We build real-world agricultural data for Physical AI.

We collect and annotate image, video, and sensor data from crops, livestock, and agricultural machinery across Latin America.

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AgroData.ai in numbers
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Egocentric video collected
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Farm partnerships
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Data collectors
Tasks we record

The data we capture.

Click any card to see what each task captures. Photos shown are placeholders, so swap in real footage anytime.

Hover to pause · Click a card to flip it

How it works

From spec to trained model.

Here's exactly what happens once you reach out.

01

You define the spec

Modality, crop, vertical, labeling criteria.

02

We activate the network

Contributors in the right regions, ready in days.

03

You watch it happen

Capture with live quality control, visible to you.

04

We label it

Your spec applied, structured for training.

05

You start training

A validated dataset, on your timeline.

Why LATAM

One region, dozens of crops.

No single country in Latin America grows everything. That's the point. Click a country to see what it's known for.

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Latin America is one of the most agriculturally diverse regions on Earth. Coffee in the mountains, soy in the pampas, cacao in the rainforest, cherries in the Andes: no single climate or crop defines it. That diversity is exactly why we built AgroData here. A model trained on one country learns one country. A model trained across twenty learns what agriculture actually looks like. Click a country to see what it grows.
Multimodal data

Beyond the frame.

Video alone can't teach a robot how the real world feels. Every session we record captures multiple synchronized signals at once, so a model learns to connect what it sees with what it would feel, hear, and sense: real world data that actually behaves like the real world.

Egocentric Video

First person footage recorded from a head mounted camera, showing exactly what the worker's hands do and see during each task.

Depth Data

Spatial and distance information captured alongside video, so a model can understand how far an object is and how a hand moves through 3D space.

Tactile and Force Data

Grip pressure and contact force recorded through sensorized gloves, capturing what a human hand feels when it picks, squeezes, or twists something.

Motion and Pose

Inertial sensors (IMU) that track how the hand, wrist, and arm move through a task, frame by frame.

Audio

Ambient and task generated sound, from a knife cutting a stem to a tractor idling nearby: a signal a model can learn to associate with an action.

Sample annotation

Labeled for what your model needs.

Every dataset can ship with the labeling layer your pipeline expects: object boxes, hand pose, or scene structure. The frames below are illustrative samples over our own footage, not a live client deliverable.

Egocentric photo of hands transplanting a seedling with a garden trowel, with sample object annotations TROWEL SEEDLING
Annotated
Object boxes, frame by frame.
Egocentric photo of a hand harvesting grape clusters, with a sample hand pose overlay FRUIT CLUSTER
Annotated
Hand pose, tracked through every grip.
Egocentric photo inside a greenhouse aisle, with sample scene annotations on plant rows POTTED PLANTS SEEDLING BED
Annotated
Scene structure, labeled for spatial context.

Sample annotations shown for illustration. Actual labeling schema is defined per project spec.

Robotics research

Why Egocentric Agricultural Data Matters for Robotics

01The Shift

From routes to manipulation

Agricultural robotics is moving past GPS-guided machines that simply follow rows. The next generation needs to manipulate: identify a single ripe fruit, judge its firmness, and execute the precise motion to pick it without damage. That kind of dexterity can't be trained on route data. It has to be trained on what the task actually looks like from the point of contact.

02The Gap

The viewpoint that's missing

Most public agricultural datasets are shot from drones or fixed cameras, built for remote-sensing work like yield mapping or NDVI analysis. None of them show what a harvesting arm or weeding end-effector actually sees: leaves partially blocking the target, light shifting row to row, a hand adjusting grip mid-motion.

03What We Capture

First-person, in-task

AgroData.ai's contributors record real harvesting, pruning, grafting, and inspection, across crop types and growing seasons, with the same visual noise a robot will face in the field: occlusion, soft-body deformation, inconsistent light, dozens of hand postures for dozens of crop geometries.

In-domain egocentric data consistently outperforms third-person or synthetic training data on agricultural manipulation benchmarks.

About us

We're from Latin America, and we believe in this.

AgroData.ai was born out of Vintti, a staffing company founded in 2022 that has spent years connecting Latin American talent with companies across the US and Canada. Along the way, we got good at one specific thing: finding real people, in real places, and connecting them to work that matters somewhere else entirely.

We're applying that same expertise to a new problem. Instead of only connecting people, we're now connecting people and agricultural spaces, the farms, greenhouses, and fields across Latin America, with the AI labs that need real world data to train the next generation of physical AI.

We're not a faceless data vendor. We're a small team from this region, genuinely passionate about data, who believes this work matters beyond any one contract.

The world's population is projected to reach 9.7 billion by 2050, and global food production will need to grow by an estimated 70% to keep up.

Source: UN World Population Prospects, FAO

Every hour of data we collect is a small contribution toward the systems that will help meet that demand. That's the part we want to play.

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FAQ

Questions, answered.

Everything we usually get asked before a first call.

Who collects the data?+

Our data comes from partnerships with farms, fincas, greenhouses, and nurseries across Latin America. Workers and growers on these operations record the tasks as part of their normal day, so the footage reflects real conditions. Every contributor signs a consent and likeness release before recording.

Do we own the data once we buy it?+

Yes. Datasets are delivered under a commercial license that transfers full usage rights, including for model training and redistribution within your product. Exclusivity is available on request.

What crops and tasks can you capture?+

Anything from row crops to orchards and greenhouse work: harvesting, pruning, grafting, weeding, and inspection. If it's not in our current library, we scope a custom shoot.

What does a dataset include?+

Egocentric video by default. Hand pose, depth, synchronized multi-camera, and force sensing are available on top, depending on the task.

Can we get a sample before committing?+

Yes. We start most engagements with a small pilot, usually 20 to 50 hours of a single task, so you can validate quality before scaling.

How is the data annotated?+

Every clip can ship with hand pose tracking, object bounding boxes, and action-level captions, tailored to what your training pipeline needs.

How fast can you deliver?+

Pilots typically ship in 2 to 3 weeks. Larger volumes depend on season and crop availability, we'll give you a real timeline on the call.

Where is the data collected?+

Across Argentina, Brazil, and other Latin American growing regions, which gives access to crop varieties and harvest windows not well represented in existing datasets.

Book a call

Let's talk about your dataset.

Pick a time that works for you. Come with a spec, or just an idea, we'll help you define the modality, crop, vertical and labeling criteria.

Email: mia@vintti.com
Based in: Buenos Aires, Argentina
Schedule your call 30 min