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Your AI has your data. Now give it your meaning.
Folaint builds the your AI checks before it answers: grounded in your industry’s open standards, not guessed from your data. Your experts approve every fact. You own the result.
Planning buildings or building services? Then Building design or TGA is the sharper entry point.
Three questions that cost days today
Three shapes of question, from any industrial company. The pattern is the same every time: what takes days today is answered in seconds, and every part of the answer links to the document it came from.
Each of these takes ten minutes. Once you know where to look.
Illustrative examples. Names, figures and documents are fictionalised.
Every common format, connected into your knowledge graph.
PowerPoint, Excel, PDFs, SAP and the tools you already run on: Folaint reads the formats and systems your teams live in and turns them into one governed knowledge graph. New connectors land with every release.
All product names and trademarks are the property of their respective owners. Names are used to describe data-source compatibility and do not imply any affiliation or endorsement.
The three questions above, tried against the usual fixes.
The two common fixes are a context file (instructions in the prompt) and RAG, top-k nearest-neighbour retrieval over vector embeddings. Both help. Neither survives the three questions you just saw.
The model is not too weak. The question is nowhere written down whole.
“Which delivered machines contain valve V-220?”
Vector search ranks. It doesn’t enumerate.
The usual fixTop-k nearest-neighbour search returns the 10 to 20 most similar passages: it maximises relevance, not completeness.
What it needsA set-membership query: traverse the bill-of-materials graph and return all 47 machines, with proof there’s no 48th.
For a product recall, “probably most” is a wrong answer, and no tuning of k changes that.
“How much already hangs on Brandt, across everything?”
Retrieval is extractive. This is a computation.
The usual fixThe €4.2M figure is written in no passage. Extractive retrieval returns only text that already exists.
What it needsResolve three supplier names across three systems to one group, then add up the volume: entity resolution plus a graph join.
An embedding can only return what was written down somewhere, and this number never was.
“Finance says 14%, Sales says 19%. Which margin is right?”
A prompt is context, not a constraint.
The usual fix“Net of rebates” is just tokens in the context window; the model can weight the Sales table higher and answer 19%.
What it needs“Margin” resolves to one typed metric: a canonical formula with an owner, a version and rules every answer is validated against.
Instructions the model may ignore aren’t governance, and no one keeps a thousand of them current in a file.
Context files and RAG give your model more text. Folaint gives it verified facts, complete relationships and enforced definitions it can query.
Your AI can’t reason over rows. It can reason over a map of meaning.
A gives your AI entities, relationships and rules it can follow, anchored in your industry’s published standards, not just patterns inferred from past data.
A list knows what is in it. A graph knows what follows from it.
Live graph: drag any node, hover a dashed one to see what stands behind it.
What the words actually mean.
Standards-anchored, not precedent-based.
It captures distinctions that never appeared in your data but that regulation requires. An and rules make the meaning explicit and checkable.
Open, not a private dialect.
Built on standards you could read, export and keep, never a black box only we can interpret.
- Knowledge graph
- A network of entities (customers, contracts, orders) connected by typed, named relationships. Unlike database rows, every connection carries a defined meaning an AI can follow and reason over, not just a foreign-key join.
- Ontology
- A formal vocabulary for a domain: the entity types, their properties and the relationships permitted between them. Think of it as a schema for meaning, not just for data: “a Contract must have a counterparty of type Customer.”
- Triple
- The atomic fact in a knowledge graph: subject, relationship, object. “Contract 2024-117, has counterparty, Acme GmbH.” Extraction from your documents runs in two passes, entities first, then the relationships between them, and every triple stores the source document, page and timestamp it came from.
- Competency question
- A concrete question the graph must be able to answer, collected with your experts before anything is built: “which suppliers are single-source for a critical part?”. Each one is stored with a test query. After every change to the ontology, all of them run again, a regression test for meaning.
- SHACL
- Shapes Constraint Language: a W3C standard for writing rules that data in the graph must satisfy. Machine-readable and machine-checkable: if a compliance rule says every supplier must carry a risk rating, SHACL will flag any that don't.
- Open standards
- The W3C publishes the full specifications for RDF, OWL and SHACL. Any developer can read, audit and implement them independently. Your data model is never proprietary. You can export it, inspect it, and take it elsewhere without our involvement.
Two assets you own. Every door they open.
At the end of the process you hold a that maps your domain and an that governs it. Both in open formats, running on your infrastructure. Below are the capabilities they hand you, regardless of which AI layer you choose next.
Entities, relationships, and connections your AI can traverse and reason over.
The definitions, rules, and governance layer that makes every fact in the graph trustworthy.
Power any AI stack
Your LLM, pipeline, and agents all draw from the same governed graph. Connect new tools as you adopt them. The meaning you built is reusable across every layer.
Audit and certify every output
Every graph-derived answer carries a reasoning path back to the source document and the expert who confirmed it. Compliance reviews and regulatory audits become a lookup, not an investigation.
One record across every system
Entity resolution gives every customer, product, and asset a single canonical ID. Your CRM, ERP, and support desk stop disagreeing. Master data governance becomes a side effect of the graph.
Swap models, keep your work
The captures what your domain means. Change LLM providers, upgrade versions, or add new reasoning engines. Your investment in the graph survives every model generation.
Open formats, full portability
The graph and are stored as standard RDF/OWL. Export them, host them on your own infrastructure, hand them to a new partner, or migrate them at any time. No proprietary format holds you.
A compounding knowledge asset
Every expert review sharpens the . Every resolved entity makes the graph denser. Unlike statistical model weights, this asset encodes deliberate choices and accumulates value as your domain evolves.
Most companies build on rented intelligence: when the model changes, meaning disappears. Your Knowledge Graph and Ontology stay. Both yours, in open formats. Rent the intelligence anywhere. The meaning you own, with us.
Intelligence you can rent anywhere. The meaning stays yours.
And how does this get into your company?
Five steps, an automated pipeline, and your experts at exactly three approval gates. The explainer video and the machinery behind each step sit on their own page.