Building a New Kind of AI Platform

Across industries, agencies, and individual organizations, leaders are tasked with making high-value, high-stakes decisions. To do that, they need access to data and insights – even as there’s an ever-growing amount of data available for them to explore. They need those insights in real time. They need them to be actionable. And they need to be able to trust the results they get from the platform they’re using. 

We built the Finch AI platform specifically for these professionals. It’s a real-time, high-fidelity decision-making platform that uses AI to deliver fast, accurate and trustworthy insights. The platform’s foundation is built on data fusion, data transparency and data enrichments – from there we can support a host of capabilities and integrate into a variety of user applications.

Data fusion involves unifying vast amount of data from many different places, whether it’s real time streaming data – meaning that the second something is broadcasted, we’re taking in the transcription and enriching the entities mentioned in it – or whether we’re doing that on social content or static content that a user has. The magic is in the fusing of these disparate, and often large, datasets and surfacing insights about what’s in it. 

Data transparency – or, what we prefer to call our trust engine – is about being able to rely on not only the data, but the insights that are being provided by the data. We “show our work.” So for instance, if we’re showing a user the sentiment of a particular news item, a user can trust that what we tell them is accurate because we’re showing them a sentiment score and way we arrived at that score. To use another example, if we’re showing data about an event taking place or area around the event, a user can trust that we’ve correctly identified the geographic entities around that event and returned the accurate metadata about them. In this case, we show a user a confidence score and the specific elements in the text that led us to return our score. 

Those data-backed data enrichments are what enable us to quickly and accurately surface connections in data that are not readily apparent – and AI helps us do that even quicker and more accurately. 

Finch has always been really good at entity intelligence – extracting an entity from text, disambiguating it from other entities and then showing a user everything we know about that entity and its connections. We’ve done that well and with a high degree of confidence – and without relying on a business rules-driven approach or manual analyst reviews.

Today, we’re able to leverage the power of AI to, essentially, supercharge what we do. We’re taking in millions and millions of documents and data points every single day, and we’re fusing all that together. And it’s really the AI that we’ve built out over the last couple of years that enables us to do that.

Our customers, for instance, in the defense and intelligence space use Finch AI to access real-time data and generate insights that enable decision makers to get up to speed very quickly on information that’s taking place on the battlefield. Customers using Finch AI for media intelligence depend on it to get information about individuals, events, or trending topics on social media, and then to make strategic decisions about how to manage, engage or shape public dialogue. Our customers in the financial space – think quant funds or organizations looking to generate alpha – will use our platform to take in that information and use a lot of the sentiment that we built out to understand when they should make trades. In each of these examples, our users are looking through millions of data points and cutting out the noise to find that one signal that’s going to make an impact on what they’re doing.

There’s exceptional power in our platform and there’s power in tuning it for a specific customer’s use and that’s one of the best things about what we do. We give them information about something they’re interested in; if that’s another organization, we tell them who they are, who they’re interacting with, who they’re associated with, any trending information about them, the sentiment about them in the news, for example. 

We can pinpoint geospatial features around their headquarters, look at mentions that are taking place in the same area, or find individuals or organizations or events in the same area. Users can map out an area of the world that matters to them and get alerted about events there. 

Looking ahead, the AI revolution shows no signs of slowing – and neither do we. We’ve been working on generative AI for a while as a means to accelerate and enhance our summarization capabilities. We are going to continue creating very advanced functionality but presenting it in a way that’s even simpler for end users. Our biggest focus going forward will be on continuing to give people better access to the insights that they need to do their jobs. 

And we’re just getting started. 

To learn more about the Finch AI platform, please visit



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