WASHINGTON — A new startup, Danti, came out of stealth June 21 with a search engine designed for users of geospatial data.
Danti is among a growing number of startups that are riding the artificial intelligence wave. Its search engine relies on natural language models to help users of its search engine find relevant information about places on Earth.
The Atlanta-based startup announced a $2.75 million pre-seed funding round led by Tech Square Ventures with participation from Radius Capital. Other investors include Philip Krim and Raven One Ventures, SpaceVC, Overline, Tareyton Ventures, Jordan Noone, Keith Masback and Jeff Crusey.
“The funding will be used to accelerate development of Danti’s search engine technology for deployment with U.S. intelligence agencies and early commercial customers,” Jesse Kallman, founder and CEO, told SpaceNews.
Users of the search engine, he said, can pose simple questions and get results drawn from data collected by satellites, aircraft, social media and other open sources.
Kallman decided to name the company Danti after touring the Vatican’s Gallery of Maps created by Ignazio Danti. “I was completely blown away by how a mathematician and geographer in the 1500s was able to make highly accurate maps of Italy with the tools of the time.”
NGA challenge
The company recently won a $75,000 prize challenge from the National Security Innovation Network, sponsored by the National Geospatial Intelligence Agency (NGA). The top prize was for an application that would allow non-expert users of geospatial data to quickly prioritize, analyze, and organize information into actionable intelligence.
NGA is now a Danti customer, said Kallman. The company also is working with undisclosed commercial customers.
“We are heavily focused on national security applications,” he said. Danti hired personnel with security clearances who previously worked at Palantir, Maxar, Airbus and Georgia Tech.
“I’ve worked in the unmanned systems space. I’ve worked in space based Earth observation, in a number of different industries. And I’ve kind of seen the same problem over and over specifically when it comes to geospatial content: data overload,” Kallman said.
Analysts are drowning in data and need simple tools to get their questions answered, he said. Another challenge is the significant level of expertise that is needed to make sense of geospatial data. NGA has a large workforce of analysts, but most organizations in government and commercial industries that work with geospatial data rely on small teams that source the content, analyze it and provide briefs.
“If we can reduce the level of complexity required to use it and understand it, that’s already a big win, because that opens up the amount of folks within a given organization that can use the content,” he said.
The U.S. military relies on NGA to provide intelligence analysis, but an engine like Danti’s could help forces deployed downrange get quick answers, Kallman added.
“We’re encoding decades of geospatial experience into AI based tools that can translate user queries into something that a computer can actually execute a search against,” he said.
Much of the AI investment in the geospatial data analytics industry is going into computer vision, object detection and other technologies to extract information out of an image.
“What we’re doing is complementary, providing an ability to search data sets and ask questions.”
