Each solution is a trained model plus a workflow, deployed at the right tier. A new use case needs a new AI model — not new infrastructure.
| Solution | What it does | Readiness |
|---|---|---|
| Sheet counting & crack detection | Counts asbestos / roofing sheets and flags cracks from a live 1080p feed; Dockerised REST API on Jetson. | TRL 4 · Working prototype |
| Face-recognition attendance | Contactless attendance on Jetson GPU, written straight to the ERP — for campuses and workplaces. | TRL 4 · Working prototype |
| WellNest wellness monitoring | Consent-based wellness score from meal, presence and emotion signals; staff review every flag. Campuses first, then corporates. | TRL 4 · Working prototype |
| PPE compliance monitoring | Detects missing hard-hats, masks and safety vests at work sites, with real-time alerts and evidence. | TRL 4 · Working prototype |
| Perimeter & intrusion alerts | Virtual geofences on drone or fixed-camera views; instant alerts on entry into restricted zones. | TRL 3 · Proof of concept |
| Battery counting & visual QC | Classifies batteries by type (2W, car, truck), counts them on the line and at dispatch, and flags visible defects. | TRL 3 · Proof of concept |
| Construction progress tracking | Periodic drone surveys compared over time and against plan to measure progress and flag delays. | TRL 2 · Concept |
| Crop growth & health | Drone imagery analysed for growth stage, stress, pests and disease — for timely, targeted action. | TRL 2 · Concept · pilot under way |
| Campus & estate inspection | Scheduled drone patrols of roofs, facades, grounds and boundaries to spot damage and encroachment. | TRL 2 · Concept |
Problem. Manual tallies of stacked roofing sheets are slow and error-prone, and cracked sheets slip through.
What it does. Counts sheets and flags cracks from a camera feed, live on Jetson at the edge.
Problem. Roll calls eat into class and shift time, and proxy attendance is hard to catch.
What it does. Recognises each person on Jetson and marks them Present in the ERP — contactless.
Problem. Early signs that a student is struggling are easy to miss on a large campus.
What it does. Consent-based wellness score from meal, presence and emotion signals; staff review every flag.
Problem. Safety officers cannot watch every worker on a busy site, every hour.
What it does. Flags missing hard-hats, masks and safety vests in real time, with image evidence.
TRL 3 · proof of conceptProblem. Counting and sorting batteries of many types by hand at the line and at dispatch.
What it does. Detects batteries and classifies them by size — the basis for line counts and defect checks.
Data captured · Sept 2026Problem. Crops and transmission towers span large areas that are slow to inspect on foot.
What it does. Our own drone footage, ready for labelling to train crop-health and line-inspection models.
Drone surveys and AI models on our platform — from field footage to farm-level decisions. Drone capture at the far edge, pre-processing on site, analytics and dashboards in the cloud.
Success measured against agronomist scouting: accuracy, acres covered per flight, early-warning lead time and yield-estimate error.
Surface defect and crack detection · dimensional checks · foreign-object detection · packaging and label verification · line unit counting · inventory-yard surveys · dispatch verification · thermal hotspots · conveyor health
Aerial surveillance and reconnaissance · border and perimeter patrol · target identification, classification and tracking — for Army, Air Force and Navy
Crop growth and health · pest, disease and weed detection · plant counts, biomass and yield estimates
Railway track inspection · telecom tower inspection · HV transmission line inspection — without climbs or shutdowns
We run a live demonstration on your camera feeds or drone footage, then a 30-day proof of concept at your site — before you commit.