Agriculture · Livestock monitoring
Banao builds AI livestock monitoring that watches herd movement, body condition, and feeding behavior around the clock — surfacing a sick or distressed animal on day one, when treatment costs a fraction of what it does on day three.
The system runs on cameras and sensors already on or near your sheds, sends alerts to the people who need to act, and keeps a manual override with every stockperson. It is deployed on working farms, not described from a demo video.
CP Plus— existing shed and yard cameras converted into a livestock health and movement monitoring feed.
Each capability below targets a measurable cost: a vet bill, a production loss, a manual patrol you are paying for with labour instead of data.
Vision models over shed and pasture cameras that flag abnormal gait, prolonged isolation, and inactivity before clinical signs appear — so a vet call happens at the first behavioural signal, not after visible decline.
Body condition scoring and behavioral pattern analysis that surface an off-animal on day one. The model runs against your herd's own baseline, so it learns what normal looks like for your breed and conditions rather than a generic dataset.
Sensor and vision feeds that track which animals eat and drink, and flag when an individual's intake drops below threshold. A reduction in feed consumption is often the earliest indicator of illness — caught here before it becomes a clinical case.
Continuous count from shed and yard cameras so a missing or escaped animal is a notification, not a morning manual check. Reports by pen, time period, and headcount history are available without a manual tally.
Activity and behavioral signals that identify optimal breeding windows across a large herd, reducing missed cycles and improving conception rates without requiring a stockperson on watch through the night.
Temperature, humidity, and ammonia sensor readings that trigger ventilation and management alerts before heat stress or air-quality risk cuts daily gain or lays a batch flat. Wired to controls where the hardware allows.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The systems are live — we don't publish a number before it is verified against production records.
CP Plus
CP Plus cameras already watch sheds, yards, and perimeters on many sites. Banao adds a vision layer specific to livestock health and movement — flagging isolation, gait anomalies, and headcount exceptions — without asking the farm to install a second camera network.
A poultry integrator
A broiler integrator running multiple shed complexes relied on manual morning checks for mortality and health scoring. Banao deployed camera-based headcount and behavioral monitoring across the network, with alerts sent to farm managers before the daily walk.
Banao runs a ~300-person engineering firm on the AI it builds for clients. InterviewGod handles our own hiring screen before it handles yours. Vikaas drives our own demand-generation pipeline. A system that has to survive our own operation every week reaches your farm already hardened.
When we say a livestock monitoring system should surface a sick animal on day one, we apply the same standard we hold our own AI to — if it cannot perform reliably enough for us to depend on it ourselves, it does not ship.
A camera on a shed is not always the right answer. Here is when we would say so before you commit a budget:
We don't quote a monitoring system from a spec sheet. We look at your shed layout, your current camera coverage, and your biggest recurring health cost first.
We walk your sheds, audit your current camera and sensor coverage, map the monitoring gaps AI would close, and hand back a costed go/no-go — yours to keep. If you proceed, the Sprint fee is credited against the build.
Camera and sensor integration, model training on your animals and your shed conditions, and dashboard configuration. Alert thresholds are set to your protocols, not a generic template pulled from a different species or climate.
Rollout with stockperson training and a 90-day performance review. The model retrains as your herd's baseline data accumulates across production cycles.
We have a small herd. Does livestock monitoring AI still apply?
Probably not at the entry level. Below a few hundred animals on a single site, a good stockperson covers health monitoring more cheaply than a camera network. AI starts paying back when the herd is large enough that no one person can watch every animal every day, or when you're running multiple sites and need consistent reporting across all of them.
Our sheds have no cameras. What do we need to start?
Less than you might expect. We audit what is already on site during the Discovery Sprint. On many farms, a handful of well-placed cameras covers the critical areas — entry, feeding stations, and rest areas. We specify what is genuinely needed, not the maximum the vendor would like to sell.
How does the system detect illness before clinical signs appear?
Most sick animals change their behavior before showing symptoms visible at a glance — they move less, eat less, or isolate from the group. The model tracks these behavioral signals against each animal's own baseline, so a deviation that is subtle by herd standards still triggers an alert. Behavioral flags typically appear 24–48 hours before a stockperson would notice a problem during a manual walk.
Who receives the alerts, and through what channel?
That is configurable to your protocols. Alerts can go to a dashboard your farm managers check each morning, to a mobile notification for out-of-hours events, or to an existing farm management system you already use. Severity thresholds — what triggers an immediate alert versus a next-morning summary — are set during the build against your operating procedures.
How long until the model is calibrated to our breed and conditions?
Baseline calibration typically takes 4–8 weeks of production data after deployment. During that period the system flags anomalies against population-level norms while it builds a herd-specific baseline. By the end of the first full production cycle the model is reading against your animals' normal rather than a generic dataset.
Bring your shed layout, your current protocols, and your worst recurring health loss. In 45 minutes we'll show you what AI monitoring would catch — and what it wouldn't.
Book a 45-min scoping call