The system does not know which company you are
Similar names, thin third-party references, and inconsistent identity data leave the model unsure it is describing you at all.
BACKTIER
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AI Visibility Measurement · Jason T. Wade · Central Florida
AI recommend your competitors?
Measure how AI understands your brand. Fix the gaps. Track what changes.
No provider can guarantee what an independent AI platform will cite or recommend.
Entity
Is it the right company?
Retrieval
Can your evidence be used?
Citations
Where do you appear?
Accuracy
Is the answer correct?
The problem
Ranking in Google does not mean AI describes you correctly.
Search rankings and AI answers are produced by different processes. A page can rank well and still be absent from the answer a buyer actually reads. There are usually four reasons for that, and they call for different fixes.
Your pages are not the evidence it uses
An answer is assembled from whatever the system can retrieve. If your own pages are hard to fetch, parse, or quote, something else fills the gap.
It is repeating something out of date
Old bios, retired services, and stale directory entries persist in the sources these systems reach for.
Competitors have stronger corroboration
When two brands are plausible answers, the one with more independent, consistent supporting evidence tends to be named.
What we measure
Four dimensions, tested against a documented prompt set.
Every engagement starts from the same four readings, taken across ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity. These systems are non-deterministic, so we report ranges and trends across repeated runs rather than treating a single observation as a result.
Entity accuracy
Does the system identify the correct organization, with the correct people, location, category, and services attached to it?
Retrieval coverage
Can the pages and evidence that answer your buyers' questions actually be fetched, parsed, and used as a source?
Citation presence
Across a frozen set of questions, where does your brand appear, where does it not, and which sources appear instead?
Answer accuracy
When you are described, is the description correct, current, and complete enough to be useful to a buyer?
How it works
Measure, diagnose, implement, re-measure.
Individual capabilities — entity architecture, schema, retrievable content, citation tracking — sit underneath this cycle rather than being sold as separate inventions.
- 01
Baseline
Freeze a prompt set for your category and record how each platform answers it today: entity accuracy, retrieval coverage, citation presence, answer accuracy, and which competitors appear.
- 02
Diagnose
Establish which constraint is actually binding — identity confusion, insufficient evidence, inaccessible content, weak third-party corroboration, technical structure, or category positioning.
- 03
Implement
Fix the priority issues: entity architecture, structured data, answer-ready content, internal linking, source development, and legitimate external authority building.
- 04
Re-measure
Re-run the original tests on a documented schedule and report what improved, what regressed, what stayed the same, and what cannot be attributed with confidence.
Evidence
What we will publish, and what we will not.
We are preparing case studies that meet our publication standard. Until they are ready, this site does not display anonymised percentage gains, client counts, or citation multiples, because those numbers cannot be checked by the person reading them.
A published result will carry the client or property identity, the starting baseline, the frozen prompt set, the platforms tested, the measurement dates, the work implemented, the later observations, what did not improve, and the limits on attributing any of it to our work. Anything run on BackTier-owned properties will be labelled as an internal experiment, not a client outcome.
No provider can guarantee what an independent AI platform will cite or recommend. Results vary by model, category, competition, and retrieval environment.
Read the measurement standardWho it is for
Three situations where a baseline is worth running.
Professional and regulated services
Firms whose buyers ask AI systems for a recommendation, and where an inaccurate answer carries compliance or reputational cost.
B2B companies with real expertise
Organizations that already publish substantive material but are not being retrieved or cited when their category comes up.
Brands with identity confusion
Founders and companies being merged with a similarly named entity, described with outdated facts, or attributed to the wrong people.
Founder
Jason T. Wade
Jason T. Wade is the founder of BackTier and an AI visibility architect focused on entity resolution, retrieval, structured evidence, and the measurement of brand representation across AI systems. He developed the BackTier methodology and hosts the AI Visibility Podcast.
Podcast
A public record of the tests, not a credential.
The AI Visibility Podcast with Jason T. Wade publishes practical tests, conversations with practitioners, and case-based analysis of how AI systems select and describe sources. Episode count is not evidence of anything; the useful part is the findings and the sources behind them.
Browse episodesFAQ
Questions worth asking.
Everything you need to know about AI visibility, our methodology, and what working with BackTier looks like.
AI visibility refers to whether your brand is cited, recommended, or referenced when systems such as ChatGPT, Perplexity, Gemini, or Claude answer questions in your industry. Buyers increasingly begin their research inside AI assistants rather than on a traditional results page. A brand absent from those generated answers may therefore be absent from an important part of the buying journey — regardless of how well it ranks in traditional search.
Latest Insights
From the Intelligence Feed
See what AI systems currently say about your brand.
A baseline tells you how you are being identified, retrieved, cited, and described today — and whether implementation work is warranted at all.