When a buyer asks an AI chatbot which software vendor to choose, the answer can shape a purchasing decision before a human salesperson ever gets a chance to speak. A new study from Pyra suggests those AI-generated answers frequently contain errors that could mislead prospects — and the company has built a tool to help brands catch those mistakes.
Prime AI Visibility Benchmark provides marketing teams evidence to investigate inaccurate product information and gaps in brand visibility
Buyers ask AI which vendor to choose before they ever reach a company’s website, and this benchmark shows answers that may misstate a brand, or point to outdated products or services.”— Alex Mannine, Co-Founder and Chief AI Officer at Pyra
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NEW YORK, NY, UNITED STATES, October 30, 2026 /EINPresswire.com/ — Pyra, which builds AI agents for sales, marketing, finance and operations, has rolled out a new addition to its Prime AI Visibility platform. The Prime AI Visibility Benchmark is designed to show marketing and revenue teams exactly how AI answers portray their brands when buyers conduct research.
The launch comes alongside a Pyra-led study using the benchmark that uncovered a troubling pattern: one in ten checkable claims contradicted dated snapshots of the brands’ own websites, including an answer that referenced outdated product plans. For businesses, the practical takeaway is knowing what to investigate and which content to improve. The platform accomplishes this by saving answers and source links for selected buyer questions, allowing teams to review brand appearance, prioritize page updates and compare later checks.
Product details need checking
The Prime AI Visibility Benchmark study identified 183 contradictions among 1,821 checkable claims. Error rates varied by category and model configuration, reaching 25.7% for ecommerce platforms and 26.2% for CRM software. In one documented answer, OpenAI’s GPT-6 Sol with web search off listed BigCommerce’s plans as Standard, Plus, Pro and Enterprise. The benchmark’s October 23 fact sheet recorded Core, Growth, Scale and Performance from BigCommerce’s own pricing page.
“A pricing page can be up to date while an AI answer still describes an old plan,” said Alex Mannine, Co-Founder and Chief AI Officer at Pyra. “Teams need to see what prospects could be told about their products. Prime gives them saved answers and sources to review, so they can decide what to improve and check the answers again.”
Citations need scrutiny too
Among the four search-enabled configurations, the share of checked citations that resolved to a live page and supported the attached statement ranged from 37.1% for Perplexity Sonar, accessed through OpenRouter, to 91.4% for Claude Sonnet 5.5. Dead links and unsupported citations counted against the rate; links that could not be verified were excluded. A citation alone did not establish that an answer was supported. All three models tested with search both enabled and disabled had lower overall factual-error rates with search enabled in this sample. The published results also show differences by category, giving teams a reason to check the questions relevant to their own products.
Brand visibility varied across answers
In the 63 project-management discovery answers, Asana appeared in all 63 and Smartsheet in two. These counts measure mentions, not endorsements. Repeat answers also varied: 284 of 350 question-and-configuration pairs, or 81.1%, produced the same measured outcome across all three runs. The other 66 pairs changed, underscoring the value of repeated checks when assessing a brand’s visibility.
The benchmark tested 50 questions about 25 brands in five categories across seven model configurations, with three runs per question. All 1,050 answers were collected on October 23, 2026, through APIs using the questions as written, provider defaults and no system prompt. Two AI graders checked factual claims against the dated fact sheet; all 82 disagreements were resolved against source snapshots. Citation support was also assessed by an AI judge. The study measures this sample of API answers, not consumer-app sessions or live Google AI Overviews. It does not measure sales impact or establish a universal model ranking.
Teams can start a free Flash check at app.primeaivisibility.com/sign-up. Flash includes one completed check per calendar month for up to five buyer questions across ChatGPT, Claude and Perplexity, with up to 15 saved answers and their available sources. No credit card is required. This is a visibility check; the benchmark’s separate claim-grading study is not included in the offer.
Pyra AI Platforms
Pyra’s AI agents help B2B companies grow revenue, make faster and better-informed decisions, and cut repetitive work across their sales and marketing teams. Prime AI Visibility shows brands how they are discovered, cited and recommended when buyers research through ChatGPT and other AI answer engines. Primey, Prime AI’s built-in assistant, turns that visibility data into plain-language analysis and a clear next step. Its Predictor evaluates each AI search opportunity using buyer question data, existing organic visibility, competitive citation evidence and site authority, then shows marketing and revenue teams where investment is likely to pay off and where it is not.
Pyra’s Lead Seeker finds the right buyers in real time using fresh contact intelligence, and its Lead Outreach platform turns those prospects into personalized conversations across email, LinkedIn, WhatsApp and phone. Together, these products cover the B2B revenue cycle from AI visibility to prospecting and outreach.
About Pyra
Pyra builds AI agents for sales, marketing, finance and operations. Prime AI Visibility helps teams review how their brands appear in selected AI answers, use saved evidence to plan content improvements and compare later results. Learn more at pyrabuilds.ai.
NOTE FOR EDITORS: The scored-answer dataset, question set and charts are available under Creative Commons Attribution 4.0 at primeaivisibility.com/benchmark. The CSV contains scores for 1,050 answers; it does not include the full answer text. The published methodology explains grading, exclusions and limitations.
Nick Mitsis
Percepture, Inc.
nmitsis@percepture.com
Why it matters: As AI chatbots become a primary research tool for B2B buyers, the accuracy of those answers directly impacts brand perception and revenue. Pyra’s benchmark highlights that even leading AI models frequently serve outdated or incorrect product information, making it essential for marketing teams to actively monitor and correct their AI visibility — rather than assuming their website’s accuracy translates to what chatbots tell prospects.
