India's physical retail runs on idle CCTV cameras. Enalytix built a software layer on top — turning footage into footfall intelligence across 2,000+ locations.
Brajesh Mishra
Data is the most valuable asset of the 21st century, every boardroom knows this, every startup pitches it, every enterprise has a team dedicated to it. Yet, in over 80% of India's retail: the stores, the shop floors, the high-street outlets that account for the majority of the country's actual commerce the most important data was sitting uncollected, unread, and entirely ignored. Not because it didn't exist, but nobody thought to look at the hardware it was coming from. That problem became a problem statement to be solved for Rajul Tandon: not a smarter guard, but a smarter system. This unassuming, practical, and entirely straightforward insight was the foundation for a video analytics platform Enalytix now operating in 2,000+ locations across India.
Walk into any mid-sized retail chain in India and look up. There are cameras, there have always been cameras, pointed at entrances, at billing counters, at the floor. Installed by security teams, monitored by guards, reviewed only when something went wrong. For most of their existence, those cameras had one job: Record. Enalytix gave them an Intelligence superpower. The company doesn't sell cameras, instead it sells a software layer that sits on top of the infrastructure already installed and converts footage that was never meant to be useful into data that runs a business. Here is how the platform works. Footfall counts, Time spent in each section, Peak hour patterns, Crowd density alerts, Workstation throughput on a factory floor and all of it coming from cameras that were already there, already running and no thought of. The company utilizes Software as a Service (SaaS) business model, charging a recurring fee for providing intelligence that client operations teams previously relied on instinct for, or tracked on a manual basis. Rajul Tandon explains the rationale for this model: "Outcome-based is fatal," he states. "Clients pay if they find favorable outcomes and economically it makes sense to keep it as SaaS."
Here is the thing about Indian retail that most people miss. When quick commerce exploded, Blinkit and Zepto became household names, and a very specific narrative took hold: physical stores retail will become obsolete, the store is over, everything is going online. However, that narrative is wrong. Physical retail in India accounts for 80% of all trade while E-commerce — for all its noise and VC capital — sits below 6% of total retail and Organised physical retail is about 15%. The offline store didn't die, it just stopped getting attention and while the attention was elsewhere, a very specific problem was going unsolved. Large retail chains: the ones with 50, 100, 200 stores across tier-2 and tier-3 India had no reliable way to measure what was actually happening inside those stores. How many people walked in? Of those, how many bought something? How long did they stay? Which section did they spend time in? Which display was being ignored? These are not vanity metrics, these are the numbers that decide whether a store stays open, whether a product gets reordered, whether staffing levels are right. Without them, a chain operating 200 stores across 40 cities were essentially making pan-India decisions on the feedback of store managers and sheets that often weren't effective.
The competitive question in any AI platform business is always the same: what stops a larger player from building what you built? For Enalytix, the threat is specific. The two largest CCTV hardware manufacturers in the world have the distribution network, the installed base across Indian retail, and the engineering resources to embed analytics software directly into their cameras tomorrow if they chose to. So what happens to a software layer sitting on top of their hardware? The founder's answer cuts straight to the structural point — large hardware companies building generic analytics and a company building industry-specific intelligence for Indian retail are not actually competing for the same contract. A Hikvision camera can tell you motion was detected. Enalytix tells you that footfall in your tier-2 apparel store peaks between 6 and 8 PM on weekdays, drops on Monday mornings, and that the section nearest the entrance has the highest dwell time but the lowest conversion — which means your merchandising is attracting attention it isn't converting. Those are not the same product. The moat is not the algorithm but five years of Indian retail context baked into the system: the specific KPIs that a regional head actually reviews, the variance between organised retail behavior in Tier 1 versus Tier 2 cities, the difference between a fashion store's peak hours and an electronics chain's. This level of context is unattainable through simple purchase or reverse engineering during a product cycle. A startup founder might confidently claim, "We can launch a product/feature in 3 months." However, a hardware OEM attempting to build a retail analytics vertical from the ground up faces a completely different reality. In the Indian B2B landscape, the critical blend of speed and specificity is a rare commodity.
Enalytix made a deliberate decision early on to pull back from facial recognition as a standard product feature. This is worth examining as a business call, not a values statement. Facial recognition in retail is commercially valuable. Brands would pay for individual customer tracking, movement profiling, repeat visitor identification. The technology exists. The capability exists. The market exists yet they chose not to build for it exclusively. The surface reason is compliance: STQC certification, India's evolving data protection framework, the liability exposure of storing biometric data at scale. But the deeper business logic is this: every enterprise relationship Enalytix has is built on the premise that their data infrastructure is trustworthy enough to appear in a boardroom. Boardroom-grade trust and aggressive biometric collection do not generally coexist easily. So facial recognition is offered only where explicit consent exists: employee attendance systems, controlled access environments. Everywhere else, faces are converted to a digitised ID and the best thing, data stays in India. The revenue cost is real. There are contracts they haven't chased and use cases they've deliberately left on the table. The strategic payoff is longer term. India's Digital Personal Data Protection Act is not fully enforced yet. When it is, and the compliance pressure on enterprise data collection tightens, the companies that retrofitted privacy architecture under regulatory pressure will be at a structural disadvantage against companies that built it in from the start. Enalytix is betting it's the latter. On the numbers: 2,000+ deployments, the first client is still active, SaaS model intact. The next two verticals — manufacturing floor intelligence and public crowd management — are already in development. The crowd management vertical is worth pausing on. Stampedes at Indian religious venues and public events are not freak accidents. They are monitoring failures, crowd density crosses a critical threshold and nobody with decision-making authority knows in time. AI-powered density alerts sent to event managers in real time is not a feature upgrade. It is a different category of product entirely: one where the buyer is not a retail CXO but a state government or a religious trust managing hundreds of thousands of people. That is a different sales cycle, a different contract size, and a different kind of infrastructure play. The camera that started as a security tool, became a retail intelligence platform, and is now pointing at factory floors and public venues — that is not a pivot. That is a platform finding its ceiling and it hasn't found it yet.
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