About
Building structured semantic data for enterprise use
Syntelligo produces a multilingual concept graph covering the domains where precise, connected, and auditable terminology matters most.
What we do
A long-term investment in structured language data
The Syntelligo dataset is the result of sustained, methodical work — building a multilingual concept graph from the ground up across five enterprise domains: business, finance, health, legal, and defense.
Every concept in the graph carries a validated definition, typed semantic relations, and full provenance. Every term is anchored to a concept — not mapped to another term. The result is a dataset with structural integrity that survives language boundaries and integration into complex downstream systems.
The dataset grows continuously. It is not a static export or a legacy archive. It is maintained as a living data asset — expanding in concept coverage, language depth, and domain specificity over time.
Every entry begins with a defined concept, not a term. Terms in all languages attach to the concept — enabling clean multilingual alignment.
Definitions are reviewed and validated, not generated at scale without oversight. Confidence scores distinguish reviewed from provisional entries.
Source attribution is built into the data model — not added as an afterthought. Every definition and relation is traceable to its origin.
The dataset is designed to be exported, consumed, and embedded — not only used through a proprietary interface. Format flexibility is a first-class requirement.
Context
Why structured multilingual data is a hard problem
Terms are not concepts
The same concept can have ten different terms across a single language. The same term can refer to different concepts across domains or jurisdictions. Mapping terms to terms compounds the ambiguity. Mapping terms to concepts resolves it.
Translation is not alignment
Translating a term does not guarantee conceptual equivalence. A legal term in one jurisdiction may carry implications absent in its apparent equivalent in another. Concept-anchored multilingual data makes that gap visible and manageable.
AI needs structure, not just text
Language models trained on unstructured text inherit the imprecision, inconsistency, and ambiguity of that text. Structured concept data provides the stable, validated layer that AI systems need to operate reliably in regulated domains.
The asset
What the dataset contains
Independently defined concept nodes, each with a unique identifier, domain attribution, and at least one validated definition.
Validated textual definitions in multiple languages. Each definition carries provenance and a confidence score.
Preferred terms and variants across 20+ languages, each attached to a concept node rather than to another term.
Typed semantic relations between concepts — broader, narrower, related, and domain-specific — enabling graph traversal and inference.
Source attribution for definitions and relations, including creation context and update history across the dataset lifecycle.
Per-entry confidence scores that distinguish validated content from provisional entries — usable as a filter or weight in downstream systems.
Get in touch
Learn more about the dataset
We are happy to walk through the data model, coverage scope, and export options with qualified buyers.