By Abhijeet Zilpelwar, Co-Founder and CTO pi-labs
For more than a decade, big data fusion platforms were projected as the ultimate solution for intelligence and investigative work. They promised unified dashboards, seamless data integration, and accelerated decision cycles. And for a while, they delivered. But as the digital ecosystem has rapidly expanded, the ground reality for law enforcement and intelligence agencies has fundamentally changed. Traditional big-data fusion systems are simply no longer enough. The sheer volume, diversity, and speed of digital information today far exceed what legacy architectures were built to handle. Systems originally designed to index call logs, structured databases, and transactional records are failing to extract meaningful insights from the modern intelligence landscape. The problem is not just the scale of data—it is the nature of it. Intelligence is no longer confined to neat rows and columns. Agencies now navigate a universe dominated by unstructured information: thousands of hours of CCTV and drone footage, intercepted calls and voice notes, PDFs, case diaries, chat logs, and vast open-source material. Legacy systems treat these as static attachments. They can store them, but they cannot interpret them. They cannot watch a video to spot a weapon, listen to a voice to detect urgency or fear, or read a PDF to uncover hidden associations. As a result, investigators sit on a wealth of digital evidence that remains largely untapped.
This limitation is compounded by the rigid nature of traditional entity resolution. Older fusion systems depend heavily on exact matches, identical names, numbers, or IDs, yet the real world of crime and extremism rarely behaves so neatly. Suspects use aliases, identities evolve, names are misspelled, and behavioural patterns shift constantly. Legacy logic misses the subtle, cross-modal connections that often solve cases. A person who appears under a different alias in a chat log, shows up in CCTV footage, and speaks in an intercepted call can easily be treated by old systems as three unrelated entities, fracturing the intelligence picture. Even more concerning is that many fusion platforms have evolved into giant storage lakes that collect everything but understand nothing. This pushes the burden onto human analysts, who are forced to manually scrub CCTV timelines, listen to hours of intercepted audio, and read thousands of pages of documents. Critical information gets buried in noise. Analysts burn out. Vital leads are missed. The gap between data collection and data understanding continues to widen.
The solution lies in artificial intelligence, not as a futuristic add-on, but as an operational necessity. AI brings multi-modal understanding that legacy systems fundamentally lack. It can interpret videos and images to detect objects, behaviours, and patterns. It can analyse audio to transcribe, identify speakers, translate languages, and detect emotional cues. It can read documents, map relationships, identify contradictions, and understand context at scale. Most importantly, it can fuse insights across formats, correlating a voiceprint, a license plate, and a name from a seized device to create a unified intelligence picture that no human team could assemble manually. Criminal networks, terror groups, and fraud rings are already exploiting advanced technologies to evade traditional monitoring. If agencies continue relying on outdated fusion systems, investigations will slow, conviction rates will fall, and operational blind spots will widen. Transitioning from mere data storage to AI-driven intelligence delivers speed, precision, and predictive capability. Investigations that once took months can progress in days. False positives fall dramatically. Early-warning signals emerge through behavioural and pattern recognition.
The future of investigation is not about collecting more data—it is about understanding the data that already exists. In a world where digital traces are more complex, distributed, and deceptive than ever before, AI is not optional. It is the only way agencies can keep pace with modern threats and turn overwhelming data into actionable intelligence.