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AI built with control in mind

We help teams add AI to existing products and internal workflows where there’s a clear use case.


In our own projects, AI supports selected parts of the work, while the final responsibility stays with the team.

An abstract illustration showing a chain of connected elements - a code snippet, a compass, and a verified checkmark - representing an AI workflow moving from data to a validated outcome.

Problem first. Then AI

A model can be easy to add. The harder part is making it work well inside the product and with the data already there. We look at the workflow first and decide whether AI improves it enough to justify the added complexity.

Where AI earns its place

We choose AI when it solves the problem better than conventional software. We look at the workflow, the data and the risks first, then decide where AI actually belongs.

People remain accountable

AI can generate or suggest changes, but responsibility stays with the team. Anything that reaches production is reviewed by a person, with closer review where mistakes would have a bigger impact.

Your data stays under control

Before the project starts, we define what AI is allowed to access. For sensitive workloads, the model can run inside your infrastructure and work with the data there.

Honest about limits

AI is fast, and often confidently wrong. We validate outputs instead of trusting them, and we tell you what's proven versus what's still a prototype.

What we build with AI

  1. AI assistants over your dataAsk questions across your company data

    Query your own information in plain language without searching through spreadsheets or asking different departments. Each answer is verified against the source.

    From our work: an internal assistant running entirely on-premise, answering HR, finance and admin questions on our own hardware. Setting up the model was relatively straightforward. Most of the work went into cleaning the data and connecting the sources.

    • Document intelligenceFind what matters in long documents

      AI can scan a long document, pull out the information that matches defined criteria and link each finding back to the passage it came from. Reviewers can go straight to the parts that need their attention.

      From our work: we built a local tool that reviews publicly available tender documents and flags the relevant passages for further review.

      • AI-enabled design & productGet to an early prototype faster

        Our designers use AI to explore familiar product flows and turn structured requirements into early prototypes faster. It works especially well when the problem follows patterns the tools already understand.

        The product direction still comes from research and design judgement. When the interface needs a more original approach or a build-ready design, the work continues in the hands of the designer.

        • AI across the lifecycleBuilt into how we deliver

          We've run a full delivery cycle with AI agents handling the implementation and compared it with our existing SDLC. What we learned now shapes how AI supports our delivery work, while engineers remain responsible for what reaches production.

          AI-generated code goes through the same quality gates as any other code. It helps us move faster on the tasks where it works well, while the standards for review and release stay the same.

          A man standing in the office in front of the Kellton sign, wearing a black shirt and glasses.

          Sebastian Spiegel

          Backend Development Director

          Thinking about adding AI to your product?

          Tell us what you're working on and where AI comes into the picture. We'll look at the product, the data and the technical constraints, then tell you where we think it makes sense to use it.

          Start a conversation

          AI on your project, under a formal policy

          Our client AI policy defines how AI tools can be used during delivery, what they are allowed to access and where human approval is required. The same rules apply throughout the project.

          Talk to us about AI

          • Isolated environment

            AI tools work in a sandboxed workspace with access only to the files needed for the task. Production systems stay outside that environment, and publishing code remains a human action.

          • Monitored & logged

            AI requests go through a central gateway, where usage can be monitored and recorded. Sensitive data is anonymised before use, and access is limited to approved models.

          • Tiered human review

            The level of autonomy depends on the work being done. Prototypes can run in isolation, while production code is reviewed and approved by a person before it merges.

          • Decisions on record

            Architecture decisions and the reasoning behind them are documented in a form the team can review later. For material changes, there is a record of why the decision was made.

          What we never do

          • We don't train models on your data.
          • We don't send production data or credentials to AI tools.
          • We don't let AI act outside the permissions agreed for the project.
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          AI choices affect where your data goes

          Sensitive data changes the architecture around AI. When data needs to stay inside the client’s environment, the model can run there as well, with access limited to what the product actually needs.

          We decide on that setup early. Where the model runs affects how it connects to the existing system and how easy it will be to replace or change the technology later. Keeping those options open matters, especially in products that are expected to stay in use for years.

          In a world where everyone has access to AI, understanding your business and protecting your data is the advantage.

          – Michał Kopacki

          Senior Director - EU Practice Leader

          FAQ

          Questions, answered straight.

          • What can you build with AI?

            We build AI assistants over company data and document intelligence tools, add AI features to existing web and mobile products, and use AI to automate selected workflows. AI/LLM tools are also part of our software delivery process, supporting selected stages of development while engineers remain responsible for the final result. We’re open to other use cases where AI has a clear role and can solve a real problem.

          • Can you add AI to an existing product?

            Yes. AI can be added to an existing product without rebuilding the whole system around it. We first look at the current architecture, data and workflow, then define where an AI feature can fit without disrupting what already works.

          • How do you handle client data when using AI during a project?

            Our client AI policy defines what AI tools can access before the work starts. Sensitive and personal data is anonymized before use, while production data and credentials stay outside AI tools. The agreed rules are documented for the project.

          • Can AI models run on our own infrastructure or on-premise?

            Yes, where the model and use case support it. We already run an internal AI assistant entirely on-premise on our own hardware. For client projects, the setup depends on the data involved, the existing infrastructure and the requirements of the product.

          • What AI experience does Kellton Europe have?

            Our AI work builds on more than a decade of delivering digital products. Today, that includes an on-premise company assistant, document intelligence tools, AI features in existing products and an end-to-end software delivery cycle run with AI agents. This hands-on experience helps us judge where AI is useful, what it takes to introduce it safely and when a conventional solution may be the better choice.

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