Wild Life Monitoring — early warning for human-wildlife conflict, built for the last mile.
We design and deploy solar-powered, edge-AI field units, a 24×7 digital command center, and a role-based mobile app — so forest departments can detect, alert and respond before an encounter becomes a tragedy.
A forest edge with no early warning is a village that finds out too late.
Human-wildlife conflict incidents cluster around known chokepoints and known seasons — but without continuous monitoring, the first signal a village gets is often the encounter itself.
One detection, five steps, no dead ends.
Every field unit runs the full pipeline on its own — detection doesn't depend on a shared processor or a stable network to start working.
Sense
Day/night IR + thermal camera watches a fixed chokepoint, day and night, at 1000m+ range.
Detect
An on-site edge-AI computer classifies the animal locally — no round trip to the cloud to decide.
Alert
A qualifying detection fires a local siren and transmits over 4G/LTE with a GPS-tagged location.
Notify
The command center, the mobile app and an SMS/WhatsApp fallback all receive the same village-scoped alert.
Respond
Forest staff dispatch a response team and track it through to resolution from the dashboard.
What's actually on the pole.
One camera, one edge-AI computer, one communications path and one power system per unit — nothing shared, nothing that fails together.
Optical sensing
Day/night IR camera with thermal imaging, fixed to the chokepoint geometry of each site.
1000m+ range · thermal mandatoryEdge AI
On-device inference computer running the detection model locally, so classification doesn't depend on connectivity.
Up to 67 TOPSLocal storage
Industrial-grade solid-state storage for the OS, AI model, event evidence and logs.
256GB NVMeConnectivity
An independent 4G/LTE path per unit, so every field unit reports in on its own.
Industrial routerLocation
A dedicated GPS module geo-tags the unit and every detection or tamper event automatically.
Auto geo-taggedTamper detection
A vibration sensor on the enclosure logs and reports any disturbance to the unit itself.
MEMS sensorLocal alert
A high-decibel siren sounds on-site immediately, ahead of any remote response.
12V DC sirenPower
Solar generation and battery storage sized for night operation and low-sun days.
Solar + LiFePO₄Mounting & enclosure
Weatherproofed electronics on a raised pole with an anti-climb guard and armoured cabling.
IP66 · 7m poleEvery camera, every team, one dashboard.
The software layer that turns individual field units into a coordinated response system.
Live alert dashboard
Consolidates AI camera detections, distress calls and citizen sighting reports, tagged by species, location and priority.
GIS mapping
Camera and sensor locations, movement corridors, hotspots and response-team positions, all on one map.
Video wall & drone feed
Camera and sensor feeds, plus drone video, integrated onto the control room's video wall.
Response tracking
Field teams tracked live — dispatched, en route, on site, resolved — against every open incident.
Analytics & hotspots
Historical incident trends and predictive heatmaps, so mitigation goes where the risk actually is.
Audit & access control
Role-based access, shift hand-over logs and an immutable audit trail of every action and escalation.
One app, three roles, alerts that reach the right people.
A single role-based Android application, so citizens, village leadership and forest staff each see exactly what they need.
Citizen
- Push alert the moment an animal is sighted in their own village
- Profile designed to link to land records in a future phase
Sarpanch / Panchayat
- Same village-scoped sighting alert as local citizens
- A coordination point for the village during an active incident
Forest Department
- Three access tiers — ground staff, range officer, divisional officer
- Sighting statistics by species, area and time
- A management view mirroring the command center dashboard
No smartphone, no problem: the same village-scoped alert sent to the app also goes out over SMS and WhatsApp, so reach isn't limited to app users alone. Every incident is geo-tagged to a specific village — only that village's citizens and Sarpanch are notified, while forest staff are alerted in parallel through the departmental escalation chain.
See the detection pipeline run, in your browser.
A working, browser-only demo of the detect → verify → alert → respond pipeline, running a real fine-tuned YOLOv8 model client-side against live camera or curated site imagery.
This is a real, working detection model — a fine-tuned YOLOv8 object detector running entirely in your browser. Nothing is uploaded, no server, no API key. It loads a ~5MB model on demand, so it only starts when you ask it to.
The full system, end to end.
From the first site survey to a running control room, as one engagement rather than separate vendors.
- 01
Risk assessment & site survey
Village-by-village conflict risk assessment that determines where field units go and how many.
- 02
Field hardware & installation
Camera, edge-AI, power and communications hardware, sourced, installed and commissioned on site.
- 03
Detection model & edge deployment
The AI classification model tuned to the target species and deployed to run locally on each unit.
- 04
Command-center software
The dashboard, GIS engine, alerting and analytics platform that operates the control room.
- 05
Mobile app development
The role-based Android application for citizens, Sarpanch and forest officials, built and maintained.
- 06
Public information portal
A district-level portal for department content, with an editorial moderation workflow before anything publishes.
- 07
Systems integration
Connecting response-team dispatch, drone feeds and related field systems into one integration layer.
- 08
Training & ongoing support
Structured training for department staff and village coordinators, plus software maintenance after go-live.
Planning a monitoring or command-center project?
Tell us about the geography, the species, and the response chain you already have. We'll come back with what a deployment would actually look like.