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Straightforward write-ups on how we scope, price and ship AI systems, and the jobs already running in production. Written for the people who own the outcome, not for the hype cycle.

Build nowHow we work
Guide

Write a great first brief

What to put in the first message so intake asks fewer questions and the proposal lands closer to the truth.

01 min readSeptember 14, 2026

Guide

From brief to live

The path Citeaxis is built for: intake, proposal, Blueprint, unlock, and optional Production.

01 min readSeptember 14, 2026

Guide

Agent, pipeline, or automation

How Citeaxis classifies the work, and why the type changes the scope and the price.

01 min readSeptember 14, 2026

Guide

Answer the stack questions well

How each of the seven answers steers the Architect, and what happens if you skip one.

01 min readSeptember 14, 2026

Guide

Run your Blueprint locally

Install, env, and the first run, following the MANUAL that shipped in the zip.

01 min readSeptember 14, 2026

Guide

Deploy your Blueprint

Take the verified zip to the hosting the Architect picked, using the MANUAL as the source of truth.

01 min readSeptember 14, 2026

Guide

Security and data handling

How Citeaxis stores your account, your thread, and your Blueprint, and what we do not train on from this product.

01 min readSeptember 14, 2026

Use case

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

Satellite imagery at 40 cm is useless for precision agriculture. Psyche AI flies the field at 4 cm across RGB, NIR, NDVI, and DVI and runs a crop-specific model that detects, outlines, and counts.

02 min readPsyche AI · Vision

Use case

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

PIS/COFINS credit recovery in Brazil is a five-year retroactive audit across three independent legal theses. Reaver audits the raw filings, calculates the credit per thesis, applies Selic correction, and formalizes the claim.

02 min readReaver · Agent

Use case

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

For each client entity, a Brazilian accounting firm needs the same documents every cycle. They live in two federal systems with no API. An agent inside Turing takes the request in chat, drives ReceitaNetBX, and returns the files with a run log.

05 min readFiscal Agent · Automation

Use case

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.

03 min readTransbordo logistics · Analytics

Use case

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?

03 min readOperator performance · Analytics

Use case

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.

03 min readCMAA productivity · Analytics

Use case

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.

03 min readRGB + thermal stress · Vision

Use case

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.

03 min readSatellite indices · Pipeline

Use case

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.

02 min readField delineation · Vision

Use case

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.

03 min readPhenology & harvest · Pipeline

Use case

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.

02 min readSugarcane leaf disease · Vision

Use case

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).

02 min readSoybean grain & pods · Vision

Use case

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.

04 min readTuring agro LLM · Agent

Use case

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.

03 min readTable reasoning agents · Agent

Use case

Turning a field photo into a defensible restoration record

Ecological restoration auditing depends on field photos judged against a taxonomy that was never built for images. We rebuilt the class list around what is actually visible, then trained classification on top of it.

02 min readRestoration platform · Vision

Reading times assume about 220 words a minute.

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