Knowledge & data
Knowledge catalog
What does that cost roughly? Does that fit us too? How does it work? Your business answers these questions every day – only none of the answers sit anywhere you could look them up.
Knowledge catalog
01Knowledge catalog
The knowledge catalog is your company's ordered knowledge base: services, prices or ranges, common questions, processes, references, particularities – as a clean structure instead of scattered across website copy, PDFs and heads. Built once, the same base works in several places: the website draws its content from it, search engines and AI assistants understand it, a companion can answer from it, and the team looks things up instead of asking. During the build the most valuable thing happens along the way: gaps, contradictions and outdated items become visible for the first time.
Straight up: this is structural work, not tech wizardry – "we'll just put RAG over it" is exactly the path I don't take. Order first, then the tools. And a catalog only lives if someone maintains it: owners and an update rhythm are set in the project, otherwise even the finest base ages.
02Demo
The catalogue of the fictional “Lichtwerk Signage” firm. Ask a question – and look under the answer to see where it comes from: each one stems from a maintained block, not out of thin air.
This is what the catalogue looks like from inside: knowledge blocks with clear fields – and three output channels fed from SEVERAL blocks. If a price changes, it changes everywhere it's needed.
Ask a question in the “try it out” tab – the block the answer came from lights up here along with its channels.
03How it works
- 01
Gather the knowledge
Website, PDFs, price lists, email replies, client questions: what exists where? Even the knowledge passed on only by word of mouth comes onto the table.
- 02
Set the structure
Which kinds of knowledge do you have – services, FAQs, prices, processes, references? Each gets clear fields: "it's somewhere in the text" becomes "it's in field X, maintained by Y".
- 03
Fill it and tidy up along the way
Filing it all reveals everything: the FAQ answer that contradicts the quote PDF, the price from two years ago, the service with no description. Exactly these finds make the catalog valuable.
- 04
Connect what reads from it
Website pages, structured data for search and AI, companion knowledge, an internal look-up view – the base is tapped wherever it creates value.
- 05
Anchor the upkeep
Who updates what, on what rhythm, with what review date? Small and realistic – better one area that holds true than ten that go stale.
04Stages
The catalog grows area by area – no one structures their whole company in one go.
Entry: one area of knowledge
Services plus FAQ as a clean structure document or CMS model – the core everything else aligns to.
Extended: the full catalog
All areas of knowledge structured, technically marked up on the website – closely interlocked with the AI-ready upgrade.
Full build: the living data base
The catalog feeds the companion, AI visibility and internal use – with a maintenance process and owners.
05Technical implementation
- The store suits you: a Markdown structure, CMS collections, Airtable/Notion or a database – what matters is clear content types with fields.
- Typical kinds of knowledge: services, FAQs, prices/ranges, references, target groups, processes, decision criteria.
- The output is manifold: website pages, structured data (Schema.org), companion context, an internal knowledge file.
- AI helps with sorting, gap-finding and phrasing – the factual accuracy you check.
- Vector search (RAG) only comes in at genuinely large scale – usually the clean structure with direct context is enough.
- Every area of knowledge carries an owner and a review date – upkeep is part of the model, not an afterthought.
for context · This shows how something like this usually gets built – not how it has to be built for you. What actually fits is worked out in the project: around your existing setup, your team, and what stays easy to maintain.