Yesterday I was trying to stalk my ex’s Insta account. I looked at the recent posts - mostly individual and some with her female friends, whom I already knew. I looked at the account of one of her best friends to get a clue from there. Still, nothing was found. After half an hour, I gave up and went back to my doomscrolling session. I liked a few reels that reverberated with my broken heart and casually gave Zucky my emotional data. While scrolling, I found a few reels liked by her, which said, “Will he ever come back?” I jumped on my bed. This was the sign I was looking for, and the algorithm God provided me with the right data to give her a call in the evening.
Though later in the evening, she said, “I found it funny, so I liked it. Instagram does not give a laughing emoji reaction!” and then she cut the call. Both my phone and I went completely silent.
Can you connect the dots?
Now scale that Instagram intelligence operation: my deeply embarrassing half-hour of cross-referencing posts, mutual friends, and liked reels, by about ten million. Add satellite feeds, financial transactions, drone footage, phone intercepts, dark web chatter, and troop movements across multiple borders. Give all of that to a government trying to determine whether a hostile army is mobilising 4,000 kilometres away or whether a sleeper cell just went active inside a city of 20 million people.
The instinct is exactly the same:
Something doesn’t add up. I need more data. Let me connect the dots.
The difference is that I had Instagram’s algorithm and a ridiculous reason to use it. An intelligence analyst has eleven disconnected agency dashboards, a threat that cannot wait till morning, and a superior demanding a briefing in six hours.
The problem is never the lack of data. It is that the data exists everywhere, owned by everyone, but organised by no one. By the time it is reconciled across agencies, formats, and classification systems, the moment is already gone.
This is the problem Palantir was built to solve.
How One Company Productized God-Mode Intel
Founded in 2003 by Peter Thiel and Alex Karp, Palantir began with a simple observation after 9/11: the failure was not merely intelligence collection, but intelligence coordination. Different agencies possessed fragments of the picture, but the systems never spoke to each other fast enough.
Its first investor was the CIA, through In-Q-Tel, it’s VC arm.
The company took its name from Tolkien’s Palantiri, ancient seeing stones capable of observing events unfolding across vast distances in real time. Tolkien imagined them as instruments of power and perception. Thiel and Karp turned the idea into a defence company.
What Palantir built across platforms like Gotham, Foundry, and AIP is often lazily described as “surveillance software.” It is more accurately an operational intelligence layer: a system that ingests radically different streams of information: satellite imagery, SIGINT intercepts, financial records, sensor feeds, field reports, and maps relationships across all of it, and turns fragmented noise into something decision-makers can act on in minutes instead of days.
The US Army now has contracts with Palantir worth billions. Its TITAN battlefield systems delivers AI-processed satellite intelligence directly to soldiers in the field in real time. The company is worth over $200 billion with fewer than 5,000 employees.
Individually, none of them possess superhuman abilities.
Collectively, they have built the closest thing modern warfare has to an all-seeing stone.
And India, which exports many of the engineers capable of building such systems, is still struggling to make its own intelligence databases effectively speak to each other.
Remember Operation Sindoor?
India got a glimpse of what a Palantir-like operational system could look like during Operation Sindoor in May 2025.
For the first time, multiple intelligence and defence agencies were pulled into something resembling a unified real-time operational picture. RAW, NTRO, DIA, and IB were synchronised through integrated threat dashboards under the National Security Council. AI systems fused satellite imagery, drone feeds, electronic intercepts, battlefield surveillance, and historical military data into live operational intelligence.
Lieutenant General Rajiv Kumar Sahni later revealed that 23 AI-enabled applications were used during the operation for multi-source battlefield fusion. One indigenous system, ECAS (Electronic Intelligence Collation and Analysis System), reportedly achieved around 94% accuracy in identifying enemy radars, missile systems, and electronic signatures using historical pattern modelling.
Operational commanders received live geospatial feeds directly during strike planning, something Indian defence systems historically struggled to coordinate at speed.
In simple terms: India briefly built a wartime fusion centre.
And that is precisely the point.
Operation Sindoor proved that India possesses the engineering talent, AI capability, and intelligence infrastructure required to build Palantir-like systems during a crisis.
And that raises a far more important question:
If India can assemble something resembling a Palantir during a crisis, what exactly is stopping it from building one permanently?
The Problem
We face a problem similar to what US faced during 9/11. The bottleneck is not intelligence, it is coordination.
The country already possesses satellites, cyber units, surveillance infrastructure, electronic intercept systems, military intelligence networks, and some of the world’s strongest engineering talent. The problem is that much of this capability exists across dozens of separate organisations operating through fragmented systems, overlapping mandates, and disconnected databases.
RAW, IB, NTRO, DIA, MAC, CERT-In, NCIIPC, Defence Cyber Agency, state intelligence units, each sees part of the picture.
Very few can see all of it in real time.
This fragmentation has surfaced repeatedly across crises.
Kargil in 1999 exposed gaps in military intelligence coordination. Mumbai in 2008 revealed failures in information sharing between agencies. During the Galwan tensions, commercial satellite imagery had already indicated unusual PLA activity in the region for months before the crisis escalated publicly.
The issue was not the absence of signals. The signals existed. The challenge was turning scattered information into a unified operational understanding quickly enough for decision-makers to act on it.
And in modern warfare, speed increasingly determines advantage.
What Are We Building?
To be fair, India is not starting from zero.
Somebody, somewhere inside the system, clearly looked at Palantir and thought, “We should probably have one of those.”
The closest contender today is probably Innefu Labs, a Delhi-based company founded by Tarun Wig and Abhishek Sharma. Their platform, Prophecy Guardian, already ingests satellite feeds, telecom signals, OSINT, social media activity, financial intelligence, and surveillance inputs into unified intelligence dashboards for agencies like the Indian Army, NIA, paramilitary forces, and police departments.
In other words: the instinct is correct.
Connect the dots faster than the threat can move.
Tarun Wig once claimed they could match Palantir feature-for-feature at a fraction of the cost. Which, to be fair, sounds exactly like something every Indian founder says right before either building a global company or discovering the slow pace of enterprise procurement cycles.
And honestly, Innefu is credible. Probably the most credible player India currently has in this category.
But scale matters.
Palantir spent nearly two decades embedding itself into the operational nervous system of the American state. Innefu has raised roughly $33 million till date.
Though, around them, an ecosystem is slowly emerging. Staqu is turning CCTV footage into searchable intelligence. Videonetics has built large-scale urban surveillance platforms deployed across airports and smart cities. DRDO’s CAIR division has developed dozens of AI systems for defence applications, including ECAS, one of the key systems reportedly used during Operation Sindoor.
But when you look at the broader pattern, the bottleneck still remains the same. India is great at building the individual pieces like sensors, drones, and AI algorithms, but we are still struggling to build the orchestration layer that ties everything together into a live operational picture. The future of national security does not belong to the country with the biggest databases, but to the one that can connect the dots faster than the enemy.
Without that unified dashboard, our analysts are left doing exactly what I did during my embarrassing half-hour of scrolling: drowning in a sea of disconnected noise, trying to find a pattern that is not there, and getting a massive reality check the second you make the call.