Relationships, Topics and Events: Without Them, Your AI is Incomplete

The surge in generative AI’s popularity has set off a subsequent rush to integrate its capabilities into workflows. As a result, today many organizations are working to identify generative AI’s use cases and to articulate its value to their organizations. But absent a solution that can identify relationships, topics and events – and insights surfaced from all three – many of these AI initiatives will fall short, or at the very least be lacking in the depth, breadth and fidelity that will make them standard practice for the long haul.

Here’s why:

There’s Value in the Content Ingestion Step

Often thought of as mundane, content ingestion is a critically important step in any AI initiative.  We live in a world of endless data and boundless possibilities. Endless because we are constantly generating data, most of it unstructured. Boundless because with that type of rich, context-laden data, your organization should be able to generate all sorts of transformative, actionable insights.

Rather than using language models or static databases alone, at Finch AI we leverage a massive entity knowledge base, curated datasets, and relationships surfaced from both to derive answers. We then take all three of these things and combine them with traditional NLP capabilities to generate results that are based in context, that are accurate and that are trustworthy.  And that makes us different. No one else is doing it this way.

AI Done Differently is AI Done Diligently

Our approach involves doing AI differently, yes, but also marrying it with powerful and accurate text analytics capabilities like proprietary entity extraction methods that allow us to isolate millions of entities and the relationships between them from enterprise-scale volumes of text – and in real-time. This allows our customers to see the kinds of relationships that exist among entities in large corpuses of data. This means an analyst can focus their efforts on the most compelling or concerning relationships that are surfaced. This capability is an asset to anyone working to cultivate, manage or exploit relationships between entities of interest.

Our focus on entities has driven a lot of innovation on behalf of our customers. We use a massive entity knowledge base and novel extraction and enrichment capabilities to find relationships as well as the topics and events relevant to each entity. This is possible because we also use sentiment and key phrase extraction to understand the context with which an entity is mentioned in text, enabling us to map them to topics or events of interest to an analyst. When an analyst can extract entities, topics and events they can gain a much more comprehensive understanding of the data they have – a critically important asset in an environment where more data than ever is thrown at them and where the pace of that data deluge will only accelerate.

All of This Levels-Up Your AI

The Finch AI approach ensures your data is primed for use in myriad AI applications. It ensures you extract the maximum possible value from your AI investment—and from the data you already have.

It’s like a great coach who never wastes a rep in practice—every step in the process is intentional, every motion is optimized, and nothing is left on the table. We don’t just process data; we develop it, refine it, and extract every ounce of insight possible to give you an edge.

Not approaching AI this way only increases the chances that something critical will be missed—and if your mission is diligence, risk management or supply chain security, not missing is the whole ballgame.

That’s why Finch AI leverages our unique approach to AI to identify every facet of data we can, uncovering relationships, events, topics, and more that sit just beneath the surface. Every step matters. Every insight counts. And that’s why our approach delivers more. To learn more about how we do it, visit www.finchai.com