Finch AI is doubling down on agentic AI—this time with two specialized agents designed to enrich its entity intelligence capabilities and sharpen text analytics performance in real time.
The company today unveiled the Entity Creation Agent and the Relationship Vetting Agent, both now available inside its Finch for Text® and Finch Analyst® platforms. The goal: give customers richer, faster, and more reliable insights into the people, businesses, and organizations that matter to their missions.
“These two agents, together, are a powerful way to ensure our solutions are even more responsive to users and the entities that matter to them,” said Finch AI CTO Scott Lightner.
The Entity Creation Agent: Filling the Knowledge Gaps
The Entity Creation Agent is designed to catch what traditional systems miss. When Finch AI extracts an entity from text—say, the name of a brand-new business—but can’t find it in its existing knowledgebase, the agent springs into action.
Instead of returning a dead end, it pulls context from surrounding text, runs targeted research, validates findings with other agents, and even leverages a large language model (LLM). The result? A newly minted knowledge entry complete with summaries, related entities, and even images pulled from the web. In other words, Finch AI doesn’t just identify “who” or “what” an entity is—it builds a dossier.
The Relationship Vetting Agent: Trust, But Verify
Finch AI already boasts more than 180 million relationships in its knowledgebase with 90+% accuracy, but the new Relationship Vetting Agent is designed to push confidence even higher.
When the system detects a new relationship between entities—like a company acquisition or employment connection—the agent conducts its own research, cross-checks Finch’s internal knowledgebases, and calls in an LLM for reasoning. The outcome isn’t just a “yes” or “no”; it delivers a justification, explaining why the relationship is valid (or not). That extra layer of transparency helps users trust the web of connections Finch AI outputs.
Why It Matters
Entity intelligence is a niche but increasingly critical piece of the AI stack. Whether it’s defense, finance, journalism, or corporate security, organizations rely on entity-level knowledge graphs to make sense of sprawling, messy information landscapes.
While many AI vendors are still experimenting with agentic AI, Finch is rolling out production-ready use cases that directly improve data trustworthiness and completeness—a key differentiator in a market where hallucinations and unreliable LLM outputs remain an Achilles’ heel.
Lightner hinted that these are only the beginning: “They represent the first of many agentic AI innovations customers can expect from us.” Translation: Finch wants to set the pace for how agent-based architectures can move beyond hype into meaningful enterprise-grade applications.
The Bigger Picture
Finch AI’s move underscores a broader industry shift: the pivot from monolithic LLM-powered assistants toward modular, agentic AI systems that specialize, collaborate, and validate. Competitors like Palantir, Quantexa, and even hyperscaler-backed AI platforms are all eyeing the “knowledge graph + AI” intersection, but Finch’s agentic approach to entity creation and relationship vetting could give it an early credibility edge.
If entity intelligence is the backbone of data-driven decision-making, Finch is betting that agentic AI is the muscle to keep it strong, scalable, and trustworthy.
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