Industries · Agriculture
Banao builds and deploys AI for working farms and agribusinesses — crop disease detection, yield forecasting, irrigation control, and livestock monitoring — for plantations, contract-farming aggregators, and food processors.
Each system below runs against real ground truth: field cameras, soil and weather sensors, satellite passes, and the buyer's own price feeds. We hand over deployed systems, not a research deck.
CP Plus— existing farm cameras turned into an AI feed that flags crop and livestock events.
Each of these has a cost attached — lost crop, wasted water, a missed price, or a vet call made too late. We start where that cost is measurable.
Vision models that read leaf, canopy, and fruit images from field cameras, drones, or a scout's phone, then flag disease and pest pressure early enough to treat a block instead of a whole field.
Models over weather, soil, satellite NDVI, and past harvest records that put a defensible number on what each plot will produce — so procurement, storage, and contracts are set before harvest, not after.
Soil-moisture and weather models that tell each zone when and how much to water, wired to pumps and valves where the hardware allows, with a manual override the farm manager keeps.
Vision and sensor models that track herd movement, body condition, and early signs of illness across sheds and open ground, so a vet call happens on day one rather than day five.
A record that follows produce from plot to buyer — lot, grade, treatment, and cold-chain readings — so a recall is a query, not a guess, and export paperwork stops being filled in by hand.
Models over mandi rates, arrivals, weather, and export signals that give traders and FPOs a forward view on price, so a sell-or-store call rests on data instead of a hunch.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The deployments are live; we don't publish a figure before it is verified in the field.
CP Plus
CP Plus cameras already watch sheds, gates, and field perimeters on many sites. Banao adds a vision layer on top of that hardware — flagging sick or stray livestock, intrusion, and crop-area events — rather than asking a farm to buy and mount a second camera network.
A multi-estate plantation group
A plantation operator running estates across several districts scouted disease on foot and guessed yield from last year's harvest. Banao combined satellite passes, weather, and geo-tagged field photos into a per-block disease and yield model the estate managers check each morning.
Banao is a ~300-person engineering company, and it runs on the same AI it builds for clients before that AI ever ships. InterviewGod screens the engineers we hire. Vikaas runs the demand generation that fills our own pipeline.
A model that has to hold up against our own hiring and growth every week reaches your fields already hardened. We are not describing production AI from the outside — we depend on it to run the company.
Plenty of vendors will sell a model into any field. We would rather flag the cases where it won't pay back — that candour is why agronomy heads take the second meeting.
You have likely been pitched 'AI for agriculture' before. We start by pricing the problem on your land, not by quoting a platform.
On the ground where it helps. You leave with a ranked list of AI opportunities across your crops, herds, or supply chain, a baseline ROI for each, and an honest go/no-go — yours to keep. Proceed, and the Sprint fee is credited against the build.
Data first, model second. We assemble the field, sensor, satellite, and price data into a pipeline you own, then build the model and wire it to your pumps, cameras, or ERP.
Rollout with a manual override and a dashboard your managers actually open, plus training for field staff. The model retrains as each season's data comes in.
We farm at small scale. Does AI still make sense?
Sometimes not, and we'll say so. Below a few hundred acres with one crop, a skilled agronomist is often cheaper than a model. AI starts paying back when you have scale, multiple sites, or a problem a human can't watch around the clock — disease across thousands of trees, or a herd of several hundred.
Our fields have no sensors or cameras. Can we start?
Yes. Most farms we meet are barely instrumented. We don't need a sensor on every plant — we start with what reads the field cheaply: satellite passes, weather data, and a phone camera in a scout's hand. Instrumentation grows only where it pays for itself.
We tried a farm-tech app and it never got used. Why is this different?
Most agriculture tools die because the field team doesn't trust the screen. We build the override and the daily view around how your managers and agronomists already work, and training for field staff is part of the delivery, not an add-on.
How do we prove ROI before committing a budget?
That is the job of the AI Discovery Sprint — two weeks, fixed price, and you keep the ROI model whether or not you continue. Worst case you have a costed assessment of your own operation; best case you have the business case for your board.
How fast can a system reach the field?
A common path is a 2-week Sprint, a 6–8 week build, and a rollout that tracks the season. With a ~300-engineer bench, work starts in weeks rather than the months a single local hire would need.
Bring your worst crop loss, your highest water bill, or the price call that keeps you up. In 45 minutes we'll map the AI opportunity and the ROI behind it.
Book a 45-min scoping call