the inference cost ceiling as a separate number we design to. A vendor who cannot fix a price usually has not scoped the problem.

A pattern we hear again and again from buyers on their second attempt: the tool worked in the demo and failed in the workflow. A document-processing system that misread too many real documents to be trusted. An estimation tool abandoned because it needed a dedicated person full-time and created more work than the manual method it replaced. The common cause is a build judged on a demo instead of a metric — no eval set on real data, no error-rate target, no design for the cases the model gets wrong. It is why we agree the metric and the eval set before writing production code.

Yes — and for some processes you should insist on it. Where an error has fiscal or legal consequences, human review of low-confidence cases is a design requirement, not an admission of failure. We build it in deliberately: confidence thresholds, an approval queue, and an audit log of what the system did and what a person corrected. Those corrections feed the eval set, so quality is measured against real cases rather than a demo.

Both, when the project needs it. Most of our work is custom AI development — LLM features, RAG, agents, computer vision and predictive models — but an AI feature usually needs accounts, permissions, dashboards and integrations around it, and we build those too, to the same standard: version control, evals and tests in CI, code you own. For projects that are mostly product software with little AI in them, the product-engineering side of our services is the better starting point.

Concrete things: custom AI features inside your product, internal tools, full SaaS products, and agents that do real work. Everything ships with evals, guardrails, and a cost ceiling enforced in code, not in a slide. You own the code and one agreed metric we move.

It's a scoped, project-based custom AI build: we agree one metric to move, then ship working code every week against it. Most engagements run €15k–€50k+ depending on scope, and you own the code at the end. No retainers for seats, no per-head billing.

Yes. We build and train machine learning models and ship them into production software, with evals to measure quality and monitoring to catch drift once they're live. You own the code and the trained models. We don't hand over a notebook and call it done.

Yes. We build the ingestion pipelines, warehouses, and feature stores that your models and reporting depend on. If your data is scattered or unreliable, we build the pipelines that make it usable before any model goes near it.

Data engineering builds and maintains the pipelines, warehouses, and feature stores that move and store data reliably. Data science uses that data to build models that predict or classify. Data analytics interprets the data to answer business questions and report on what happened. Engineering makes the data usable, science builds models from it, analytics draws conclusions from it. We do the engineering and the model-building; we leave standalone BI reporting to analytics specialists.