Smart Capture App
An Android application that focuses on the number plate, selects the sharpest frame from a burst, and tags every image with GPS location, timestamp and officer ID. It works offline and syncs automatically.
Our Work/AI e-Challan
Public Safety · Traffic Enforcement
From 90 seconds to under 10 per challan.
An AI-powered enforcement platform that converts officer-captured violation photographs into verified, legally defensible e-challans, at two to three times the current volume and with no additional staff.
90–95%of challan data filled automatically
2–3×more challans per station per day
80%less operator effort
- Client
- Hyderabad Traffic Police
- Field validation
- Jubilee Hills Traffic Police Station
- Components
- Capture app, AI engine, operator dashboard, backend
- Integrations
- e-Challan system, RTA records
- Deployment
- Cloud or on-premise GPU
The Challenge
A manual workflow running on WhatsApp.
Each traffic police station receives 500 to 1,000 violation photographs a day. Officers share them through WhatsApp groups, and operators download each image, read the number plate, verify the vehicle against RTA records and type the challan by hand.
Each challan takes 60 to 120 seconds. Station capacity is capped, violations go unprocessed, number plates are misread, and delays weaken deterrence.
500–1,000violation photographs reach each station every day
- Time-intensive manual workflow of 60–120 seconds per challan
- Limited processing capacity and lost revenue
- Manual errors in number plate reading and violation tagging
- Reduced deterrence because of delays in challan generation
Our Solution
One pipeline from capture to approval.
Three components replace the manual WhatsApp workflow, and connect to the systems the department already uses.
AI Engine
Detects violations, reads number plates, classifies vehicles, validates details against RTA records and assigns every case a Challan Confidence Score from 0 to 100.
Operator Dashboard
Presents pre-filled challans for review, prioritises high-confidence cases for instant approval, and packages evidence for legal defensibility.
How It Works
Five steps, under ten seconds.
STEP 01
Capture
The officer photographs the violation in the app.
STEP 02
Analyse
The AI engine processes the image within five seconds.
STEP 03
Verify
The violation is detected and the vehicle is checked against RTA records.
STEP 04
Review
A pre-filled challan appears on the operator dashboard.
STEP 05
Decide
The operator approves, edits or rejects it, and each correction trains the model.
Key Capabilities
Built for real roads and real images.
- Detection of 10+ violation categories, including helmetless riding, triple riding, mobile phone use, signal jumping, wrong-side driving and number plate tampering
- 95%+ number plate accuracy, including blurred, distant and low-light images
- Multiple vehicles and violations identified within a single image
- Duplicate challan prevention using vehicle number, timestamp and GPS
- Automatic rejection of unusable images with 90%+ precision
- Role-based access for officers, operators and supervisors, with officer- and station-level reports
Outcomes
Measured in the field.
Figures from the proof of concept at Jubilee Hills Traffic Police Station and the projected station throughput.
Field officer time per violation
Operator time per challan
Challans per station per day
Implementation
Production-ready in phases.
A six-month AI stabilisation cycle follows go-live, during which real-world feedback continuously improves accuracy.
| Workstream | Duration | Scope |
|---|---|---|
| Requirement gathering | 2 weeks | Workflow study with officers and operators |
| AI deployment and tuning | 6–8 weeks | Violation detection, OCR, multi-vehicle logic, confidence scoring |
| Mobile application | 4–5 weeks | Burst capture, metadata tagging, offline sync |
| Operator dashboard | 5–6 weeks | Pre-filled challans, evidence packaging, productivity metrics |
| Backend and APIs | 3–4 weeks | Ingestion, authentication, e-Challan and RTA integration |
| QA and field testing | 2–3 weeks | On-ground validation and training |
Bring AI-assisted enforcement to your commissionerate.
We will assess your current workflow and propose a station-level pilot with measured targets.
Speak with Our TeamMore of Our Work