I spent years in the IT industry at a large company, close to how businesses build and protect their presence online. Around the end of 2025, I kept running into the same thing: the way people find and judge brands was shifting, and almost nobody was watching it happen.

Buyers had stopped starting every search on Google. They were asking ChatGPT, Gemini and Claude which product to pick, and treating the answer as a recommendation from someone they trusted. The brands I looked at had no idea how they showed up in those answers. Many of them were simply not there. They were spending real money on their digital presence, and they could not tell you whether a single AI platform understood their brand, mentioned it, or put it forward when a customer was deciding what to buy.

That blind spot is why I started Astiva AI. We give brands competitive intelligence for AI search: how AI models describe them, when a competitor gets recommended in their place, which sources the AI is citing, and where they are winning or losing ground over time.

What makes us different is that we answer one question most brands cannot even see: when a buyer asks an AI for the best option in your category, does it name you, and if not, why not. Astiva AI is built around that whole problem, start to finish, not one slice of it. It’s a competitive intelligence platform for AI brand and visibility.

The way I think about it, there are four jobs a brand has to do inside AI search, and we handle all four in one place. The first is to monitor. You tell us the real questions your customers are asking AI platforms (or we can also find the questions people ask). Things like "what is the best CRM for a small team," and we ask those questions across the major AI platforms for you, every day, automatically.

Every answer gets stored, so you can go back and see what an AI said about you on any given day, on any given platform. That alone is a shock for most brands. They have never once read what the machine is telling their buyers.

The second job is to understand what you are looking at, and this is where it gets interesting for anyone who cares about AI visibility. We do not just tell you whether you showed up. We show you how often you appear, how you stack up against competitors in the same answers, whether the AI describes you warmly or coldly, and whether you are the first name mentioned or an afterthought at the bottom. We surface competitors you did not even know were being recommended in your space. And we go one level deeper than anyone else: citation analysis. When an AI recommends a competitor instead of you, we show you the actual pages and sources the model leaned on to form that answer, and how much weight each one carries. That tells you exactly what is shaping the recommendation, not just that you lost it.

The third job is to fix it, and this is the part I am proudest of. Finding the gap is not enough. So Astiva AI generates what we call citation-ready content, content built with the specific things AI platforms look for when they decide what to trust and quote: clear statistics with sources, structured answers, proper references. You publish it, and then we watch to see whether the AI platforms actually start citing it. The loop closes: we find the gap, we help you fill it, and we confirm the machine picked it up. I do not know another platform that takes you all the way from "you are invisible" to "the AI is now recommending you" and proves it.

The fourth job is to prove it mattered. We connect to your analytics so you can see what that AI visibility is actually worth, the traffic it sends, the conversions, the revenue. For a long time this whole category was a leap of faith. We wanted a brand to be able to point at a number.

There are other good companies looking at AI visibility, and I respect the ones building in this space. But most of them own one piece of it. Profound does serious monitoring, though the depth and the attribution a brand actually needs sit behind enterprise pricing that starts in the thousands a month, which puts it out of reach for most of the market. Peec AI tracks how you show up, but it does not generate the content to fix a gap and it cannot connect your AI visibility to real traffic and revenue. So a brand that wants the whole picture ends up stitching two or three tools together and still has holes. What I have not seen anyone else do is run the full cycle in one product: see the gap, close it with content built to be cited, confirm the machine picked it up, and then prove it moved the business. That is the part we are alone on right now, and it is a genuinely hard thing to build, which is probably why.

The reason all four have to live together is that AI answers do not sit still the way an old ranking did. The same question can return a different recommendation next month, because a model changed or a competitor published something that shifted how it gets cited. A brand that looks safe today can quietly drop out of the conversation without touching a thing on its own site. Watching that, and knowing what to do about it, is competitive intelligence for AI search. A one-time audit is not. That is what we built Astiva AI to be: not a report you read once, but the system that keeps you recommended while the ground underneath keeps moving.

Our first real traction came through an agency partner, Rotofy Media. They used Astiva AI while working with a pharmaceutical client to lift that brand’s AI visibility, and the results held up well enough to publish as a 60-day case study. That mattered more than any pitch I could have written. It was proof from a working account, not a promise.

