We Built a Self-Calibrating POI Map from Human Input, Data Agents and AI
Foursquare has developed a self-calibrating POI map that integrates human input, data agents, and AI to create a dynamic dataset of points of interest. This system evaluates contributions based on a meritocratic trust score, allowing it to continuously refine and verify place records. By weighing conflicting inputs and adjusting contributor reliability, the engine ensures accurate representation of real-world locations.
- ▪Foursquare's Places Engine combines human input, data agents, and AI to create a comprehensive POI dataset.
- ▪The system uses a modified Dawid-Skene algorithm to evaluate proposals from different contributors based on their trust scores.
- ▪The consensus engine continuously calibrates contributor reliability, boosting or penalizing trust scores based on the accuracy of their inputs.
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Resources / Blog / Products How We Built a Self-Calibrating POI Map from Human Input, Data Agents and AI Inside the FSQ Places Engine: Part 1 December 18, 2025 by Fourquare One year ago, we introduced Foursquare’s new Places Engine, a unique crowdsourcing platform that brought together humans and agents to create a comprehensive POI (point-of-interest) dataset. We built this system to find consensus from conflicting inputs and anchored it with a strong spatial foundation to ensure every POI record in our database matches the physical reality. The result is something fundamentally different from a traditional POI system: a self-calibrating, living representation of POIs that continuously reasons about every input it receives.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Foursquare.
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