AIVO Journal

We ran it through six AI buying journeys across ChatGPT, Gemini, and Perplexity. In four of six, the brand was displaced at Turn 1. The AI formed a competitor preference before Pepsi's product was ever seriously considered. By the time the consumer reached the purchase handoff, Olipop or

They're not unsophisticated buyers. They understood the category, ran the tools, and found the same gap every time. Visibility dashboards tell you where your brand appears across AI platforms. Share of voice, mention rate, citation frequency. The metrics are real and they are measurable. What they can'

The more precise description is this: An end-to-end LLM evidence supply chain. Meridian runs structured buying sequences across ChatGPT, Gemini and Perplexity. Anchored probes, generic probes, four-turn consumer conversations, eight-turn agentic simulations. It identifies the exact turn at which a brand is displaced, which competitor was selected instead, and what

They're not unsophisticated buyers. They understood the category, ran the tools, and found the same gap every time. Visibility dashboards tell you where your brand appears across AI platforms. Share of voice, mention rate, citation frequency. The metrics are real and they are measurable.What they can'

AIVO Journal | Product | May 2026 Nineteen of the twenty brands in our first published decision-stage audit had a T4 win rate of zero. Not low. Zero. Every one of those brands had invested in AI visibility. Most scored reasonably well on citation frequency. Some appeared in hundreds of AI responses

The measurement conversation in AI search has stalled at the wrong question. And the budget consequences of that are about to become visible. Over the past eighteen months, a category of platforms has emerged to help brands understand their position in AI-generated responses. Citation volume. Mention rate. Share of voice

Many leaders are celebrating high brand health metrics while silently losing the battle for the actual purchase. Here is the breakdown of the phenomenon currently reshaping the commerce landscape: The Brand vs. SKU Paradox Traditional search and social media often reward broad brand authority. However, in the age of Agentic

Profound published a 3,000 word comparison of itself against AthenaHQ this week. It is the wrong comparison. Here is the gap both platforms share. There is a turn in every AI buying conversation that the entire GEO and AEO category has collectively decided not to measure. It happens when

A peer-archived study of five major brands reveals a structural gap between AI visibility and AI purchase recommendation outcomes The GEO and AEO category was founded on a correct insight. Traditional search metrics don't measure brand performance in AI-generated responses. Citation frequency, share of AI voice, and first-prompt

When a mass market brand loses the AI recommendation, it is usually a content architecture problem - the model can't find the structured evidence it needs to pass the criteria filter. Fix the evidence layer, improve the position. For luxury brands the problem runs deeper. The brand equity

The channel is now accessible to the buyers who care most about attribution. The measurement infrastructure they need is not there yet. ChatGPT advertising launched in February 2026 as an enterprise CPM product. Ten weeks later it has a self-serve ads manager, CPMs down from $60 to $25, minimum commitments

How organic inference position became the most important variable in media planning — and what agencies need to measure before a single dollar enters ChatGPT inventory The brief that nobody can answer yet Every agency holding company is fielding the same client question in 2026: should we be on ChatGPT advertising,

AIVO Evidentia · Working Paper WP-2026-04 · April 2026 DOI: 10.5281/zenodo.19588522 Abstract This article announces the publication of brand.context — a machine-readable brand context standard designed for consumption by AI agents during commerce and purchase recommendation tasks. brand.context is the implementation layer of the AIVO Evidentia Decision-Stage Filter

Why the brand you can see in AI is not the brand that wins the sale Every week, brands across the world pay for tools that tell them how often AI mentions them. Share of voice. Citation frequency. Mention rates. The dashboard updates. The numbers go up. The quarterly report

A CODA score of 8/100 on the day of acquisition. Here is what that means. On the day Unilever announced the acquisition of Grüns for $1.2 billion, we had fresh probe data from a buying sequence we had run that morning. We did not plan the timing. We

Institutional Representation Risk in Financial Services Abstract As large language models increasingly mediate product and institutional selection in retail financial services, representation outcomes have become non-uniform across AI systems and unstable over time. Identical queries can produce divergent institutional winners across models, while periodic model updates alter survival to final

Abstract The Q1 2026 Global Banking AI Decision Index demonstrated that large language model recommendation systems exhibit measurable structural concentration, late-stage substitution, and cross-platform instability. Subsequent industry response and media coverage elevated AI-mediated selection into executive discourse. This article argues that the decision layer now requires formal institutional oversight. AI

Evidence from the Global Banking AI Decision Index (Q1 2026) and Subsequent Industry Response Authors: Tim de Rosen, Paul Sheals Affiliation: AIVO Evidentia Ltd Date: February 2026 Abstract The AIVO Surface™ Global Banking AI Decision Index (Q1 2026) evaluates how major banking institutions perform at the decision stage within large

The Infrastructure Layer for AI-Mediated Purchase Decisions 58% of buyers now use AI systems to choose between competing brands. Not to research. Not to browse. To choose. “Which CRM should we buy?” “Book me a hotel in Paris.” “What’s the best anti-aging serum?” AI systems are answering these questions

Digital leaders have spent two decades optimizing for visibility. Rank position. Click-through rate. Conversion rate. Share of search. But AI assistants are introducing a structural shift. They do not distribute attention across ranked links. They compress category decisions into a single recommendation. That compression changes competitive dynamics. Presence Is Not

If an AI assistant generates a materially adverse or misleading representation of your institution today… Could you reconstruct it six months from now? Precisely. • Exact prompt • Exact model and version • Exact output • Exact timestamp • Demonstrable testing conditions Or would you rely on screenshots and internal recollection? In controlled testing, we

When the U.S. Food and Drug Administration formalized its risk-based credibility framework for AI in healthcare, commentary focused on compliance cost and innovation friction. That interpretation understates the shift. The FDA normalized AI as regulated decision infrastructure. Once AI is treated as infrastructure, accountability expands beyond what an organization