In a push towards modernising its investigative capabilities, Chandigarh Police are set to acquire advanced software for analysing internet protocol detail records (IPDR), call detail records (CDR), and tower dump records (TDR). This cutting-edge tool, currently in tender stage, is aimed at revolutionising police operations by pinpointing suspect movements, uncovering hidden networks, and monitoring encrypted communications platforms.
The new software will integrate open-source intelligence (OSINT) capabilities with artificial intelligence (AI)-powered analytics, enabling the police to track individuals, identify behavioural patterns, and predict suspicious activities. With the increasing use of encrypted platforms and virtual communication tools by criminals, this solution will help cops stay ahead of evolving threats from cybercriminals, drug traffickers, and organised crime networks.
Virtual walk-throughs of suspect movements
As per the tender document, one of the key features is the ability to convert raw IPDR, CDR, and TDR data into human-readable formats like PDF and XLS for easier interpretation. It will also visualise tower sessions on maps, enabling officers to track suspects in real-time by analysing which mobile towers they connected to. The software can create virtual walk-throughs by mapping a suspect’s movement from one location to another, helping officers reconstruct crime scenes and trace escape routes.
The software will further bolster investigations by resolving encrypted traffic, including the onion router (TOR) and virtual private network (VPN) connections used by criminals to conceal their activities. It will also monitor voice over internet protocal (VoIP) calls and chat logs from platforms such as WhatsApp, Telegram, Signal, and over 30 other messaging apps. This will allow cops to track mobile numbers, user names, and activities across platforms, providing detailed insights into suspects’ digital interactions.
{{/usCountry}}The software will further bolster investigations by resolving encrypted traffic, including the onion router (TOR) and virtual private network (VPN) connections used by criminals to conceal their activities. It will also monitor voice over internet protocal (VoIP) calls and chat logs from platforms such as WhatsApp, Telegram, Signal, and over 30 other messaging apps. This will allow cops to track mobile numbers, user names, and activities across platforms, providing detailed insights into suspects’ digital interactions.
{{/usCountry}}Uncovering hidden networks
Using AI and machine learning, the tool will analyse communication patterns to predict suspicious behaviour and detect anomalies in voice or messaging usage, flagging potential threats early. It will correlate data from financial transactions, social media activity, and call logs to uncover hidden networks. By integrating OSINT and social media tracking, the software will detect criminal networks operating across multiple platforms and encrypted channels.
A feature called predictive analytics will allow the police to act proactively by identifying suspicious behaviour before it escalates into criminal activity. Furthermore, monitoring encrypted messages and other data will provide crucial intelligence on suspects using anonymous communication tools.
Beyond tracking communications, the software will retrieve vehicle ownership details and enable Aadhaar-based searches to verify identities and link individuals with phone numbers or social media profiles. It will also simplify the analysis of TDR data by consolidating records from various telecom operators into a unified format. Officers will be able to apply date and time filters to extract relevant data for specific investigations, while comparison across multiple CDR files will provide insights into suspects’ activities over time.
With this advanced tool, Chandigarh Police aim to enhance their investigative efficiency, ensuring quicker resolution of cases and improved public safety. The tender submission deadline is November 8, with technical evaluations scheduled for November 11.