Case study · Public safety & law enforcement
A vision AI platform for Delhi Police
AI Anytime is building a vision AI platform with Delhi Police that turns visual data into faster, evidence-backed decisions for investigation and field teams — designed for scale, accountability and deployment inside the department's own environment.
- Client
- Delhi Police
- Sector
- Public safety & law enforcement
- Region
- Delhi, India
- Engagement
- AI consulting & build
- Status
- In delivery
Outcomes & KPIs
What it changes
Targets for an engagement in delivery — to be replaced with measured results after go-live.
Manual review time
60–70% less
Target
Hours of footage review per case
Time to insight
Hours → minutes
Target
From data received to an actionable lead
Coverage
Up to 100%
Target
Of visual data analysed, instead of sampled
Officer capacity
2–3× more
Target
Cases reviewed per officer per shift
- Faster review of visual data, freeing officers' time for investigation and response.
- Quicker, more consistent identification of relevant events.
- Evidence-backed outputs that support accountable decision-making.
- A platform designed to scale across units as adoption grows.
01
The challenge
- Large volumes of visual data that teams had to review by hand, slowing down time-critical work.
- Insights spread across sources, making it hard to act quickly and consistently.
- Strict requirements for data security, auditability and keeping sensitive information inside the department.
02
Our approach
- Joint scoping with the department's teams around the decisions that matter most in day-to-day policing.
- A computer-vision platform that analyses visual data and surfaces what needs attention, with people always making the final call.
- Deployed within the department's own environment, with access controls and an audit trail for every result.
- Built in phases, measuring impact against agreed KPIs at each stage.
What's next
The platform is in delivery. KPIs shown are forecast targets and will be replaced with measured results after go-live.
Next step
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