Service 02
Most company knowledge sits in unstructured files nobody can retrieve. We classify it, cut the ROT, and build the retrieval, RAG, and workflow layer that turns stored data into working knowledge.
Who this is for
Decades of documents, three generations of folder structures, and nobody can find last year’s contract. Search fails because the data underneath is a mess.
The chatbot demo was great, then it hallucinated in front of a client. AI is only as reliable as the data and guardrails underneath it.
Clients ask ChatGPT and Perplexity for recommendations. If those engines cannot read you, you are not in the answer.
We map what you have, label it, and remove the redundant, obsolete, and trivial. The result: less risk, lower cost, and data AI can actually work with.
Search that understands meaning, not just keywords. AI answers grounded in your own documents, with sources your team can verify.
Repetitive knowledge work, automated: document processing, report drafting, triage, and the glue between your tools.
A company copilot that answers from your own knowledge base. Your team asks in plain language, gets grounded answers with sources, and stops digging through folders.
Contracts, invoices, applications, and inbox triage turned into structured data. Extraction pipelines that cut processing time from hours to minutes.
How do ChatGPT, Perplexity, and Google AI describe you today? We measure it, then fix it: schema, llms.txt, and content structure that machines quote.
Access rules, audit trails, and deployment policy aligned with the Swiss nLPD and the EU AI Act. AI your lawyers and your clients can live with.
Ongoing evals, hallucination and drift checks, and cost monitoring for the AI systems you run. Reliability is not a launch feature; it is a practice.
Structured data, clean schemas, and content formats that AI search engines can read, quote, and recommend. Your expertise, findable by the machines your clients ask.
How we work
01
We map your data estate: what exists, where it lives, who can access it, and what share of it is redundant, obsolete, or trivial. You get an honest picture before anything gets built.
Data map and prioritized roadmap
02
Classification, cleanup, then the systems on top: retrieval and RAG pipelines, internal assistants, document intelligence, and the workflows that connect your tools.
Working systems on your own data
03
Evals, hallucination and drift monitoring, cost tracking, and governance. We keep the systems reliable as your data, team, and the models themselves change.
Monthly reliability and impact report
Ways to work together
Fixed scope
A fixed-scope assessment of your data estate and AI readiness, ending in a prioritized roadmap. Useful on its own, and the foundation for everything after.
Project based
A defined system, delivered: a RAG pipeline, an internal assistant, an intake automation. Scoped, built, tested, and handed over working.
Monthly retainer
Ongoing evaluation, monitoring, and governance of the systems in production. The recurring layer that keeps AI trustworthy after launch.
ROT stands for redundant, obsolete, and trivial data. It typically makes up the majority of company storage. It slows retrieval, poisons AI outputs, raises storage cost, and creates compliance risk. Cleaning it is the first step toward reliable AI systems.
Retrieval-augmented generation lets AI answer questions using your own documents instead of guessing. Your team gets accurate answers grounded in your data, with sources, instead of generic model output.
Most AI disappointments are data problems wearing an AI costume. Tools bolted onto messy, unclassified data produce messy, unreliable answers. We fix the foundation first, then build on it, and we monitor what we ship.
No. The services stand alone. But they reinforce each other: clean infrastructure surfaces insights worth publishing, and published expertise attracts clients who need infrastructure.