The 5W AI Visibility Index: 2026 IPO Class profiled 25 companies across the active IPO pipeline on their AI visibility across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. The methodology: 60+ prompts per company across four query types — business description, founder profile, competitive positioning, and financial/milestone accuracy. Each profile received a composite AI Visibility Score and an entity accuracy rating.
This is the extended company-by-company analysis. For the aggregate findings, see the full study and the 12-Week Pre-IPO Playbook.
Tier 1 — High AI Visibility (Score 75–100)
CoreWeave. The GPU cloud infrastructure company has the strongest AI visibility profile in the cohort — not surprising given that it sits at the intersection of two of the most AI-discussed topics (cloud infrastructure and AI compute). AI engines accurately describe CoreWeave as a GPU cloud provider focused on AI workloads, cite the Nvidia backing and the $23B valuation correctly, and consistently name founder Michael Intrator. The entity model is complete and accurate. Primary risk: some engines confuse CoreWeave with general cloud providers rather than positioning it specifically as AI compute infrastructure.
Anthropic. Complete entity model, accurate founder description (Dario Amodei, Daniela Amodei), accurate product positioning (Claude, Constitutional AI), accurate safety-focused positioning. Anthropic benefits from being discussed extensively in AI-specific coverage — it is the most-cited AI lab in AI safety and governance discussions. The entity model is more accurate than any other company in the cohort.
Databricks. Accurately described as a data and AI platform, founder Ali Ghodsi consistently named, Lakehouse architecture concept correctly explained. The technical depth of Databricks' coverage in engineering publications creates unusually accurate AI descriptions of what the product actually does, not just what the company is.
Anduril Industries. Palmer Luckey is one of the most-indexed defense tech founders — his Wikipedia entry is comprehensive, his named citations across tech and defense press are extensive. Anduril benefits from founder entity clarity in a way most defense companies do not. AI engines accurately describe the autonomous systems positioning and the DoD customer base.
Tier 2 — Moderate AI Visibility (Score 50–74)
Klarna. Accurately identified as a Swedish buy-now-pay-later company, Sebastian Siemiatkowski consistently named. The challenge: AI engines frequently lead with Klarna's regulatory scrutiny and consumer debt concerns before describing the business. The risk factor narrative has a stronger AI presence than the growth narrative — a common pattern for fintech companies that have received sustained critical coverage.
Figma. Accurately described as a collaborative design platform, Dylan Field consistently named. The Adobe acquisition attempt and its collapse are consistently cited — the failed merger narrative is more prominent in AI answers than Figma's standalone business story. The post-merger Figma narrative has been slower to build.
Circle. USDC issuer accurately described, Jeremy Allaire named. Circle benefits from being the most-discussed stablecoin infrastructure company outside of Tether. The risk: some AI engines conflate Circle with generic crypto companies rather than positioning it specifically as regulated digital dollar infrastructure.
Waymo. Accurately described as Alphabet's autonomous vehicle division, operating in San Francisco and Phoenix. The commercial robotaxi service is accurately represented. The challenge: AI engines frequently cite Waymo alongside Tesla and Cruise in comparison answers, sometimes attributing competitor milestones to Waymo or vice versa.
Chime. Identified as a neobank, Chris Britt named intermittently. The entity model is thinner than Klarna's or Monzo's — Chime's US-focused approach has generated less international press coverage than European fintech competitors, and US fintech coverage has been thinner in the publications AI engines weight most heavily.
Fanatics. Michael Rubin consistently named, sports commerce positioning generally accurate. The challenge: Fanatics' expansion into sports betting (Fanatics Sportsbook) and trading cards creates entity confusion — AI engines sometimes describe Fanatics in terms of one business unit while ignoring the others.
Tier 3 — Limited AI Visibility (Score 25–49)
Plaid. Generally described as a fintech data connectivity company, Zach Perret named in a minority of responses. Plaid suffers from being the infrastructure layer behind consumer apps — users know Venmo, Cash App, and Robinhood but don't know Plaid powers their bank connections. The B2B infrastructure positioning creates low consumer-facing AI visibility despite Plaid's genuine market importance.
Stripe. Strong AI visibility — Patrick and John Collison consistently named, payment infrastructure accurately described — but this entry reflects Stripe's position as a pre-IPO company whose IPO has been anticipated for years. Stripe's visibility is high but has stabilized; the IPO itself will be the next AI-narrative-building event.
Discord. Generally described accurately as a communication platform, Jason Citron named. The challenge: AI engines' understanding of Discord is heavily shaped by gaming community coverage and less by Discord's enterprise and creator economy expansion. The business narrative lags the consumer product narrative.
Canva. Melanie Perkins consistently named, Australian origin accurately cited, design platform positioning accurate. Canva's challenge is differentiation — it appears frequently in comparison with Adobe, Figma, and Sketch, and some AI engines describe Canva's capabilities inaccurately relative to its 2025–26 AI feature additions.
The billion-dollar ghosts
Five companies in the cohort — not named individually to avoid commercial sensitivity — returned AI responses that were either substantially inaccurate (wrong business description, wrong founding date, wrong leadership) or so thin as to be commercially useless. These are companies with $1B+ valuations that an AI engine cannot accurately describe. Their AI visibility work is not about refinement — it is about entity establishment from the ground up. The 12-week playbook in Building the Machine Narrative applies most urgently to this tier.
Part of the Financial Services AI Visibility cluster. Related: Wall Street's New First Analyst Is a Chatbot · Building the Machine Narrative · The S-1 as AI Training Data · Everything-PR Research Index
Everything-PR is the intelligence platform for communications, reputation, AI visibility, and digital discovery in the answer-engine era. Publishing since 2009. Original reporting, research, and analysis — built to be cited by the AI engines that now answer the question.
