Agriculture
Chilean agriculture — row crops, fruit growing, horticulture, and dairy — lives a contradiction: it's data-rich (soil moisture sensors, weather stations, satellite imagery, packing and milking records, per-block or per-cow costs, export certifications) yet most decisions are still made by eye or by habit. The problem isn't always a lack of technology; often there's unused software and a lack of judgment to connect what's already there. We start with a diagnostic: what data your operation generates — whether it's an orchard, a horticultural greenhouse, or a dairy herd — which decisions actually move the margin (water, feed, labor, waste, milk yield, export rejection), and where an improvement pays for itself. Only then do we decide what to build. Sometimes it's tidying spreadsheets and a couple of dashboards; sometimes it's a knowledge graph linking blocks or herd, weather, irrigation, animal and plant health, and traceability. The rule stays the same: only what earns its keep.
— ODEPA / VIII Censo Nacional Agropecuario y Forestal, 2021
The problems
Agriculture uses 73% of Chile's water, yet most farms still irrigate by calendar and spreadsheet. The data to irrigate better already exists — nobody's looking at it together.
Blind irrigation of the scarcest resource
Water is Chilean agriculture's main constraint, yet irrigation is still decided by calendar and gut feel. Soil moisture, weather, and flow data sit in separate systems nobody cross-references in time.
Data scattered across field, packing, and office
Per-block or per-cow costs, yield, milk litres, labor, and waste live in spreadsheets, notebooks, and software that don't talk to each other. Knowing which hectare — or which animal — actually makes or loses money takes weeks, too late to fix it.
Export traceability assembled by hand
Destination markets (EU, US, China) demand ever-stricter traceability and food-safety records. Today they're assembled by hand before each audit, risking whole shipments rejected over one missing record.
Our solutions
Agriculture
Diagnostic first, technology second
We map what data your operation generates and which decisions move the margin — water, labor, waste, export rejection — before proposing anything. Sometimes the recommendation is to buy no software yet and simply use what you already have well.
Data-backed irrigation and field decisions
We connect soil moisture, weather, satellite, and flow into dashboards that tell you when and how much to irrigate per block, and where the money is leaking. We implement only the instrumentation the diagnostic justifies.
Automated traceability and reporting
We unify field, packing, and certification records into a single source, so export documentation and SAG audits generate themselves instead of being assembled by hand against the clock.
Related services
Technology diagnostic and problem reframing
Pain first
Fractional leadership: CAIO + CTO
Executive judgment
Custom software, platforms, and websites
Internal or external use
Automation, agents, and applied AI
When it adds value
Data, integrations, and databases
Foundation when needed
Frequently asked questions
What do you diagnose first in Agriculture?
We start with the operational pain, usually around Blind irrigation of the scarcest resource: Water is Chilean agriculture's main constraint, yet irrigation is still decided by calendar and gut feel. Soil moisture, weather, and flow data sit in separate systems nobody cross-references in time. From there we decide whether the issue is process, data, systems, leadership, or a mix of them before proposing any implementation.
Do we need AI or a new platform to solve it?
Not by default. In Agriculture, the right answer can be a process change, integration, automation, dashboard, custom software, or no build at all. We only use AI or advanced data architecture when the diagnostic shows it will change the business result.
What could an initial project look like?
A first scope often targets Diagnostic first, technology second: We map what data your operation generates and which decisions move the margin — water, labor, waste, export rejection — before proposing anything. Sometimes the recommendation is to buy no software yet and simply use what you already have well. We keep the scope tied to one measurable outcome, clear ownership, and the capacity your team actually has to operate the change.
How does fractional CAIO/CTO support work here?
We act as fractional technical leadership for Agriculture: prioritizing risks, turning Data scattered across field, packing, and office into concrete operating decisions, selecting or coordinating vendors when needed, and supervising implementation until the change is working in the business.