Industry
K-12 Schools and Educational Trusts
Child safety, parental consent, and verifiable attendance — designed for the realities of Indian K-12 operations: bus arrivals, multi-campus trusts, monsoon outages, and DPDP Act obligations for children's data.
Typical client profile
Single-campus schools, multi-campus educational trusts, residential and boarding schools, international curriculum schools. Typical deployment is 30–200 cameras per campus with FRS terminals at gates.
Indicative scope
Typical multi-campus engagement: a 2–3 month Discovery covering DPIA framework, hardware standard, integration to school-management system and parent-comms, accuracy-SLA agreement. Then a 9–12 month platform build with a pilot campus, followed by 4–8 week per-campus scale-out.
Compliance considerations
- DPDP Act 2023 — verifiable parental consent for under-18 data processing
- Age-banded biometric policy — RFID + parent-app fallback for under-6, FRS with periodic re-enrolment for 6–18
- Right to erasure on student exit, with retained audit log of the erasure itself
- POCSO-aware visitor flows — no unsupervised camera coverage of restrooms, changing areas, or counselling rooms
Schools stack
Modules typically deployed in this vertical.
The platform supports more — this is the configuration we most often land on for clients in this category.
Face Recognition (FRS)
Hardware-grade FRS terminals with depth and IR anti-spoofing at gates and high-traffic entries; software liveness for back-end matching.
Accuracy: 95% indoor adult under controlled lighting · 90% under-12 with periodic re-enrolment · 88% outdoor daytime
Visitor Management
Pre-registration, on-arrival capture, parental-consent workflow for visiting children, and badge issuance with auto-expiry.
Accuracy: Process compliance >99% with mandatory-field forms; identity match per FRS module.
ANPR / Licence Plate Recognition
Read Indian commercial and private plates at vehicle gates — single-lane, multi-lane, and toll-style installations.
Accuracy: 94% on standard high-security plates in daylight · 88% on aged/dirty plates · 85% at night with IR fill.
Intrusion Detection
Polygon-defined perimeter and restricted zones, schedule-aware, with separate models for human vs. animal vs. vehicle.
Accuracy: 93% detection on humans crossing defined polygons; <2 false alerts per camera per night with proper masking.
Loitering Detection
Zone-based dwell-time thresholds with operator-tunable durations; separates queue-waiting from anomalous loitering.
Accuracy: 88% on zones with consistent lighting and unobstructed lines of sight.
Weapon Detection
Two-stage detector: low-latency edge filter + cloud-of-one verifier, with mandatory human acknowledgement before escalation.
Accuracy: 90% on visible firearms and large blades in well-lit CCTV; false-positive rate tuned to operator capacity, not vanity metrics.
Audio Analytics
Edge-deployed acoustic models for gunshot, glass-break, scream, and distress sounds. Calibrated per-site to suppress local ambient noise.
Accuracy: Class-dependent: 92% gunshot in semi-urban environments, 88% glass-break, 80% scream in noisy interiors.
Lecture / Classroom Recording
Automatic recording with speaker tracking, board-content capture, and searchable transcripts. Indexed for revision and audit.
Accuracy: Transcript word-error rate around 8–12% on Indian-accented English; per-subject vocabulary tuning improves this.
Crowd Density Estimation
Density mapping for event safety and evacuation planning. Triggers tiered alerts before crush thresholds.
Accuracy: Density estimation within ±15% for medium-density crowds; higher uncertainty at extremes.
AI-Prioritised Alert Routing
Severity ranking, dedupe and suppression rules, escalation chains with on-call windows, and SLAs per alert class.
Accuracy: We measure mean-time-to-acknowledge and false-positive rate per class, monthly.
Run a schools site through Discovery.
Tell us about the site count, the existing camera estate, and the compliance perimeter. We'll come back with a scoping outline.