AI that delivers.

We build AI solutions that work in your day-to-day — no demos, no black boxes.

What we actually build

RAG systems & knowledge bases

Your documents, tickets, SharePoints and internal wikis become searchable — via chat, with source citations. Vector stores, clean chunking, embeddings on your domain. Answers come with evidence, not gut feeling.

LLM integration into existing workflows

We hook AI into the tools you already use — CRM, ticketing, ERP. Structured outputs, function calling, clean handovers to humans. No tool switch — an extra hand inside the same system.

Custom AI agents

Agents that work tasks on their own — research, data enrichment, lead qualification, routine tickets. With tool use, guardrails and human-in-the-loop at the points where humans need to decide.

How we work

Use-case workshop

1 day

We sit down with you and look at where AI actually moves the needle — and where it would just be marketing. By the end of the day, you have a prioritised use case worth the effort.

Deliverable: Prioritised use case with success criteria

Data audit & proof of concept

1–2 weeks

Before we build anything for production, we check the data situation and run a PoC on your real data. With measurable criteria — accuracy, latency, cost per request. If the result doesn't hold up, we'll say so.

Deliverable: PoC on real data plus a go/no-go briefing

Production rollout

2–6 weeks

We move the system into production: connecting to your tools, authentication, logging, monitoring, fallback paths for when the model gets it wrong. Includes handover documentation.

Deliverable: Production system with monitoring and fallbacks

Operations & iteration

ongoing

AI systems decay quietly. We keep the system running or support your IT team — with observability, prompt versioning and regular evals so quality stays measurable instead of eroding.

Deliverable: Stable operations with evals and versioning

The stack we work with

No favourite tool that fits every problem. We choose per project — guided by the use case, data-protection needs and budget.

LLM providers

OpenAI (GPT-4/5)
Anthropic (Claude)
Open-source via Hugging Face

Vector stores

pgvector
Qdrant
Pinecone

Frameworks

Vercel AI SDK
LangChain
LlamaIndex

Hosting

EU region (for GDPR-sensitive data)
On-prem on request

Evals & observability

Langfuse
Helicone
Custom evals

Frequently asked questions

Can we use AI without sending data to the US?

Yes. For sensitive data we use EU-region deployments or open models we run on EU hosting or on-prem with you. Processor agreements and data flows are documented — no hand-waving, no shrugging it off.

How is this different from just wiring up the ChatGPT API?

Wiring up an API takes an hour. Building a system that holds up in production takes longer: evals, monitoring, prompt versioning, fallback paths, authentication, cost control. Otherwise the demo works — and production fails quietly.

What does a typical project cost?

Concrete numbers come out of the use-case workshop. Effort depends on data quality, integration depth and use-case complexity. After the workshop you get a written estimate with a fixed price per phase — decidable after each step.

What if the model hallucinates?

Three layers: RAG with source citations so answers are traceable. Structured outputs with schema validation so junk gets caught early. And human-review paths at the points where a mistake would actually hurt.

Who operates the system afterwards?

Either us or your IT team. We build so a handover is possible from day one — with documentation, a clean repo and runbooks. No lock-in that forces you onto our hourly rate.

Which use case is worth tackling first?

Book a consultation