A knowledge graph
of your brand.
Vector search states wrong things about your catalog fluently. A graph gives the engine the real relationships — this product, this dose, this claim — so AI answers about you are accurate, not just confident. GraphRAG over Neo4j, built and operated.
Fluent and wrong is the default failure mode.
Ask an assistant about a specific product, dose, or contraindication and it will answer confidently — from whatever similar-looking text it retrieved. For a brand, and especially a regulated one, confidently wrong is worse than silent. The cause is architectural: the engine has no structured model of you to reason from.
Large models are pattern-matchers over text. Point one at your category and it retrieves passages that look relevant — a reseller's page, a forum post, a competitor's comparison — and produces an answer that reads authoritative whether or not it's true about you. There is no fact-check step; fluency is the only thing optimized.
The fix isn't "more content" or a better prompt. It's giving the engine a structured truth it can traverse — your products and their actual relationships — so retrieval pulls connected facts instead of similar-sounding text.
An assistant can only be as right about your brand as the structure it can read. With no ontology, it infers from prose and PDFs. With one, it looks the answer up. That single shift is what a knowledge graph buys you.
Embeddings retrieve what's similar. A graph retrieves what's true.The GraphRAG case, in one line
The ontology, the retrieval, the proof of provenance.
Four parts of one operated system: a typed model of your brand, GraphRAG retrieval over it, a sourced-claims layer, and an asset you own outright.
An ontology of your actual brand
We model the entities that define you — products, ingredients, dosages, claims, certifications, differentiators, the relationships between them — as a typed knowledge graph. Not a pile of documents; a structured truth an engine can traverse and reason over.
Schema · entities · relationshipsGraphRAG retrieval over the graph
Retrieval-augmented generation that walks graph relationships instead of just fetching similar text. The model answers "which of your products is right for X, at what dose, with what contraindication" from connected facts — so the answer is correct, not merely plausible.
GraphRAG · Neo4j · grounded retrievalA claims and provenance layer
Every claim in the graph is structured and sourced, and sensitive assertions carry a provenance trail. For regulated categories this is the difference between an answer you can stand behind and a fluent guess you can't.
Sourced claims · provenance · audit trailAn owned, portable asset
The ontology is yours — a substrate that outlives any single model or platform and feeds every future AI surface: assistant answers, your own site agent, search. Built once, it compounds; take it in-house whenever you want.
Your IP · portable · model-agnosticWhy the graph, not just embeddings.
Both retrieve context for the model. Only one encodes the relationships that make a brand answer correct rather than merely plausible — which is the whole job in regulated categories.
| Traditional RAG | GraphRAG (PlanePaper) | |
|---|---|---|
| Retrieves | Similar text chunks by vector match | Connected facts by walking the graph |
| Knows relationships | No — relationships are implicit in prose | Yes — entities and edges are explicit |
| Multi-hop questions | Weak — stitches across passages | Native — traverses linked entities |
| Brand accuracy | Fluent, often wrong on specifics | Grounded in your structured truth |
| Best for | Broad Q&A over a document corpus | Accurate, regulated, entity-level answers |
Want the buyer-facing version of the outcome instead of the mechanism? See AI Brand Visibility.
The graph is the asset every other system reads from.
It isn't a side project — it's the substrate. Visibility, content and agents all get better when there's a structured truth underneath them.
Accurate citations
The visibility system can only get you named correctly if there's a correct model to cite. The graph is that model.
Grounded publishing
The operated Content Engine writes from the ontology, so what it publishes is sourced and consistent — not improvised per post.
Trustworthy answers
Any agent you run on your own site answers from the same grounded truth — the reason it can be trusted in front of customers.
Questions, answered straight.
What is a brand ontology?
A structured model of your brand as entities and relationships — products, ingredients, claims, dosages, differentiators — that a machine can read and reason over, rather than the marketing prose and PDFs an AI engine has to guess from.
It's the difference between handing the model facts and hoping it infers them.
How is GraphRAG different from regular RAG?
Traditional RAG retrieves similar text by vector match and lets the model stitch an answer from passages. GraphRAG retrieves connected facts by walking a knowledge graph — entities and the relationships between them. For questions about your brand specifically, that's the gap between "fluent" and "accurate," and it's why vector search alone confidently states wrong things about catalogs.
Why does a supplement or peptide brand need this specifically?
Because the answers are regulated and relational: this peptide, at this dose, with this contraindication, under this claim. A fluent-but-wrong answer about a health product is a liability, not just a miss. A graph encodes those relationships so the assistant's answer is right — see Peptide & Hormone Brands.
Is this the same as AI brand visibility?
It's the defensible layer underneath it. AI brand visibility is the outcome — being named by assistants. The brand ontology is the substrate that makes the naming accurate and durable rather than a content trick that decays. You can pursue visibility without a graph; you can't make it correct and defensible without one.
Do I own the graph, or are we renting it from you?
You own it. The ontology is 100% your IP and portable — it's model-agnostic and platform-agnostic by design, so it survives any single vendor and can move in-house whenever you choose. Most clients keep us on to operate and extend it; that's a choice, not a lock-in.
See where AI gets your brand wrong today.
Then give it the truth to read.
The $1,500 AI Brand Audit shows you exactly where the engines misstate you — and whether a brand ontology is the highest-ROI fix for your catalog.