Industries
AI for agtech that survives the field.
Grain moves faster than the lab. Grading drifts from one grader to the next. Connectivity dies at the collection point. We build computer vision and AI systems for agriculture that hold up in those conditions: one photo, an answer in minutes, humans on the disputed calls. Production AI since 2019, built for teams worldwide.
Who builds production AI for agriculture and agtech?
extendfuture is an AI agency working worldwide since 2019, focused on production AI for funded startups and $10M to $100M companies. For agtech that means computer vision quality control, field-ready AI products and automated procurement workflows, with human review on the calls that matter. In production: a grain classification system that counts and classifies corn and wheat kernels from a single photo, at parity with manual lab counts. Engagements start with an entry sprint, scoped on a founder-led call.
The sector problems we see
Agtech decisions happen where infrastructure is thinnest. Quality is judged by eye, and two graders disagree. The lab is accurate but slow, and the truck cannot wait. Most vision demos are trained on clean images, then fail on dust, mixed lighting and an ordinary phone camera. The pattern we see across the sector:
- Manual grading that drifts from grader to grader and site to site
- Lab results in hours while the trade happens in minutes
- Patchy connectivity at farms, collection points and mandis
- Records on paper and in photo galleries, invisible to the business
- Seasonal peaks that manual QC cannot staff for
- Pilots that worked in the demo and died in the field
What we build for agtech
The recurring shape is a judgment made by eye, at volume, in imperfect conditions. That is a computer vision problem with a human layer, and it is exactly what we build. Every service starts with a scoped entry engagement, defined on a founder-led call before work begins. The service pages are linked below.
- Vision QC products: detection, classification and counting from a single photo. AI product development; the entry point is a Prototype Sprint.
- Procurement and collection workflows: intake, grading records, reconciliation, reporting. AI process automation; the entry point is One Workflow Automated.
- Review loops for disputed samples that tighten the model with every correction. Human-in-the-loop operations; the entry point is an Accuracy Gap Assessment.
- A scored roadmap when the right first bet is not obvious. AI consulting; the entry point is an AI Opportunity Map.
Proof from production
Grain quality control meant manual counting and visual grading: slow, inconsistent between graders, impossible to scale across collection points. We trained vision models to detect, classify and count corn and wheat kernels from a single photo taken in ordinary conditions. Class boundaries were calibrated against expert graders, and disputed samples routed to human review. The full case study is linked below.
- Kernel counts and classification from one photo, in minutes instead of lab hours
- Counting accuracy at parity with manual lab counts on validation sets
- Consistent grading across locations, ending grader-to-grader drift
- A human review loop on disputed samples that fed continuous improvement
- Adjacent field proof: biometric onboarding for fintech that cut about two days of process to about eight minutes, working offline
How an engagement starts
It starts with a founder-led 30-minute call: you describe the workflow, we give an honest read on whether AI can carry it and what number it has to beat. If the answer is yes, the first step is a scoped entry engagement that ends in a working system on your real samples, not a deck. If the answer is no, we say so on the call.
- Book a 30-minute call with a founder, no sales layer in between
- Pick the entry engagement that fits: sprint, audit or assessment
- See accuracy as a number on your own samples before scaling anything
- Then decide: scale it, run it with us, or stop
What a similar project needs
Vision QC projects live or die on calibration, not on model choice. Before the first model is trained, we agree on ground truth with the people who hold it today: your graders. Here is what a grain-classification-shaped project needs from your side.
- Sample images from real conditions: dust, mixed lighting, ordinary phone cameras
- Access to the expert graders whose judgment defines ground truth
- A tolerance decision: the accuracy the trade requires and what a wrong grade costs
- A review path for disputed samples, so corrections feed the model
- An honest read on connectivity: if collection points are offline, the system is designed offline-first
- Security from day one: least-privilege access, audit logs and data residency, designed to support DPDP and GDPR expectations
Can computer vision match manual lab grading?
On our grain classification work, kernel counting accuracy came in at parity with manual lab counts on validation sets. The honest caveat: parity is earned, not assumed. It comes from calibrating class boundaries against your expert graders and running a review loop on disputed samples. We measure accuracy as a number before anything scales.
Does it work in the field, on ordinary phones, with poor connectivity?
That is the design constraint we start from. The grain system worked from a single photo taken in ordinary conditions. We have also shipped field systems that run fully offline, including a biometric onboarding flow that cut about two days of process to about eight minutes without a connection. If your collection points are offline, we design for offline.
What data do we need before starting?
Representative images from real field conditions and access to the graders whose judgment defines ground truth. We calibrate class boundaries against your experts and build the labeling pipeline as part of the project. If records live on paper today, that is normal for the sector; digitizing that workflow is often the first automation worth doing.
How long until we see a working system?
Entry engagements are scoped on a founder-led call: a Prototype Sprint, One Workflow Automated, or an Accuracy Gap Assessment. Each ends with working output on your real samples and an honest read on what production takes. Build and scale follow from there.
How do you handle grower and trade data?
Least-privilege access, audit logs on every action, PII masking before models see personal data, and data residency honored. Human approval gates sit on high-stakes steps. The posture is designed to support DPDP, GDPR and SOC 2 expectations, and your data stays in your accounts wherever possible.
Our agtech AI pilot stalled. Can you take it over?
Yes, that is a common starting point. Pilots usually die between demo and production because evals, review loops and field constraints were never built in. We audit what exists, add the missing layer, and either productionize it or tell you plainly why it will not work.
Talk to the people who build.
One call. An honest read on what AI can do for this, and the number it has to beat.