That early win showed us something useful: agencies feel this problem first, because their clients ask them why a competitor keeps getting named by AI and they are not. So agencies became one of our strongest channels. They bring us several brands at once and they already understand the stakes.

Today new business comes from a mix. Agency partners like Rotofy are one side of it. The other side is founder-led, which is really just me being loud about a problem I believe in: writing, talking to people in the builder community here in Bengaluru, and sharing what we learn about how AI systems actually pick brands. We serve more than 40 customers now, from B2B SaaS to EdTech, and what keeps me going is that companies in completely different industries all arrive at the same realization at roughly the same moment. As discovery moves to AI, they all need to know how they are being represented and recommended.

One thing we decided early was to keep Astiva AI reachable. A lot of this category was built for Fortune 500 budgets, where you book a call, sit through a sales process, and pay enterprise rates before you see a single number about your own brand. We went the other way. You can start free, see what the AI platforms are saying about you within minutes, and move up only when it is clearly paying off. No signup wall, no enterprise contract to get in the door. That choice is a big part of why the people finding us are not just large companies. They are small teams and agencies who finally have a way into something that used to be locked behind a five-figure deal.

The decision that set our direction was choosing to build before the market was obvious. In late 2025 and early 2026, "AI search visibility" was not a category anyone was budgeting for. There was no guarantee businesses would treat it as a priority. We bet that the shift in how customers discover brands was real and permanent, and we started building anyway. That bet shaped everything: our product, our early conversations, the kind of customer we went after.

The lesson I took from it is simple. Do not wait for a market to become obvious before you enter it. If you can see a genuine shift and a real customer problem, start building, put it in front of people, and let the market teach you the rest.

One mistake I made at the start was not hiring. I believed that with AI I am a one-man army. After experiencing a severe burnout I had the painful realization that there’s only so much I can do alone. Having a small but effificient team is important which is why I personally get involved in the hiring process and take my time to finalize someone.

I believe what a startup needs today for growing well is a small but powerful team.

3 years ago, a marketing leader asking about visibility meant one thing: Google rankings and backlinks. That conversation has changed completely. Now the same leader wants to know whether ChatGPT recommends them, how an AI describes their brand versus a competitor, and which sources are shaping that description. The unit of competition moved from the keyword to the recommendation.

Customer expectations moved with it. In the beginning, a lot of our job was explaining why any of this mattered. Brands understood rankings; they did not yet understand why a sentence inside an AI answer could decide a sale. That part has gotten easier. More buyers now arrive already worried about it, which tells me the category is maturing.

We adapted by refusing to stop at "yes, you appeared in this answer." That is not intelligence; it is a notification. We pushed the product toward the harder questions: how are you represented, how do you compare with the competitors showing up next to you, and what specifically can you change to show up better. The goal was always to turn something genuinely complicated into something a business can read on a Tuesday morning and act on by Friday.

If someone is starting out in this industry I’ll strongly recommend him or her 2 things to work.

1) Positioning.

Building an AI visibility tool is not that difficult. All you need to do is take the APIs of your target LLMs and use something like a Codex or Claude Code and its possible to create an AI visibilty tool.

Which is why you need to work on how you position yourself for standing out. Astiva AI isn’t marketing itself as another AI brand monitoring tool but a competitor intelligence platform for AI search. Otherwise Astiva AI would have been lost in the sea of the AI visibility tools.

2) Research the competitors and customers.

In the initial days I used to research Profound, Peec AI and other top players in the market (even purchased their premium plans) just for getting a good grasp of what they offer and what not.

Then I would even talk to people active in the online forums and then tried having conversartions with them and started asking them if a tool offering so and so (what Profound like players offer and the features that I feel should be there) is worth paying for or not. Based on the insights from those conversations I kept refining the product. And then when we cracked our first big B2B client. This was the much needed validation for us. So research your competitors to see what different you can offer otherwise people have no reason to pay attention.

When it comes to ignoring, I’ll say ignore the popular stuff. Just because something works for a certain someone doesn’t mean that it will work for you too. Use your common sense, thats so underrated.