A working AI feature in your own software: text, image, code or audio generated on demand. Not isolated experiments, but an integration that fits your workflow, your security requirements and the European AI Act.
Generative AI is the umbrella term for models that produce new content from a prompt: a blog text, a product description, an image, a piece of SQL, a speech fragment or a summary of a long document. The difference from classic AI is that the output is not a score or label, but new content that is immediately usable. For your end user, this usually means a visible creation or generation interface in your product: a "write for me" button, a suggestion field, an image generator or a summariser that works in one click.
For organisations, the question is rarely "can this be done?" and almost always "how do we do this securely within our existing systems?", a question we hear from many clients in exactly those words. We build that layer: from initial use-case selection to production-ready integration with your application, your data and your compliance frameworks. We work custom, the code stays in your repo, and we also deliver an AI strategy and roadmap if you need direction first before we build.
Our typical clients are content agencies, marketing teams, e-commerce organisations with large product catalogues, creative agencies, media and publishing houses, training and learning companies, customer service organisations, and product teams who want to add an AI feature to their existing SaaS. The use cases vary widely, from automatic product descriptions to legal contract generation to question-answering bots over internal documentation, but the pattern is the same: a model that creates content, a UX that keeps the human in control, and an architecture that doesn't leak your data unnecessarily.
Most enquiries we receive fall into one of these three categories. Which one fits depends on where the generation becomes visible to your end user and how much the model needs to know about your data.
You already have a product and want to add a generative layer: a "write for me" button, a summariser, a suggestion field, a translation flow or an alt-text generator. We build it into your stack, with a prompt layer you can adjust yourself, streaming output so users don't wait for a long response, and cost monitoring so a sudden spike doesn't blow your monthly budget.
The model doesn't write from nothing; it answers based on your own documents, product data, policy rules or helpdesk history. Retrieval-Augmented Generation with a vector store, source citation and an evaluation set you can expand yourself. Related to our custom LLM integrations, but specifically aimed at generative content flows.
A custom platform or feature suite in which multiple models work together: text, image, audio and code. Often for marketing, e-commerce or media organisations producing branded content at scale. We build the orchestration between models (Stable Diffusion for images, Claude or GPT for text, ElevenLabs or similar for audio), a review workflow in which editors or brand managers can approve content, and cost controls per user, project or campaign. For public-facing output, we handle the transparency labels required by the AI Act.
A working generative AI solution, embedded in your application, plus everything around it so you can manage and fine-tune it yourself.
We work custom: no white-label SaaS, no wrapper around a chatbot product. The code lives in your repository, the choice of model is yours, and you can switch providers at any time without us having to rebuild anything.
Four patterns we often step into. If you recognise one of them, we're happy to talk through how we would approach it in your situation.
You produce blog posts, social posts, product descriptions, alt text or multilingual variants in volumes where writing by hand no longer scales. AI delivers the first draft based on your product data, tone of voice and SEO guidelines; an editor reviews and publishes.
Tickets are summarised automatically, replies are suggested based on your knowledge base, and sentiment and urgency are made visible. Your service team stays in control and makes the final decision, but handles more cases a day without quality slipping.
Quotes, contracts, audit reports or policy documents are largely filled in by a template plus AI, based on structured input. Lawyers or consultants review and finalise, rather than writing every paragraph from scratch.
You have a SaaS product and your users now expect there to be "something with AI" in it — a writing assistant, a suggestion, a summary. We build it in neatly, without disrupting the existing UX.
Job posting copy in several variants, anonymisation of CVs for more objective screening, or suggested interview questions based on a role profile. Quite feasible, but exactly the kind of use case where AI Act classification needs careful attention: high-risk when decisions about candidates are made with it.
Generating quiz questions from study material, progress coaching with personal feedback, or an AI tutor that explains content at the learner's level. Popular with training and learning companies that want to scale their course library.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →We spend half a day with your team identifying which generative application would deliver the most value. Not the most spectacular one, but the one that fits your data, your users and your risk appetite. Without a workshop we build nothing, because we too often see impressive demos that fall apart in production.
Together we choose the model (cloud API, open-source self-hosted or a hybrid), define the evaluation criteria, and draft a DPIA question if personal data is involved. At the end you have a concrete scope, a plan and the first screen flow with a working prototype prompt.
A working build every two weeks. We test not only whether the feature runs, but whether the output is good enough, using an evaluation set you help create. Hallucinations, prompt injection and output leakage are actively tested in this phase, not discovered later during an audit.
A phased rollout, starting with a small group, with monitoring of costs, latency, error rates and user satisfaction. Afterwards, ongoing management: evaluating new models, refining prompts, and expanding the evaluation set as you learn more about what works well.
What clients typically want to know before we start with generative AI.
A thirty-minute introductory call, without obligation. We listen to the use case you are unsure about, give direction on model choice and compliance, and are honest when generative AI is not yet the right solution. No sales pitch — nobody is waiting for that.
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