Last week, even before I sat down to interview Trendos’ CCO, I was already using their product…
I was auditing my own site’s AI-search health for a YouTube video. I pulled my Trendos visibility score, 65.0 across 24 tracked prompts and every major model, which put Data-Mania 9th in a peer set that included Y Combinator and Hawke Media.
Then I did the one thing Trendos will never do for me, which is to generate recommendations. To do that I handed the Trendos data to Claude and gave it some very targeted prompts (you can get those here)…
I didn’t think twice about it at the time. It was only in the interview that I realized the gap I had just stepped over – the space between Trendos handing me the data and me getting my plan somewhere else – was Trendos’ deliberate product strategy that was built on purpose to support AI-native users like me.
The winning omission
Every AI-search visibility tool on the market is racing to bolt on recommendations. The incumbents, the challengers, all of them want to tell you what to fix next. Gintare Rimolaityte, the Chief Commercial Officer who built the commercial engine at Trendos, is deliberately building the opposite: Pure data and no advice.
While to most that might seem like a fast way to lose in the market, just look at the results this move has produced in four months.
- 5,000 signups, growing by roughly 1,000 a month and accelerating.
- 3,000 daily visitors pulling their AI-search data.
- 90% of signups activate, meaning they add prompts and actually use the product.
- Paid conversions doubled in a single month, from 10% to 20%.
- One enterprise client, Hostinger, runs 15,000 prompts a day.
- Enterprise deals closing in 1 month against competitors’ 12 to 18.
And a four-month-old company has driven these outcomes all while refusing to ship the feature that every other AI search competitor treats as their main outcome deliverable.
Why omitting to give advice is the smart bet
Gintare’s logic is simple, and once you see it you can’t unsee it.
In an AI-native stack, the “what to do next” layer is already free. The moment you hand clean data to Claude or ChatGPT, the model generates the strategy for you.
Every competitor that’s pouring investment and engineering efforts into a system that ultimately produces AI search recommendations is actually just building a feature their customer’s own AI hands them at essentially no cost – and it’s pretty challenging to position against free, let me tell you.
I’m the proof of Tredos’ theory in action. Last week I shared with you about how I pulled Trendos data, gave it to Claude Cowork alongside my Bing citation exports and SEMRush AI health audit findings, and Claude wrote the 90-day plan.
It’s because of user journeys just like this that Gintare placed a different bet. The durable value in the AI search tool category is the proprietary, high-accuracy data an agent can’t generate on its own. Trendos monitors more than 3,000 categories, and its visibility scores get more accurate the more prompts you track.
That accuracy? It’s Trendos’ moat – and every hour the team didn’t spend building a me-too recommendations tab went into producing greater data quality that compounds for each of their customers.
The thing I love about it is that Trendos’ has even built the infrastructure for the data handoff between the platform and an LLM. Trendos ships an API and a MCP server, so clients can pipe the data straight into their own agents and their own data lakes. For clients who aren’t yet AI-native, Trendos will generate a PDF of next steps, and Gintare told me those clients watch their visibility score climb within a month or two.
So, in this way, the recommendations still happen, but Trendos just lets the AI generate them instead of pretending a proprietary algorithm prescribed them. In this day and age, that honesty is surprisingly refreshing.
Is Your Startup Falling Behind Its AI-Native Competitors?
Why a GTM engineer will want to build on Trendos data
If you build go-to-market systems for a living, you’re going to love this… Trendos’ data sources are the most robust I’ve seen, covering all the following (and then some):
- Trendos covers the whole answer-engine landscape. Trendos tracks brand visibility across ChatGPT, Claude, Gemini, Google AI Overview, and Perplexity, so one score reflects the models your buyers actually use.
- It talks to your agents natively. The MCP server lets Claude (or any other AI agent) query your visibility data live, reason over it, and hand back the next move without a human exporting a single spreadsheet.
- It feeds your own systems. The API pushes data straight into your data lake, so Trendos becomes one clean input in a pipeline you control.
- It prices for automation, not seats. Per-prompt pricing means you can point an entire automated workflow at the data without paying for every service account that touches it.
For example, here’s how I run things on my own site.
- Trendos measures where my brand stands inside AI answers.
- Claude reads that data through the connection, separates confirmed problems from hypotheses, and drafts the fixes.
- My content system deploys the approved changes through its own MCP.
- Then Trendos re-measures on the next crawl.
Audit, observe, prioritize, execute, verify, with a human approving every irreversible step and agents doing the rest.
