Every day, without pause, nearly 16 lakh calls flood into the servers anchoring Telangana’s Emergency Response Support System. Inside the command centers, the phones never stop. The atmosphere is one of permanent urgency.
The data, however, tells a very different story.
A structural audit conducted by state authorities has exposed one of the most striking imbalances in public safety infrastructure anywhere in the country. Of those 1.6 million daily calls, only 0.28% are genuine emergencies requiring a police complaint or field dispatch. That translates to roughly 4,500 actionable calls a day across the entire system. The rest — an unrelenting ocean of operational noise — must be manually sifted through by operators who cannot afford to miss the signal buried inside it.
Confronted with this bottleneck, Telangana Police are now preparing a significant technological pivot: deploying advanced AI tools directly into the emergency response framework to do what no team of human operators can do at this scale — filter the noise in real time, so that genuine distress reaches help faster.
Two Numbers, Two Very Different Realities
The audit reveals a sharp operational divide between Telangana’s two primary emergency channels that has gone largely unexamined until now.
The unified national helpline, Dial 112 — which handles police, medical, fire, disaster services, and women and child safety — absorbs the overwhelming bulk of incoming volume, receiving between 14 lakh and 16 lakh calls on any given day. After being processed through automated Interactive Voice Response systems, an astonishingly small 500 to 600 calls — roughly 0.04% of total volume — are identified as time-sensitive emergencies actually requiring boots on the ground.
The traditional Dial 100 police helpline tells a contrasting story. It processes between 20,000 and 50,000 calls daily, of which approximately 4,000 — around 20% — transition into formal police complaints or field deployments. The conversion rate is dramatically higher, which points to a simple reality: people who dial 100 are more likely to be calling with a specific, active police matter.
The remainder clogging both systems is a chaotic mix — administrative inquiries, follow-ups on earlier complaints, minor traffic congestion reports, suspicious activity alerts and cross-border misdials. Up to 2% of daily calls originate entirely outside Telangana’s borders, adding unnecessary strain to a network designed for the state’s own residents.
When The Phones Get Busiest — And Why That Matters
Call volumes follow a predictable and high-stakes curve throughout the day. The infrastructure is taxed continuously, but volume spikes dramatically past 7:30 PM and remains elevated through the night. The majority of nocturnal emergency calls involve domestic disputes and community-level altercations, followed closely by mental health crises and suicide-related distress signals — categories where response speed is directly tied to outcome.
Seasonal conditions compound the pressure. During the ongoing monsoon, emergency lines are heavily hit by calls regarding severe waterlogging, broken traffic grids and fallen trees — all genuine public safety concerns, but ones that further stretch the capacity available for life-threatening emergencies.
Here is the operational paradox the audit uncovered: despite the heavy night-time volume, actual response times are often faster after dark. Telangana’s average emergency response time currently sits at approximately 15 minutes within metropolitan Hyderabad, stretching to between 20 and 30 minutes across rural districts and smaller towns. During the day, patrol vehicles are frequently pulled away from active emergency loops to manage VIP bandobast duties, court assignments and routine neighborhood patrolling — competing demands that slow the system precisely when public activity is highest.
What AI Will Actually Do
The AI tools Telangana Police plan to integrate into the ERSS infrastructure are not blunt instruments. They are designed to operate across multiple layers simultaneously — parsing voice patterns in real time, tracking caller conversion rates, identifying and filtering repetitive spam networks, and optimizing dispatch workflows so that the right vehicle reaches the right location in the shortest possible time.
The goal is not to replace operators or automatically block incoming calls — that carries too high a risk of filtering out a genuine emergency from an unusual source. The objective is smarter triage: surface the 500 calls that truly need a response faster, with greater confidence, so that the human operators who make final dispatch decisions are working with better information, less noise, and more time.
Globally, the approach has precedent. The Philippines upgraded its Unified 911 system earlier this year specifically to detect and block AI-generated and automated prank calls. Following rollout, prank and abandoned calls declined by 98.3%, freeing up operator capacity for genuine emergencies almost immediately.
