We automate costly and complex online intelligence gathering activities.
What We Do
FilterLabs.AI was founded to help organizations connect with audiences, markets, and communities where they live and work. From the beginning, we sensed that organizations don’t need another analysis of social media chatter. Instead, we target alternative data sources so you know exactly what matters not just in your city, but in individual communities and regions around the world.
FilterLabs.AI scours the world’s online communications to make sense of local communities’ needs, issues, and understandings of the world. By fusing Natural Language Processing (NLP) models tailored to these communities and local economic and social indicators, we provide actionable intelligence to help organizations achieve their objectives.
Our Story
2016
Working at the University of Virginia, Jonathan started to experiment with ways to capture granular community-level data where social media platforms were silent, and pioneered ingestion strategies to understand what local communities care most about. A fundamental insight was born: to understand what people care about and who they trust, organizations, governments, and campaigns needed to turn off the social media feeds and reconnect with audiences, markets, and communities where they live, work and play.
2020
While living in Berlin, Jonathan started to imagine ways to apply the same data ingestion and analysis strategies to help a broader set of organizations, companies, and agencies in the US understand what matters most to those they are trying to serve. He realized that organizations desperately needed a new way to understand and connect with communities in all parts of the world. So over the course of COVID lockdowns, he developed a prototype to capture and analyze community-level data from the micro-neighborhoods of Brooklyn to the most remote corners of the globe.
2021
Seeing several broad use cases for community-level data, Jonathan partnered with David to form FilterLabs in order to scale these solutions globally. Our first customers had previously been spending money tapping into Twitter and social media platforms to find nothing new, or paying consultants and analyst firms to give their opinions. The results, however, proved unreliable, limited and narrow. Connecting with the right audience, market and community was too important for such limited understandings. FilterLabs was built to help these organizations, companies, and agencies reconnect with what matters most in their communities and markets.
2022
FilterLabs launches Russian-language capabilities to provide analysis of discourse and behavior in Russia.
2023
FilterLabs secures $5M seed round.
2024
FilterLabs launches Talisman — a global, AI-powered, data analysis product.
About Our Founders
Dr. Jonathan D. Teubner
Chief Executive Officer
Jonathan has served in a number of leadership capacities in academia, business, and non-profits and has pioneered the application of machine learning to improving peace building methods. At the University of Virginia, Jonathan led a collaborative team of data scientists and scholars across the social sciences to create NLP tools. Previously, Teubner was Co-Director of Global Covenant Partners and Economist at Lehrman, Bell, Mueller and Canon in Washington, DC. Since 2021, Jonathan has been Research Faculty at the Human Flourishing Program at Harvard University, where he leads the Social Connectedness Research group. Teubner has a Masters degree from Yale and a PhD from Cambridge and has held fellowships at the Sorbonne in Paris, the Humboldt-Universität zu Berlin, and Yale.
David P. Smith
Chief Technology Officer
David most recently served as Director of Engineering at A Cloud Guru. Previously, he was VP of Technology at Linux Academy. David has gained extensive experience in full stack development, agile methods, dev-ops, management of security and engineering processes, and team leadership in a wide range of tech startup scenarios. His experience ranges from cloud-based application development, database management and security administration, to Natural Language Processing corpus collection and training. In his academic career he specialized in language and literary theory. David has Masters degrees from Princeton Seminary and Yale.