This way the data provider stays in its lane and does one thing very well. Every other part of the system – the reasoning, the drafting, the deploying, the re-checking – plugs into it. In a time where most tools in this category still want to be the entire stack, Trendos wants to be the most trustworthy input in your own stack – which IMHO, which is a far more useful thing to be.
They also keep shipping data that nobody else has. Trendos was the first to monitor ChatGPT ads, and next month they launch an index of the 1,000 most visible brands in AI engines across the United States, the United Kingdom, and Germany, split out by ChatGPT and Google AI Overviews.
New proprietary datasets, released in public, on a monthly cadence. It’s glorious.
How the whole thing started
Before Trendos had a paid product, the team started by giving away the data that proves their market exists.
Starting in December 2025, they gathered a massive public dataset, more than 600,000 prompt analyses across thousands of categories, and published it for free.
The goal at that time was not lead capture, but rather it was to validate the hypothesis that the category is real and commercially viable.
Think about it… If you want to sell into a market that’s still early… where buyers may not yet believe, a great way to start is to manufacture that buyer belief first with undeniable proof. Trendos’ free dataset drove the traffic… the paywall went up later, and only on the brand-specific insights.
Reproduce This Win: cut the free tier to widen the gap
If you run a product-led motion, you’re gonna wanna learn from this bit…
Trendos originally gave away up to 100 custom prompts on the free tier.
That was (extremely) generous… too generous in fact. For many smaller brands, 100 prompts fully answered the question “where does my brand stand,” so they never found a reason to pay. But about a month ago, the team dropped the free tier to 20 prompts and moved the 100-prompt bracket behind a $39 paywall. The results:
Paid conversions doubled, from 10% to 20%.
The lessons here for you are these:
- Free tiers convert to paid on the value gap, not the value given. If your free tier completely solves the job, you have built a competitor to yourself.
- Find the point where the free plan delivers a real glimpse of what’s possible and leaves an obvious next step. For Trendos, the free 20 prompts shows you the shape of your standing, whereas 100 prompts tracked shows you the whole picture.
- Price the gap, not the product. Of course, the $39 was never about 80 more prompts. It was way more ephemeral than that. The payment conversion actually represents the moment a user can feel what they’re missing on the free plan (and wants more).
The operator behind the bet
None of this reads as beginner’s luck once you see Gintare’s track record. She built this commercial motion after running commerce at scale elsewhere, as CEO at JumpTask and Head of Commerce at Oxylabs – and of course that experience shows up in Tredos’ stellar commercial design:
- Per-prompt pricing, never per-seat. One enterprise client has 50 people across SEO, paid, PR, and employer-branding teams on a single account. Trendos charges for usage instead of seats, because usage is where the value actually sits.
- Monthly contracts as a wedge. Competitors lock enterprises into annual deals. Trendos offers month to month, and will even match a competitor’s prompt credits to pull a locked-in client out mid-contract. In this way, confidence in the Trendos’ data supports a winning pricing strategy.
- Enterprise motion built by demand. Gintare wanted a pure product-led company. Then unhappy enterprise buyers, including ecosystem companies like Hostinger and NordVPN, came inbound because the data quality elsewhere was poor. She staffed three salespeople to catch that demand rather than spending time and money on chasing it. That’s PLG done right!
There is a discipline that underlies all of this as well. The team cuts roughly 30% of its own roadmap using one simple rule: if they’re less than 50% confident a task will produce the result they want, it waits.
Inevitably Trendos protects data quality, and ends up cutting almost everything else.
What I’d take from this
There is a clear through-line here, from the free dataset to the “missing” recommendation feature, and even the 20-prompt free tier… Know exactly what your product is for, and refuse to be talked out of it, even when the market, the competitors, and the obvious roadmap all point the other way.
For anyone building in an AI-native category, Gintare’s refusal to add the recommendations feature is worth holding up against your own roadmap. Before you build your next recommendation feature, ask one question: does my customer’s own AI already generate this the moment I hand it my data? If the answer is yes, the feature is a cost center and the data is most likely the moat.
If you want to see where your brand stands in AI search, you can pull the same Trendos data I used at trendos.com. Feed it to your own AI, and let the strategy write itself.
P.S. The morning I ran my audit, my score had moved 1.2 points in a week. I almost closed the tab satisfied. Healthy numbers are the most dangerous kind, because they talk you out of looking closer. The reason I kept digging is the same reason Gintare’s bet works. The data is only ever raw material. Your assessment of it (AI-assisted or otherwise) is what turns it into a meaningful answer.
