Building a data & AI infrastructure backbone

A precision-agriculture intelligence platform depended on a third-party aggregator to consume machinery data from multiple OEMs, a single point of failure that limited control over data quality and capped its ability to scale. The geospatial data flowing in wasn’t standardized either, so validation and correction happened inconsistently before reaching production reports. Soil data had the same problem from a different direction: every lab reported its tests differently, with its own column names, units and extraction methods, and uploads had to be reconciled by hand before the data was usable.

Discovery set the integration strategy, a standard data model and the architecture, with one goal: field data should arrive already trustworthy, so agronomists and growers read reports instead of questioning them. In phased, versioned releases, Magoya replaced the aggregator with direct connections to the main OEM platforms and built a cleaning pipeline that validates, geospatially matches and corrects field data before it reaches the reporting layer, on an architecture sized for millions of data points. On the soil side, a generic CSV and shapefile import canonicalizes lab-specific formats, with an LLM agent that interprets cryptic columns and proposes a mapping an operator reviews before anything is committed; approved mappings are saved as templates for future files from the same lab. A dedicated data-quality phase made the whole process transparent before imagery and reports reach end users. The platform now owns its data relationships, syncs faster and feeds its AI report engine with machinery, field and soil data that is already validated, ready for the warehouse and LLM capabilities under development.