Today
An agronomist waits weeks for lab results. A fertiliser plan runs on a handful of samples spread across several hectares. Every new tool means another login, dashboard, or app nobody opens twice.
Then vs Now
An agronomist waits weeks for lab results. A fertiliser plan runs on a handful of samples spread across several hectares. Every new tool means another login, dashboard, or app nobody opens twice.
One coordinate returns a full soil profile in seconds. The intelligence lands inside the system your team already runs — no new interface, no retraining, no extra login to remember.
Product
Every query returns a full chemical and physical profile — NPK, organic matter, pH, texture — translated directly into a dosage plan per zone. Not a table of numbers to interpret. An amount to apply, and where to apply it.
Soil stressors and deficiencies map onto treatment recommendations for the crop already in the ground, matched to what the profile shows rather than a generic regional average.
Seeding, fertilisation, and harvest windows sequenced against real soil and moisture conditions, not a fixed date circled on a paper calendar.
Soil data feeds straight into the system already running the operation — input costs, yield forecasting, and compliance records, without a separate platform to maintain.
The Core Engine
Most soil models treat land as a flat surface — latitude and longitude, nothing underneath. Ours adds elevation, slope, and time, so a hillside is read against physically identical hillsides, not the nearest flat field. That’s the difference between a plausible guess and a verified number.
Anchors the reading to an exact place on the north–south axis.
Pairs with latitude to fix the coordinate precisely, east to west.
Sets drainage, temperature, and formation history for that height.
Drives how differently erosion, runoff, and compaction behave.
Reads how the land has formed and shifted, not just a snapshot.
Use case
Standard surface sampling read severe, widespread nitrogen deficiency. Checked against ground truth, our system found a stable 1.11 g/kg maintenance baseline instead — and caught something the nitrogen reading never would have: 41–45% clay concentration, a compaction risk invisible from above. The fertiliser budget went to deep-ripping instead of nitrogen that was never missing.
Arid, sandy, the kind of site every playbook says needs constant heavy irrigation. Underneath, the model predicted 39.6% clay — with no local training data to lean on. The irrigation plan changed before a single root sat in standing water.
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Read by AI, seen from satellite. We are the laboratory of the world.
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We used to wait weeks for lab results before deciding where to plant. Now we get the same answer in seconds, and we trust it enough to act on it.