Search gave you ten blue links. AI gives an answer. If your brand isn’t named in that answer, cited as evidence and tied to the right page, you don’t exist in that moment.
That’s why we built TAV — Trusted AI Visibility. It’s a 0–100 score that summarizes how strongly and reliably your brand shows up inside AI answers across ChatGPT, Gemini, Claude, Perplexity and others. Think of it as a “truth-weighted share of voice” for AI.
This post is the deeper story: why TAV matters, what it captures that SEO can’t and how teams use it to win budget and fix pages that models will actually cite.
The problem no dashboard was built to show
Traditional metrics assume a results page. Assistants don’t work that way. They synthesize, paraphrase and selectively cite. Two uncomfortable truths follow:
- Being good is not the same as being cited.
If the model can’t find a page that perfectly fits the question, it will cite a competitor or a generic authority. You might be “mentioned” in text but unsupported in sources. That’s invisible to most analytics. - Trust is multiplicative.
A glowing paragraph can collapse if the sources point elsewhere. Likewise, three solid citations can be undermined by a misattribution or a negative tone. You need a metric that respects this compounding.
TAV is that metric.
What TAV captures in one glance
TAV compresses five realities of an AI answer into a single number:
- Coverage: Are you the subject or just adjacent?
- Prominence: Do you lead the list or sit below the fold?
- Evidence: Do citations include you, the right page and credible third parties?
- Freshness: Does the answer signal recency so it’s safe to trust right now?
- Risk: Are there signs of misattribution, hallucination or negative framing?
If an answer doesn’t both name you and cite you, TAV for that answer is 0. No partial credit. That hard gate keeps teams honest.
Then we average across runs by model, locale and topic, so you can say things like:
“US-EN, ‘pricing’ intents, ChatGPT: TAV 83 last 30 days, up +11 since the pricing page rewrite.”
Why a composite score beats a checklist
Checklists are brittle. They inflate progress when small items improve but the overall experience remains untrustworthy. TAV is multiplicative by design: if one dimension breaks (say, misattribution), your score drops even if everything else looks great. That mirrors how human readers assign trust: a single red flag erases goodwill fast.
Executives get a clean number. Practitioners get levers to move it.
What “good” looks like in practice
Scenario A: The category leader
You’re first in a ranked answer. The text names your brand and the sources include your best-fit page plus a respected third-party. The copy references a recent update. Tone is neutral to positive.
Typical TAV: high 80s to 100.
Action: defend the lead, expand to adjacent intents.
Scenario B: The credible also-ran
You appear third with a short blurb. One citation points to you, but it’s your homepage, not the intent-specific subpage. No freshness cues.
Typical TAV: 50s to low 60s.
Action: ship a purpose-built page and earn brand-plus-authority citations.
Scenario C: The ghost
You’re mentioned in text, but the model cites only competitors and neutral guides.
TAV: 0.
Action: create the cite-worthy page and link to it internally so assistants can justify using it.
Why executives care
- It aligns to business reality.
Inside the answer is where influence happens. TAV tells you if you’re in that room with receipts. - It separates motion from progress.
Traffic and impressions can rise while TAV stays flat. That’s a signal to stop polishing the wrong pages. - It prioritizes investment.
TAV by intent shows where a single page can unlock dozens of answers across models. That is efficient budget.
Why practitioners care
- Pinpoint fixes.
A drop driven by “evidence” vs “prominence” calls for different work: citations and alignment vs copy and sectioning. TAV shows which lever matters. - Time-series truth.
Ship the new integrations page Tuesday. See TAV for “does it integrate with X?” jump by Friday. No waiting for quarterly rollups. - Model and locale nuance.
Some assistants overweight authorities, others elevate brand pages faster. TAV exposes those patterns so you tune content per surface.
Benchmarks to set expectations
- 80–100: strong and reliable. You’re named, cited and aligned.
- 40–79: visible but uneven. You’re in the conversation, not leading it.
- 1–39: weak or risky. Evidence and tone issues dominate.
- 0: you weren’t both mentioned and cited. Treat as a miss.
We also track supporting signals alongside TAV: wins/losses, citation alignment, misses, explicit recommendations, volatility and citation bias. These explain why the score moved.
How teams move TAV in a single sprint
- Build the cite-worthy page for each high-value question.
Intent pages beat catch-all pages. “/pricing,” “/integrations,” “/compare/you-vs-competitor,” and focused docs outperform homepages. - Make facts scannable.
Short, extractable definitions and specs near the top. Assistants quote what they can parse fast. - Link internally like you mean it.
Point relevant pages to the intent page with clear anchors. You’re teaching the crawler what to pick. - Prove recency.
Visible “Updated” markers reduce model hesitation and nudge answers to cite you. - Balance your sources.
Win your own citation and court a neutral authority. Models trust a mix. - Eliminate risk flags.
Fix misattributions and ambiguous claims. The fastest TAV gains often come from removing doubt.
What makes TAV different from SEO metrics
- It’s answer-centric, not SERP-centric.
- It’s entity-aware, tuned to brand mentions and page intent, not just keywords.
- It is trust-sensitive, penalizing misattribution and hallucinations.
- It’s designed for experiments, showing movement per model, per locale, per week.
You can keep your SEO stack. TAV fills the gap that matters in the assistant era.
The takeaway
AI is already the front-door interface for research and recommendations. If you aren’t named, cited and aligned inside the answer, you’re out. TAV turns that reality into a measurable, budget-worthy target. It tells leadership whether you’re winning and tells practitioners exactly what to fix.
If you want to see your current TAV by model, locale and intent, we can run a quick baseline against the questions your customers actually ask. Then we’ll show which pages to ship next to move the number fast.
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