Structure. Knowledge graph + AI-native index.
Raw records don't reason. Structure is where nine thousand rows become entities, entities become a graph, and the graph becomes an index built for thinking.
STAGE 02 · RECORDS BECOME A GRAPHWhat happens at Structure
Connected sources give you reachable records; they don't give you meaning. Structure is where entity resolution decides that "Acme Industries Pvt Ltd" in the ERP, "ACME IND" in the claims file and "acme-industries" in the repo are the same noun — and links every fact each system knows about it.
On top of resolved entities we define your ontology: the entity types, relationships and vocabulary of your business. The ontology is authored with your experts and versioned like code — it is the schema of the Company Brain and one of the first artifacts you own.
Finally, the graph is compiled into an AI-native index — embeddings, structured relations and policy hooks in one substrate — built for reasoning, not just search. Retrieval answers "what documents mention X"; the index answers "what does the business know about X, and who may see it".
What gets built here
Entity resolution
Deterministic and probabilistic matching join records across systems into single entities with confidence scores.
Ontology definition
Your entities, relationships and glossary — mapped with your experts, versioned, and gated before it powers anything.
Knowledge graph
Facts as relations between entities, each edge carrying source, timestamp and permission context.
AI-native index
Vector + graph + policy in one queryable layer, tuned per workload and model-agnostic by design.
Glossary & rules
Business terms and validation rules live beside the data they govern, so agents and humans share one vocabulary.
Ontology Building capability
The full methodology behind this stage is its own engagement.
Explore →Pick your function. Own the intelligence behind it.
Discover one opportunity, engineer one capability, deliver one measurable outcome — then scale.