AI & intelligent automation

The problem

Most companies aren't short on data. They're short on access to it.

Your product specs live in a SharePoint nobody searches. Your process documentation is spread across Confluence, email threads, and one person's head. Your support team answers the same 40 questions by digging through PDFs every week.

People have been promising that AI will fix this for years. What most companies got instead was a chatbot that hallucinates, a pilot project that never left the lab, or a vendor demo that looked great on fake data and fell apart on real documents.

We think the problem isn't AI — it's how it gets built. Most AI projects fail because nobody took the time to understand the data, the workflows, and the people who'll actually use the thing.

What we build

Retrieval-augmented generation (RAG)

Your employees ask a question in plain language. The system finds the right documents, pulls the relevant sections, and generates an answer — with sources, so people can verify. No more digging through folder structures or hoping the search bar cooperates.

We build this on top of your existing knowledge: internal wikis, document management systems, technical manuals, contract databases, support tickets. Whatever you've got, we can make it searchable and useful.

AI agents

Agents go a step further than Q&A. They don't just find information — they act on it.

Think: a supplier sends a packing list as a PDF. An agent extracts the line items, checks them against your purchase order, flags mismatches, and routes exceptions to the right person. What used to take someone half a day happens in minutes — and the human only gets involved when something's actually wrong.

We build agents for document processing, data entry workflows, approval chains, report generation, and anything else where a smart person is currently doing repetitive work that follows a pattern.

Workflow automation with AI in the loop

Not everything needs a full agent. Sometimes the biggest wins come from dropping AI into one step of an existing process — auto-classifying incoming emails, extracting key fields from contracts, summarizing meeting notes into action items.

We look at your workflows end to end and figure out where AI creates the most leverage with the least disruption.

How we work

Week 1–2: Discovery

We sit down with your team — the people who actually do the work, not just the people who commissioned the project. We map out data sources, current workflows, and pain points. We look at your data quality honestly, because that determines what's realistic.

Week 3–4: Architecture & proof of concept

We design the system and build a working proof of concept on a real subset of your data. Not a demo on curated examples — an honest test of what the system can and can't do. You see results before we go further.

Week 5–8: Build & iterate

We build the production system in sprints, with your team in the loop. Every two weeks you see working software and can steer direction. We handle the infrastructure, the pipeline, the edge cases, and the "what happens when someone uploads a scanned PDF from 2003" problems.

Week 9+: Production & handover

We deploy, monitor, and optimize. Then we either hand the system over to your team (with proper documentation and training) or we keep running it for you. Your call.

The technical side (without the sales pitch)

We use tools that are production-proven and that we genuinely believe in — not whatever has the most hype this quarter.

Embeddings & retrieval: Qdrant for vector search, because it's fast, self-hostable, and we don't have to send your data to a third party. We combine vector search with traditional keyword search (hybrid retrieval) because neither approach alone is good enough.

Orchestration: LlamaIndex for pipeline orchestration. It lets us build modular systems where we can swap out components without rewriting everything.

LLMs: We're model-agnostic. Claude, GPT-4, Mistral, Llama — we pick what fits your use case, your budget, and your data residency requirements. For clients who can't send data to US providers, we run open-source models on European infrastructure.

Infrastructure: FastAPI backends, deployed on Kubernetes. If you're already on AWS or Azure, we deploy there. If you want European-only hosting, we run it on Hetzner. Either way, you own the infrastructure.

What we don't use: black-box SaaS platforms where you can't see what's happening, modify the pipeline, or leave when you want to. Everything we build, you can take with you.

Who this is for

You probably need this if:

• Your team spends more time searching for information than using it
• You've tried AI tools that worked in the demo but not on your data
• You have compliance requirements that rule out just "plugging in ChatGPT"
• You want AI that handles real work, not just answers questions
• You've got messy, unstructured data and you're not sure where to start

You probably don't need this (yet) if:

• You don't have the data to work with — we can help you figure that out in a strategy engagement first
• You need a customer-facing chatbot for your website — that's a different problem, and honestly, most off-the-shelf tools handle it fine