As conversational AI becomes a primary gateway for information, a new industry has formed around Generative Engine Optimization (GEO) – a discipline focused on monitoring how brands appear in AI-generated answers. Analysts estimate that traditional search volume could fall by 25% by 2026, with users increasingly turning to systems such as ChatGPT, Gemini, Claude, and Perplexity for direct, synthesized responses. This has created demand for tools that track how often brands are named in AI-generated outputs, particularly as these models draw from a narrow pool of sources.
Over the past year, several startups have launched visibility dashboards designed to measure such mentions. Most of these tools offer single-run snapshots or weekly scrapes that provide basic counts across AI engines. While the sector is young, competition within it has intensified, with companies raising funding by presenting themselves as early movers in a rising market. Despite this, interviews with agencies and B2B operators indicate growing frustration with limited transparency, partial coverage, and the lack of meaningful prediction in existing tools.
NetRanks, an internationally distributed startup founded by Reha Sönmez, has taken a different route. Rather than commercializing visibility features – currently the core product for most GEO entrants – the company announced it will offer free visibility snapshots to all users. The decision has unsettled rivals whose business models rely on selling access to mention data. As Sönmez explained, “Visibility alone has become a commodity. Charging for it no longer reflects the needs of teams navigating AI-driven discovery.”
Why NetRanks Is Disrupting the Existing GEO Asset Model
NetRanks’ decision to make a feature competitors charge for completely free reflects a broader shift in AI visibility. What once felt difficult to access is now far more common. Many marketing teams run their own prompt tests, build simple internal scripts, or scrape AI engines to see how their brands appear. As more organizations generate this information themselves, basic visibility dashboards – especially those limited to mention counts – hold less value than before.
Analysts say the GEO market is already showing signs of fragmentation. Several tools offer nearly identical outputs, relying on single-engine scrapes, small prompt sets, or partial citation checks. Some competitors advertise predictive capabilities, yet their public demos often present static charts rather than genuine modelling. This gap between claims and delivery has led to growing skepticism among agencies interviewed for this story.
NetRanks is responding by offering baseline visibility snapshots at no cost, with continuous tracking available through paid tiers. Its subscription model focuses instead on forecasting and actionable steps, the components competitors promote but have not consistently provided. “If the market is crowded with identical dashboards, then the logical move is to remove the barrier entirely,” Sönmez said. “Once baseline visibility checks are free, users will choose based on who can actually predict outcomes. That is where the differentiation begins.”
The Market Dynamics Behind Free Visibility
Making visibility snapshots free also reflects how AI-generated answers function. Large language models draw from several sources at once, and their outputs shift with dataset updates, prompt variations, and tuning changes. Basic visibility alone cannot explain why a brand appears, what influenced the answer, or what might change in future model updates.
Marketing leaders interviewed said that visibility dashboards are helpful for initial awareness but fall short for planning or forecasting. Without modelling, teams cannot gauge whether new content, messaging, or authority will meaningfully affect future mentions – something traditional SEO tools once provided through metrics like keyword difficulty and link scores.
By removing the price tag from visibility, NetRanks is putting pressure on a market built on paid access to simple dashboards. Competitors may find it harder to justify subscription fees as visibility becomes widely accessible. Agencies using multiple GEO tools say they are already looking to consolidate around platforms with deeper diagnostic capabilities.
Model-Agnostic Predictive Capabilities as a Differentiator
NetRanks’ predictive capabilities rely on continuous, multi-engine scanning that produces longitudinal datasets rather than isolated snapshots. The company says its modelling identifies which sources shape AI-generated answers, how rank position may shift under different scenarios, and which actions are most likely to raise a brand’s AI Share-of-Voice. These findings are then translated into prioritized tasks ranked by expected impact.
A review of NetRanks’ technical materials shows that its model-agnostic probing spans several major AI engines at once. Instead of relying on public-endpoint scrapes, the system uses large, structured prompt sets to test responses across categories, regions, and query types – an approach analysts say more closely reflects actual user behavior.
The company positions prediction as the point where GEO moves from monitoring to measurable influence. As Sönmez noted, “Teams do not just want to know where they appear. They want to know what will change their position tomorrow.” Whether similar methods spread across the sector will depend on how quickly enterprises shift toward AI-driven discovery and how much importance they place on forecasting accuracy.
Free Visibility and the Changing Economics of GEO
The GEO sector remains young, and many of its business models were formed before benchmarks or best practices existed. By making visibility snapshots free, NetRanks is challenging early assumptions about what constitutes proprietary value. The move echoes earlier moments in digital history when foundational analytics shifted from premium offerings to widely accessible utilities.
AI-generated answers are increasingly influencing how information surfaces, and demand for predictive and diagnostic systems is expected to grow alongside that shift. Whether NetRanks’ strategy sparks broader industry changes or leads to consolidation will depend on how competitors react to the erosion of their primary revenue stream. What remains clear is that AI-driven visibility now requires more than simple mention tracking, and the companies most likely to advance the field will be those that move beyond visibility into measurable, repeatable prediction.
Organizations looking to assess their AI visibility and understand what actions are most likely to improve future rankings can use NetRanks to test how their brand appears across ChatGPT, Gemini, Claude, and Perplexity, with visibility provided at no cost and forecasting reserved for deeper optimization.