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Tax, agriculture, fleet, filings. Each one is a job we built end to end - agents, pipelines, vision and automations, all in production.

Build nowHow we work

Agriculture

Psyche AI

100x the resolution satellites give you, read by a model that knows the crop

Resolution vs satellite

Tax

Reaver

Five years of tax credits, recovered in hours instead of months

Of tax credits, recovered in hours

Tax

Fiscal Agent

Five years of federal tax filings, pulled without a human in the portal

Humans in the portal

Environmental compliance

Restoration platform

Turning a field photo into a defensible restoration record

To a defensible record

More work

Read the write-ups
Analytics

34% productive time. The other 66% was a truck that had not arrived yet.

A sugarcane harvest front is a convoy. A harvester cuts, a transbordo (an infield haul-out wagon pulled by a tractor) collects the cane beside it, drives to the field edge, and dumps into a road truck.

Transbordo logisticsAgriculture

Analytics

One harvester burned 3,144 liters more than its neighbors. The data knew in May.

A sugarcane mill logs a great deal: hectares cut per hour per operator, liters of diesel per refuelling, RPM, event codes for every stop, tons entering the gate. None of it is organized to answer the question a fleet manager actually asks at the end of the month: who, and which machine, is consistently worse than the others doing the same job on the same ground?

Operator performanceAgriculture

Analytics

A productivity ruler built from the people who were already best at the job

CMAA's focus was one word: productivity. The mill logs operations by sector, activity, operator and machine, but the log answers "what happened" and not "what usually happens when it goes well." Managers wanted three things the log alone could not give: a forecast of low days, an alert when one was forming, and a reference for what a good day looks like per activity and sector.

CMAA productivityAgriculture

Vision

The drone only saw red, green, blue, and heat. Here is how much that still measures.

Most of what is written about drone agronomy assumes a multispectral sensor: near-infrared, red edge, sometimes SWIR. Corteva's plot was flown with an RGB camera and a thermal camera.

RGB + thermal stressAgriculture

Pipeline

Thirty-seven vegetation indices, five years of Sentinel-2, and one question: which hectare is drifting?

A single NDVI image is a photograph. A grower needs the film: the same field, the same index, every clear day for years, so that "low" means low for this field in this month, not low compared to a neighbor with a different crop.

Satellite indicesAgriculture

Vision

Nobody has the field boundaries. Draw them from orbit.

A vegetation index without a field boundary is a heatmap with no rows. Productivity zoning, moisture anomalies, season detection, harvest forecasting: all of them are computed per field, and all of them need the polygon first.

Field delineationAgriculture

Pipeline

When did this field start, when was it cut, and how many tons will it give? Ask the time series.

A sugarcane field's NDVI curve is a saw-tooth: it climbs after planting or ratoon regrowth, plateaus, then drops at harvest. Everything a manager wants to know about the field's behavior is in the shape of that tooth.

Phenology & harvestAgriculture

Vision

Six public datasets, one duplicate trap, and a sugarcane leaf classifier that reads phone photos

Leaf disease classifiers are well studied; the hard part in practice is the data. Public sugarcane datasets are scattered across platforms, labelled with different class names, and, as it turned out, partially copies of each other.

Sugarcane leaf diseaseAgriculture

Vision

Good, damaged, clod, or wood: a detector that grades soybean grains one at a time

Two counting problems with the same shape. At the silo, a sample of grains is graded for damage and foreign material (clods of soil, fragments of wood).

Soybean grain & podsAgriculture

Agent

A language model that has read a million pages about Brazilian agriculture, and can be asked what it read

Ask a frontier model about Brazilian tilapia exports, regional sugarcane productivity, or the latest crop-insurance statistics by municipality and it will answer. The answer is a plausible paragraph with a good chance of a wrong number.

Turing agro LLMAgriculture

Agent

Drop in a spreadsheet, get back a report that ran the numbers instead of guessing them

A language model reading a table does what it does with any text: it pattern-matches. Ask for the average fare difference between men and women on a 891-row dataset and a bare model will produce a number with two decimals and no computation behind it.

Table reasoning agentsAgriculture

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