How we ran an AI Visibility Audit on ourselves — and what we found
Most marketing agencies start with case studies of clients you can't verify. We started by auditing ourselves. Here's the raw score, what it told us, what we changed, and what we plan to publish every month so you can watch the trajectory.
Every marketing agency tells you their work moves the needle. Most of them ship a deck full of case studies that you cannot independently verify, written by the agency, attributed to clients with first names and last initials. You read "Mike R, HVAC operator, Austin TX, 47% reduction in cost-per-job in 90 days" and you have no way to call Mike R, no way to confirm the baseline, no way to know if Mike's improvement was the agency's work or a Q3 demand spike.
We deliberately do not have any of those. TNova Labs was founded in 2026. We have zero retroactive case studies because we have not had any clients yet. The honest version of "proof of work" we can offer is to run our methodology on ourselves first, publish the raw numbers, and show the trajectory monthly. That's what this post documents.
What we measured on day 1
We ran the full TNova methodology — 30 standardized homeowner-style queries × 4 AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews) — but adapted for our category ("AI visibility agency for HVAC", "agency that audits AI citations for service businesses", etc.) instead of homeowner HVAC queries. We also ran the entity-strength audit across the same 12 directories and the GBP completeness check.
Day 1 starting score was honestly bad. The agency category is crowded — "AI marketing agency" returns established names with hundreds of mentions across press releases, podcast appearances, and industry directories. We had a fresh website, no press, and no presence in the AI agency directories that exist (yes, those exist).
| Component | Score | Notes |
|---|---|---|
| AI citation share | 1.7% | Named in 2 of 120 prompt-engine combinations |
| Entity strength (NAP) | 42/100 | GBP not yet claimed; 0 listings beyond LinkedIn company page |
| GBP completeness | 0/100 | Profile not yet claimed |
| Lead flow | 82/100 | Contact form works; site speed is good; missing schema on some pages |
| AI Visibility score (overall) | 12/100 | Bottom decile relative to established AI agencies |
Source: TNova Labs internal audit, run on tnovalabs.com
Twelve out of one hundred is in the bottom decile of agencies in the broader "AI marketing" category — which makes sense, because we are a brand-new business with no operating history. The point is not the score itself; the point is that the score is a starting line, and the trajectory from here is what tells you whether the methodology works.
What the audit told us — concretely
Three findings shaped what we did next.
Finding 1 — citation share gap concentrated in comparison and trust intents
When we broke citation share down by intent category, the biggest gap was in comparison queries ("alternatives to {established AI agency}", "new vs established AI marketing agencies") and trust queries ("honest AI visibility agency", "AI agency that doesn't oversell"). The AI engines either named no one or named the established players. Our category positioning — independent, founder-led, outcome-based — is exactly the angle a comparison or trust query should match. We weren't matching because the AI didn't have any signal that we existed.
Finding 2 — entity strength was zero outside the website
We had a great website (we built it) but no presence anywhere else. No GBP claim. No Bing Places. No Apple Business Connect. No BBB. No mentions on any agency-roundup site. The AI engines' citation graphs had nothing to retrieve about us. Even if a homeowner asked the right query, the AI had no source to cite.
Finding 3 — schema markup was strong on homepage, weak on internal pages
Our homepage had Organization, LocalBusiness, and WebSite schema. Our /pricing page didn't have ServiceCatalog. Our /about page didn't have Person markup for the founders. Our (then nonexistent) /methodology page wasn't being rendered. We fixed all of this within a week. Google Rich Results Test confirmed valid markup on every page after the fix.
What we changed in the first 30 days
1. Claimed and completed Google Business Profile for TNova Labs (Cleveland metro). Photos, hours, services, attributes, every available field.
2. Submitted to Bing Places, Apple Business Connect, BBB, and 8 industry-specific agency directories.
3. Added schema.org markup to every page — Service + OfferCatalog on /pricing, Person markup on /about, FAQPage on every blog post, BreadcrumbList everywhere.
4. Published a public methodology page (/methodology) explaining how we calculate the AI citation score. This is GEO-cite-friendly content — exactly the kind of definitional reference an AI engine quotes.
5. Added a static llms.txt at the site root so LLM crawlers (GPTBot, ClaudeBot, PerplexityBot) have a curated list of indexable pages.
6. Started this blog series — 4 posts over 4 weeks, each authored to the GEO content rubric (TL;DR, FAQ, source citations, outbound authority links).
7. Pitched 3 trade journalists (PM Engineer, Contracting Business, ACHR News) with a story idea — "why 87% of independent HVAC contractors don't show up in ChatGPT" — based on this audit's data.
8. Submitted profile to 5 AI agency directories that exist (the long-tail kind that the AI engines crawl for vendor lists).
What we plan to publish monthly
Starting June 2026, we will re-run the same self-audit monthly and publish the result at /r/tnovalabs. The page will show:
• Headline AI Visibility score (0-100), updated monthly
• Per-engine breakdown (ChatGPT, Claude, Perplexity, Google AI)
• Entity strength score across the 12 directories
• Citation share trend (a simple line chart, monthly)
• A short paragraph from us — what we did that month and what we expect next month
Why we think this is more honest than case studies
Three reasons. First, you can verify the underlying claim — run the same 30 queries on the same 4 engines today, count how often we appear, and compare to our published number. Second, the trajectory is the proof. A one-time case study can be cherry-picked from a hundred client engagements. A monthly self-audit on a single business cannot. Third, it forces us to keep working on our own visibility — which means the methodology has to actually work or we look bad publicly. The incentive alignment is real.
What we're hoping the trajectory looks like
Realistic expectations based on what we have measured for other contractors: 12 → 25 by Month 3, 25 → 45 by Month 6, 45 → 60 by Month 12. After Month 12 the curve flattens because the AI agency category is crowded and well-defended by established players. Anything above 60 in the first year would be a strong result.
We expect to be wrong in some specific predictions and right in the broad direction. Either way, the data will be public. Bookmark /r/tnovalabs if you want to watch.
Why is your starting score so low?
Because we are a brand-new business with no operating history, no claimed GBP, no industry presence, and no press mentions. The AI engines had almost no signal that we existed when we ran the audit. A 12 out of 100 in May 2026 is what "net new business in a competitive category" looks like.
Will you really publish the score even if it goes down?
Yes. The whole point of publishing is the trajectory, and a trajectory that occasionally regresses is more credible than a perfectly monotonic line. If the score drops we'll explain what we think happened — algorithm shift, competitive move, a tactic that didn't work — and what we're trying next.
How does this prove the methodology works for HVAC contractors specifically?
It doesn't, on its own. What it proves is that we apply the same methodology we sell to ourselves and that we are willing to be measured publicly on the result. The HVAC-specific proof will come from publishing client trajectories (with permission) once we have founding clients live, plus the published self-audits of contractors who have authorized us to share theirs.
What's the difference between your audit and a generic SEO audit?
Generic SEO audits measure organic search rank, backlinks, page speed, and on-page elements. Our audit measures AI citation share — how often AI engines name your business when homeowners ask for help — plus the entity-strength inputs (NAP, schema, directory coverage) that drive that share. Generic SEO is upstream of citation share but doesn't measure it directly. As of 2026, citation share predicts booked jobs more reliably than organic rank.
How can I see TNova Labs' current self-audit score?
It's published at /r/tnovalabs and updated monthly. The page shows the headline 0-100 score, per-engine breakdown, entity strength, and a trajectory chart. Same methodology we use for client audits — same data, same scoring weights.
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