Traditional databases store isolated records. Linga's Knowledge Graph understands how everything connects—who works where, who invested in what, who knows whom.
Knowledge graphs give AI employees the context to understand your business—not just search it
They connect the dots across your business. "Find LPs who've co-invested with us before" requires understanding: LP → invested in → Fund A, Fund A → co-invested with → Fund B, Fund B → managed by → us.
Traditional databases can't traverse these relationships. Knowledge graphs are built for it.
They're grounded in real entities and relationships, not guessing. When asked "Who approved this deal?", they query the graph for actual approval relationships—not generate a plausible-sounding name.
Knowledge graphs anchor AI responses in facts, not probabilities.
They remember past work and evolve over time. Every interaction, document, and relationship is stored in the graph—building a persistent memory that grows with your business.
They're not starting from scratch every session. They learn.
Linga's Knowledge Graph stores your business as entities (people, companies, deals, documents) and relationships (who works where, who invested in what, who knows whom).
When you ask an AI employee a question, it doesn't just search for keywords—it traverses the graph to understand context and connections. This is how it can answer complex questions like "Who at this portfolio company knows someone at our target acquisition?"
The graph is schema-free and flexible—it evolves as your business does. Add new relationship types, new entities, new context. No rigid database schema to update.
Watch them reason across relationships in real-time.
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