Pattern discovery without labels. Used for clustering, anomaly detection, and segmentation.
40 implementations across 19 industries
This is like giving your litigation and investigations team a super‑powered, tireless junior lawyer that can read millions of emails and documents in hours, highlight what’s important, group similar issues, and surface risks and evidence so your senior lawyers only spend time on what really matters.
This tool is like an automated marketing analyst that studies all your customer data and groups people into smart, predictive segments so you can send the right message to the right audience at the right time.
This is like putting a smart security guard in your cloud data center who never sleeps, learns what “normal” looks like, and automatically flags or blocks suspicious behavior before it turns into a breach.
This is like putting a smart, always-on analyst in your call center who listens to every customer conversation (calls, chats, emails), figures out what customers are really feeling and saying, and then tells your teams how to fix problems, keep customers from leaving, and sell more — automatically and at scale.
This is like an AI control tower for your IT systems that constantly watches logs, metrics, and alerts, spots issues before humans notice them, and suggests or triggers fixes automatically.
Think of AIOps as an AI control tower watching all your IT systems 24/7. It reads all the logs, alerts, tickets, and metrics, spots patterns humans miss, and then either recommends or automatically takes actions to keep systems healthy and prevent outages.
Imagine your entire IT and network environment has a 24/7 “air traffic controller” that watches every signal from every system, spots early warning signs of trouble, and automatically re-routes traffic or fixes issues before users even notice. That’s what AIOps does for IT and security operations.
This is like a real-time control room for sports and esports fans: it listens to what fans do and say across channels, then tells teams, leagues, and brands who their fans are, what they care about, and how to keep them engaged and buying.
This is like giving your marketing team a super-smart sorting machine. It looks at all your customer data and automatically groups people into smart segments—"likely to buy now", "needs nurturing", "high-value upsell"—so you can send the right message to the right people without guessing.
Think of this as a smart salesperson that quietly watches how every customer behaves across your ads and website, then groups similar people together so you can show each group the most convincing message automatically.
Imagine a 24/7 digital security guard that has watched your company’s computers and network long enough to know exactly what “normal” looks like. The moment something behaves strangely — a laptop logging in from two countries at once, a server suddenly talking to an unknown system, or data moving at odd hours — it raises a flag, even if that specific attack method has never been seen before.
This is about using AI as a smart security guard for government IT systems—constantly watching network activity, spotting unusual behavior faster than humans can, and helping security teams respond quickly to threats.
This is like having an AI-powered focus group analyst that continuously studies your customers’ behavior and groups them into clear audience clusters so you can target ads and campaigns much more precisely.
This is like a 24/7 digital guard that constantly watches your systems, apps, and data, and only calls you when something looks wrong or important enough to act on.
This is like drawing a big map of who knows who in a city, then using math to see which people or groups are at the centre of crime activity or at highest risk of becoming victims. Instead of only looking at individual incidents, it looks at the web of relationships around them.
This is like a smart store assistant that quietly watches what shoppers tend to buy together, then groups similar shoppers and shows each group products they’re most likely to want next.
Think of the body as a city with many roads and intersections. Old-style drugs tried to fix a single broken traffic light and hoped the whole traffic jam would disappear. Network-based drug discovery uses computers to map the entire traffic system and find combinations of lights, roads, and junctions to adjust together, so the whole city flows better, not just one corner.
This is like a health monitor for factory heating and cooling systems. It watches temperature, pressure, and energy data from HVAC equipment and uses machine learning to flag when something looks wrong before it actually breaks.
This is like giving a retail brand a super-smart store manager who watches how every customer shops across channels, learns their habits, and then tells you exactly what to stock, how to price, and what offers to send so they buy more and stay loyal.
This is like giving your marketing team special glasses that color each customer by how they feel about your brand—happy, neutral, or unhappy—and then grouping similar colors together so you can treat each group differently.
This is like giving every critical compressor in a jet factory or defense plant a ‘fitbit’ that constantly watches how it behaves, groups similar behavior patterns together, and flags when one starts acting differently from its healthy group—before it actually fails.
