A startup emerging out of stealth today wants to help companies understand the world’s text data using AI. The company is called Primer, and it uses machine learning techniques to help parse and collate a large number of documents in several languages for further investigation. Here’s how it works: users feed Primer’s software with a […]
A startup emerging out of stealth today wants to help companies understand the world’s text data using AI. The company is called Primer, and it uses machine learning techniques to help parse and collate a large number of documents in several languages for further investigation.
Here’s how it works: users feed Primer’s software with a stream of documents, and it then automatically generates summaries for what the system determines to be the most important information out of a haystack of data. Users are able to filter by topic, event and other categories to drill down into information Primer collected so that they can get a more complete picture beyond the automatically generated headlines.
The idea behind the software is that Primer will augment the work done by human analysts who would ordinarily be tasked with the job of wading through as many sources they can find and collating those into a similar report. It’s useful for intelligence agencies (which Primer has as customers) as well as large companies trying to understand how events logged in text impact their business.
One of the major differences with Primer’s software is that the system is capable of processing many more documents than a human could, which should provide a more complete picture. As it stands, companies, intelligence agencies and other organizations are pulling in far more data than the humans working there can process effectively.
Primer isn’t the first company to offer natural language understanding to outside organizations, but the company’s strength comes from its ability to collate a massive number of documents with seemingly minimal human intervention into a single, easily navigable report that includes human-readable summaries of its content. It’s the combination of that scale and human readability that could give the company an edge over larger tech powerhouses like Google or Palantir.
In addition, the company’s product can run inside private data centers, something that’s critical for dealing with classified information or working with customers who don’t want to lock themselves into a particular cloud provider.
In addition to the product news, Primer also revealed that it has raised $14.7 million in two rounds of funding. Data Collective (DCVC) led the company’s series A, and Primer has also received funding from In-Q-Tel, Lux Capital, Amplify Partners and others.
Primer has a contract with In-Q-Tel, an organization that helps connect the U.S. intelligence community with new technology through investment and contracting. The startup’s product is being used by several agencies within the American government, but Primer doesn’t know which ones.
Intelligence agencies aren’t the only ones interested in Primer’s technology. Walmart is an early customer, and the company sees its platform being useful for people who want to understand things like subtle changes in financial filings, or shifts in regulator statements around key policies.
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