AI where it earns its place — AI development & LLM integration

No. If the scope is clear, we start from a normal first conversation. When the problem or the return is still fuzzy, a short paid assessment settles what to build — or whether to build at all.

The highest-value targets are usually repetitive information tasks: document review, data extraction, triage and routing, first-draft generation and customer-facing Q&A over private knowledge. The practical ceiling is determined by how much error you can tolerate and how much audit trail you need — we map this for your specific workflows in a discovery session before writing a line of code.

There is no single switch — it requires layered defences. We combine retrieval-augmented generation (grounding answers in cited sources), structured output schemas (constraining what the model can say), confidence thresholds (routing low-certainty responses to a human), and a continuous eval suite that measures factual accuracy against a golden dataset you maintain. Monitoring in production catches drift before users notice.

Buy for generic tasks where a SaaS product already covers 90% of your need. Build when your data is proprietary, your workflow is unusual, or the SaaS per-seat cost exceeds a custom build within 18 months. We run a structured build-vs-buy analysis early in every engagement — if the honest answer is "buy", we tell you before you pay us to build.

We process your data under a data processing agreement aligned with GDPR. By default we use Azure OpenAI or self-hosted models so your data never trains a public model. For particularly sensitive domains — healthcare, legal, finance — we scope the architecture around on-premises or VPC-only inference from the start. Data retention policies and access controls are documented before any model sees production data.

A focused LLM feature bolt-on — prompt design, retrieval layer, evals and monitoring — typically runs €20k–60k. A full agentic workflow with multiple tools and an observable trace layer is €60k–150k+. Ongoing model costs (API tokens or hosting) depend on usage volume; we estimate these during scoping so there are no surprises after launch. A discovery call gives you a first-cut range before any commitment.