Think of Trend Hunter as a giant, always-on radar that scans the internet and culture for new ideas in fashion, lifestyle, and products, then highlights what’s starting to catch on so brands can react before competitors do.
This is like having a live, detailed skills map of your entire workforce that shows what people can actually do today, what you’ll need tomorrow, and where the gaps are – so you can hire, reskill, or redeploy people based on data instead of gut feel or outdated org charts.
This is like giving a small business its own smart marketing assistant that learns what different types of customers like, then automatically shows each group the right message, offer, or product at the right time.
This is like organizing all your customers into smart "buckets" based on how they behave and what they care about, so you can talk to each group differently instead of shouting the same message to everyone.
This is like taking a few lab tests of mine waste, then asking a smart statistician-plus-AI system to ‘fill in the gaps’ and group all the waste into meaningful types. Instead of sampling every pile of tailings, the model learns patterns from existing samples, simulates realistic extra data, and then clusters the material into zones with similar properties.
This is like a 24/7 ‘smoke detector’ for crime data. It constantly watches crime reports and related signals, and when something looks unusual for a given place and time (a spike in incidents, a new pattern, or activity in a normally quiet area), it raises a flag so police and city officials can respond faster.
Think of your customer base like a crowd at a stadium. Old segmentation grouped people by simple traits like age or zip code. AI-driven behavioral segmentation instead watches how each person actually moves, cheers, and buys during the game and then groups them into much smarter clusters—so you can talk to them in ways that feel personal and timely.
This is like sorting all your customers into smart, data-driven buckets—such as big spenders, bargain hunters, and at‑risk customers—so you can talk to each group differently and more effectively instead of shouting the same message at everyone.
Think of this as a smart advisor that reads what shoppers say about prices (reviews, surveys, social posts) and tells retailers how customers really feel about their pricing, promotions, and fees.
Imagine a highly detailed, always-updating digital copy of a building that shows temperatures, equipment behavior, and room conditions in real time. This “digital twin” constantly compares what should be happening to what is actually happening, and flags when something looks off so facility teams can fix problems before people notice or energy is wasted.
This is about upgrading today’s DevOps practices with AI so that IT systems can watch themselves, spot problems early, and often fix or prevent issues without humans jumping in every time—like giving your operations center a 24/7 intelligent assistant.
This project is like a global neighborhood watch that uses satellite images and other digital traces to spot unusual military or government activity before it becomes an obvious crisis. It sifts through huge amounts of location-based data to detect “gray zone” moves—actions that are aggressive but fall short of open war.
Imagine sorting millions of customers into natural “clubs” based on how they actually behave, instead of guessing with broad labels like ‘young professionals’ or ‘families.’ Machine learning watches what people do—what they click, buy, and respond to—and automatically groups them into meaningful segments so you can talk to each group in a way that fits them best.
This work is like a detailed map of how AI is being used across the renewable energy world – solar, wind, storage, grids – showing who is doing what, which ideas are hot, and where new opportunities are opening up.
Think of SurakshaNetra as an AI-powered early warning radar for cyberattacks on Indian networks. It constantly scans digital traffic, learns what “normal” looks like, spots suspicious activity in real time, and alerts defenders before small issues turn into major breaches.
This is like a Google Maps view of all the researchers and papers working on AI in HR—who collaborates with whom, which topics are crowded, and where the white space is.
Imagine you’re looking at a massive, messy spreadsheet of soil and rock chemistry from a whole region. Hidden inside those numbers are subtle patterns that hint where copper deposits might be. Manifold learning algorithms are like very smart mapmakers: they compress all those complex chemical readings into a simpler picture where unusual areas (anomalies) associated with copper mineralization stand out, making it much easier to see where to explore next.
This is like an aviation incident log, but for AI: a central place where real-world AI failures, harms, and near-misses are collected, labeled, and analyzed so others can learn from them and avoid repeating the same mistakes.
This work is like a detailed map of how scientists are using AI to find new medicines. Instead of inventing a single AI tool, it surveys thousands of research papers to show where AI is helping most in drug discovery, which tools are popular, and how the field is evolving.