# Introducing Lanesra: The GEO Platform Built Web3-First By Joha J. Sulaymonov, Co-CEO, Lanesra AI · 2026-09-12 · AEO for Web3 > Lanesra is a Web3 GEO platform that tracks how AI engines describe, cite, compare, and recommend blockchain projects. Built for brands, agencies, and individual professionals across ChatGPT, Gemini, Google AI Overviews, AI Mode, Perplexity, and Copilot. Source: https://lanesra.ai/blog/introducing-lanesra-web3-geo-platform The short answer What Lanesra is Lanesra is a GEO platform that measures how AI engines describe, cite, compare, and recommend brands. It tracks visibility across ChatGPT, Gemini, Google AI Overviews, Google AI Mode, Perplexity, and Copilot, and it was built Web3-first: the product's entity model, prompt handling, and citation attribution were designed around blockchain projects before being generalized to other industries. Every brand now has a second reputation it cannot see. Not what people say about it, but what AI engines say about it, in millions of private conversations, when someone asks which product to use, which protocol to trust, or which option is best. Lanesra exists to make that reputation measurable. This piece explains what the platform is, who it serves, why Web3 was the first industry we built for, and what it actually does. ## What Lanesra Is > Lanesra is the analytics layer for AI search: AEO, GEO, and Agent Experience for brands and agencies, in every market and model. Practically, it is a monitoring platform. You define the prompts your audience actually asks. Lanesra runs them repeatedly across AI engines, parses every response, and turns the output into structured metrics: whether you were mentioned, whether you were cited, where you appeared in the answer, how you were described, and which competitors occupied the positions you did not. The word "repeatedly" is doing significant work there. Generated answers are non-deterministic, so a single query result is a sample, not a measurement. Continuous sampling is the difference between an anecdote and a trend line, and it is why AI visibility is a monitoring problem rather than a one-time audit. ## Why Web3 Was the First Industry The honest answer is that Web3 broke every assumption in generic AI visibility tooling, and building for the hardest case first produced a better product than building for the easiest case would have. **What Web3 forced us to build differently** 1 Entity separation A single blockchain project name maps to a network, a token, a framework, a foundation, a DAO, and an ecosystem. Generic tools collapse all six into one brand and produce a meaningless average. Lanesra tracks them separately. 2 Multi-domain citation attribution Web3 projects spread authoritative information across marketing sites, docs subdomains, GitHub, governance forums, and third-party aggregators. Attributing a citation to the right surface required going beyond single-domain tracking. 3 Name collision handling Crypto naming conventions borrow heavily from common words. Tracking a project called Base or Sui with naive keyword matching returns mostly noise, so prompt sets need context qualification built in. 4 Vocabulary that moves quarterly Category terminology in Web3 shifts faster than any static keyword database can follow. Prompt sets had to be treated as living inputs rather than a configured-once list. 5 Non-SaaS trust signals Web3 trust runs on audits, TVL history, exploit records, and governance transparency rather than review-platform presence. Treating audit and security mentions as first-class visibility inputs came directly from this. Every one of those capabilities generalizes. A pharmaceutical company has an entity separation problem between its corporate brand, its drug names, and its research arm. An enterprise software vendor has documentation spread across a marketing site, a docs subdomain, and a GitHub org. Building for Web3 first produced a platform that handles complex entity structures generally, which is why Lanesra now serves brands across industries rather than crypto exclusively. ## What the Platform Does Visibility tracking Mention rate, share of voice, and answer prominence measured per prompt and per engine, not pooled into a single number that hides divergence between models. Citation analysis Whether an engine linked a retrievable URL or merely recalled your name, plus which of your domains and subdomains the citation actually came from. Competitor comparison The same metrics computed for every named competitor, with ranked position per prompt, so you can see exactly who occupies the slots you are absent from. Gap detection Prompts where competitors appear consistently and you never do, ordered by prompt demand, which converts measurement into a content and PR roadmap. Sentiment and framing How you are described qualitatively, separating a tone problem (hedged, caveated, tied to a past incident) from an absence problem. Topic and intent breakdown Visibility segmented by query intent: non-branded, informational, and commercial, so you can tell awareness gaps from purchase-intent gaps. The through-line is that every metric exists to produce the last one. Visibility tracking, citation analysis, competitor comparison, and sentiment are diagnostic. The gap list is the deliverable: a prioritized set of prompts where demand exists, competitors are winning, and you are absent. ## Who It Serves Brands and protocols tracking their own visibility Lanesra for Companies **Prompt-based tiers.