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builds the API that standardizes fragmented agricultural data from platforms like John Deere Ops Center and Climate FieldView, serving enterprise customers including Syngenta and Bayer. Their mission: make agricultural data accessible at scale—enabling analysis across millions of acres.

The Challenge: Desktop GIS couldn't scale

Leaf needed to visualize large agricultural datasets for development and customer support, but desktop GIS tools weren't cutting it. Performance issues meant crashes on datasets over 1GB—a non-starter for analyzing a million acres at once. Data prep required extensive scripting just to import CSVs with embedded coordinates. And collaboration meant screen-sharing calls or static screenshots with no way for stakeholders to explore data independently.

The Solution: Multiplayer spatial analysis with �鶹��

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Leaf's new workflow is simple: export data as CSV, GeoJSON, or Parquet, then drag-and-drop into �鶹��. The "aha moment" came when the team discovered �鶹�� could parse CSVs with embedded geo-data directly—no scripting required.

Instead of being botttlenecked by in-person screen-sharinges, the team now distributes maps withsends shareable links, viewable from any device. With data flowing from S3, MongoDB, and Postgres, Leaf has a unified analytical environment on AWS. Using �鶹�� AI, they're prototyping customer-facing analytics widgets without engineering support.

The Impact: Hours saved, hires avoided

• Analysis time reduced from hours to minutes. Tiling big datasets from cloud sources and building interactivity into the map used to take multiple tools and hours of work – and now they’re a couple of prompts away with �鶹�� AI.
• Avoided hiring a web-GIS engineer. �鶹�� gives PMs self-service mapping capabilities.
• Backend team refocused on core API. Engineering hours go to product, not visualizations.

Looking ahead, Leaf is exploring �鶹��'s new to combine large scale geo processing and analysis  with the power of building maps, apps and dashboards in seconds.