An ontology is the shared map of what things mean in your business: what counts as a customer, how a product relates to a contract, when a risk becomes material. Modern LLMs draft the generic scaffolding of that map in minutes. What they cannot draft is how you actually define and decide things, and that is the part we develop with you, through TextDistil, as the foundation of your knowledge graph.
Ask a capable LLM for an ontology of your domain and it returns something impressive in minutes: entities, relationships, textbook definitions. In our experience that gets you roughly two-thirds of the way. It is genuinely useful. It is also the same two-thirds anyone in your market can generate.
The draft covers what is publicly known about your industry: standard entities, common relationships, textbook definitions. Necessary and fast, and largely identical for every organization in your market.
A model reproduces what the world has already written down. Your pricing logic, your exception handling, your hard-won distinctions are not in its training data, so they are not in its draft.
A generated ontology reads well, but nothing in it is anchored to how your business operates. In a regulated setting, an unreviewed definition of a customer, a supplier, or a material risk is not a style issue. It is someone else's assumption operating under your organization's name.
The remaining third rarely exists in any document a model was trained on. It is how your organization defines, decides, and handles the cases that do not fit, and it is what makes a knowledge graph trustworthy enough to act on. In our experience, the organizations that build this layer outperform the ones that skip it. It is also hard to retrofit: the longer it stays implicit, the more decisions run on the draft's assumptions instead of yours. The work compounds for whoever does it first.
What an active customer, an approved supplier, or a material risk means in your organization: stated precisely, with the boundary cases decided rather than assumed.
The relationships that drive real decisions: which approvals depend on which conditions, which records must never be merged, which distinctions your regulators expect you to maintain.
Much of this exists only in your experts' heads and in edge cases handled over the years. Making it explicit is the work: written down, it stays with the organization instead of walking out the door when people move on.
Take a common question in regulated procurement: "Which of our suppliers are approved for regulated work?" The draft and the finished ontology answer it very differently.
Answers from a textbook definition of "approved": reasonable, and the same answer anyone in your market would get.
Answers from yours: audited within the agreed window, no open findings, contract current, and the two legacy records that must never be merged kept apart.
Same question. Only one answer you would act on.
We use LLMs for the first two-thirds too. There is no reason to hand-build what a model drafts well. We keep the scope to a minimal viable ontology, built around the decisions it must support, and spend expert time only where it changes the outcome.
We generate the scaffolding from your documents and industry standards, so your experts never spend a workshop on what a model already knows.
Structured sessions with your domain experts turn implicit knowledge into explicit definitions, rules, and relationships: reviewed, decided, and written down.
The ontology is tested against competency questions: the real questions your team needs answered. It is done when it answers them, not before. Only then does it become the blueprint for the graph.
On its own, an ontology is a document. Applied through TextDistil, it becomes the structure of your knowledge graph: the documents and data you already have, connected into one queryable layer where every answer carries a citation back to its source.
TextDistil reads your documents against the ontology and extracts the entities and relationships it defines, so the graph reflects your business rather than a generic template.
Questions are answered from connected facts, not loose text search, with a full trail back to the source document. That is what makes the results dependable in regulated work.
Because the final third is explicit, domain rules live in the graph instead of in every analyst's head, and every new document strengthens the same structure. This is the difference we see between knowledge graphs that compound and pilots that stall.
Still choosing an architecture? The free AI Architecture Choice tool compares six retrieval architectures against your requirements in a couple of minutes.
Try the AI Architecture Choice tool →A 30-minute conversation about your domain: which decisions an ontology should support, where your definitions live today, and what capturing the final third would take. No deck, no pitch.