** For a single brand monitoring its own position across engines. Priced by the number of prompts tracked, since prompt coverage is the variable that actually determines the quality of the data. Agencies managing visibility for multiple clients Lanesra for Agencies **Credit-based tiers.** Multi-client workspaces with pooled credits, so an agency can allocate tracking capacity across accounts rather than buying a fixed seat per client. Individual professionals whose name is a search query Lanesra Persona **Per-person monitoring.** Founders, executives, doctors, lawyers, and consultants monitoring how AI engines describe them personally. Excludes politicians and any sentiment-manipulation features by design. ## Engine Coverage **Core coverage, all tiers:** ChatGPT Gemini Google AI Overviews Google AI Mode Perplexity Copilot **Enterprise and custom coverage:** Claude Grok DeepSeek Qwen Kimi K2 Mistral Jais ALLaM Fanar Falcon Includes sovereign and regional models for government and regulated enterprise use cases in the Gulf and Asian markets. Per-engine breakdown is not a reporting preference, it is a requirement. Engine behavior diverges substantially on identical prompts: a project can hold strong position in Perplexity, where grounded retrieval favors well-documented projects, and be nearly absent from ChatGPT, where training-weight recall favors longer public histories. A single blended score across engines hides exactly the information you need. ## How Brands and Agencies Use It ### Brands and protocols The typical entry point is a baseline audit: define forty to two hundred prompts covering real use cases, run them across engines, and find out how often the project appears and how it is described. The common first finding is not a low score but a wrong description, positioning that was superseded a year ago and still lives in two of three engines because an old landing page and a stale directory listing were never corrected. From there it becomes an operating loop. The gap list drives content and PR priorities. Continuous tracking confirms whether published work moved anything. Competitor position tracking shows whether a slot was won or a rival simply slipped. ### Agencies Agencies use Lanesra in two modes. In pitch mode, running a prospect's category prompt set produces a concrete finding to open a conversation: here are eleven prompts where your competitor is recommended and you are not. In delivery mode, it becomes the reporting layer that makes AEO and GEO retainers measurable, which is the thing conventional SEO reporting cannot provide for this work. The credit-based model exists because agency workload is uneven. Pooled credits allocate across accounts rather than forcing a fixed purchase per client, which matters when one client needs two hundred prompts and another needs twenty. ### Individual professionals Lanesra Persona covers people whose name is already a search query: founders, executives, doctors, lawyers, consultants. The mechanics are similar, the entity model is different, and the guardrails are explicit. Persona excludes politicians and does not offer sentiment-manipulation features. Monitoring how you are represented is the product. Engineering how others are represented is not. ## Where This Is Going The generative layer is early enough that most brands have no instrumentation at all, which is unusual. In traditional search, measurement infrastructure arrived alongside the channel. Here the channel arrived first and measurement is catching up, which means the current advantage goes to whoever starts measuring while competitors are still guessing. What we are confident about: AI answers are becoming a primary discovery and trust surface, engine behavior will keep diverging, and brands that cannot see their own representation cannot manage it. What we are not going to pretend: nobody knows the exact weighting inside these models, and any platform claiming to have reverse-engineered a ranking algorithm is overselling. What Lanesra measures is output, consistently, over time. That is sufficient to act on, and it is more than most brands have today. ### See what AI says about your project Track visibility, citations, competitors, and sentiment across every major AI engine. Sign up and run your first baseline. [Sign up free](https://app.lanesra.ai/signup) [Book a demo](https://calendar.app.google/hz1oWXVrnvCf5eYg6)