TECHNOLOGY · ONTOLOGIES
Not just search.A model of your legal knowledge.
An ontology is a machine-readable map of a domain: the things that exist, their attributes and the rules connecting them. Zaphir builds one for legal and compliance work.
01 · DEFINITION
What an ontology is, simply.
A formal, shared model that describes the things in a domain and the links between them. Not just a list of words, but their meaning and the logical rules that govern them. It answers: what kinds of things exist? What attributes do they have? How are they connected? Different people, and different software, read it the same way.
02 · ANATOMY
Four building blocks
Four elements describe almost anything.
Classes
The categories of things: Obligation, Clause, Policy, Control, Evidence, Matter.
Instances
The concrete cases of a class: a GDPR duty is an Obligation, a specific DPA term is a Clause.
Relations
The links between things: "is-counterparty-of", "secures", "is-governed-by". The real value.
Axioms
Logical rules: an obligation requires controls; a control must be supported by evidence.
03 · THE FORMAT
From a triple to a graph
Knowledge is stored as minimal three-part facts: subject, predicate, object. Millions of connected triples form a graph you can navigate.
SUBJECT
Obligation A
PREDICATE
is-tested-by
OBJECT
Control B
From one node you can hop to others: it is the multi-hop reasoning a flat list of paragraphs cannot do.
04 · WHY IT MATTERS
An LLM aloneisn't enough on your data.
Powerful on language, fragile on specific facts.
/ 01
It hallucinates
Plausible but invented answers, with no anchor to real facts.
/ 02
No sources
Hard to know where a statement comes from, or to verify it.
/ 03
Weak on links
Multi-hop questions over your internal data trip it up.
The turning point: LLMs remove the friction, the ontology keeps the structure. They are complementary, not competitors. Fluid statistics plus verifiable logic, the core of neuro-symbolic AI.
05 · HOW ZAPHIR USES THEM
A legal ontology,built from your documents.
Four ways the ontology and the LLM reinforce each other inside Zaphir.
01
Zaphir builds the ontology
The LLM reads contracts, policies, regulations and matters and extracts obligations, clauses, controls, evidence, parties and relations. A human validates: the LLM accelerates, it does not replace judgement.
02
The ontology grounds the LLM
Every term maps to a concept with a unique identifier. Generation is constrained to entities and relations that actually exist in the graph: fewer hallucinations, unambiguous terms, and every answer cited to document, page and paragraph.
03
GraphRAG, not just similarity
Retrieval runs over the legal graph, not only over similar paragraphs. Zaphir can connect a regulatory obligation to policies, controls and supporting evidence.
04
Neuro-symbolic reasoning
The LLM interprets the question while logical reasoners verify legal and compliance constraints. The answer combines language fluency with verifiable structure.
06 · ARCHITECTURE
Three layers that reinforce each other.
Zaphir does not rely on a single LLM: it combines the fluency of language, the solidity of structure and the predictability of search.
01
Neural
LLMs read the documents, extract entities and answer in natural language.
02
Symbolic
Canonical entity types and their relations form the legal and compliance ontology.
03
Deterministic
A query planner resolves many questions without inference, where the LLM is not needed: fast, predictable, low cost.
Neuro-symbolic AI, in production.
07 · THE PIPELINE
From document to entities, with sources.
A dedicated worker processes each file in the background, in two phases.
01
Document
PDF, Office, text or image.
02
Chunks
Passages sampled from the file.
03
Phase 1 · Regex
VAT, IBAN, ISIN, EUR amounts, dates, with real validators (Luhn, mod-97).
04
Phase 2 · LLM
One call, JSON, temperature 0.1: issuers, counterparties, roles, deadlines, values. It also checks Phase 1 false positives.
05
Graph
Entities and relations stored, ready for search and the agent.
The entities enter the search index: "contracts over EUR 50M" becomes a filter, not just "similar". A reranker puts the best passages on top before the agent.
08 · WHAT YOU GAIN
The sum is worth more than the parts.
01
Fewer hallucinations
Answers are bound to facts verified in the graph.
02
Multi-hop reasoning
Complex questions answered by following chains of relations.
03
Consistency & explainability
Reasoners and constraints keep data consistent and answers traceable to source.
04
Fast to build
LLMs cut construction from months of manual work to days.
90%+
GraphRAG accuracy on schema-bound queries where vector-only RAG can fail.
Days
Build time. What took months of NLP experts now starts in days.
Cited
Every answer carries document, page and paragraph of origin.
Indicative figures from 2024-2025 industry reviews and benchmarks; they depend on the implementation.