Use case · Presence in AI answers
Be the name AI assistants recommend.
When a buyer asks an assistant who to shortlist, the answer is assembled from sources the model already trusts. We get you into those sources, then track your citation rate across five engines until presence becomes the default.
AI visibility · 7 tracked engines · 6 weeks
0→60%Share of tracked LLM answers for a pre-IPO specialty lender, from a standing start in six weeks, outranking two national incumbents on the buying prompt.
Source · RTR client reporting · pre-IPO lender, 6-week window
The problem you're living with
The answer gets given. Your name is not in it.
Ten of the last fifteen prospects described a version of this, and in several cases a board member or CEO ran the test first.
"We are simply not in the answer."
A buyer asks for the best tool in your category and three competitors come back. You find out you were left out when a rep loses a deal to a name they have never had to beat before.
"We show up once, then vanish."
AI answers reshuffle on every run. Being named occasionally is not presence, it is luck. Without citation depth behind you, the model has no reason to pick you twice.
"The consultant evaluating us uses AI."
Enterprise buyers hire advisors to run vendor selection. Those advisors often lack category expertise, so they ask an assistant, and their shortlist becomes the RFP shortlist.
Why it is still broken
The obvious fixes do not move the answer.
The instinct is to publish more and hope the models notice. More posts, a schema sweep, a visibility tool, an llms.txt file. None of them change the sources behind the answer, which is the only thing a model reads.
Fix 01 · Publish more
"More content will get us cited."
Models cite what corroborates, not what exists. New pages with no third-party evidence behind them get crawled and ignored, because the answer is assembled from sources that already agree with each other.
Fix 02 · Add schema everywhere
"We need better structured data."
Schema helps a crawler parse you. It does not make you the answer. Marking up pages nobody cites moves nothing, and the audit that recommended it never checked whether you were quotable.
Fix 03 · Buy a visibility tool
"At least we are tracking it now."
A dashboard tells you that you are absent. It does not tell you which sources the model trusted instead, which is the only actionable half of the problem.
Fix 04 · Ship llms.txt
"There must be a technical fix."
Permission files and crawler tweaks are table stakes at best. The models were never blocked from you. They had nothing to quote.
The diagnosis
Presence gets you named. Citations get you recommended.
An assistant is not ranking pages, it is assembling an answer from sources it already trusts. That means two jobs, in order. First, exist in a form the model can quote: a clear claim, a specific segment, a structure it can lift. Second, be corroborated somewhere it already reads. We start by re-running your real buying prompts across five engines and recording exactly which domains fed each answer, so the target list is evidence, not opinion.
On the reference engagement we tested 412 prompts and kept 37. Of the answers we then won, the brand was cited in 4 of 5 runs by day 90.
What we actually ship
Everything that has to be true for a model to name you.
Presence is engineered, not requested. Six workstreams, one senior team, one scoreboard.
Find the prompts that return vendors
We record what your buying committee actually types, then kill every prompt that ends in an explanation rather than a shortlist.
- Buying-committee prompt research
- Kill test on every prompt
- Competitor and category watch
- Killed list published, not buried
Moves: Share of AI answers
Pages a model can quote cleanly
Specific claims, named segments, comparable numbers, and structure that survives being lifted out of context.
- Solution-aware answer pages
- Claim-per-section structure
- Comparison tables built to be quoted
- Senior editing on every piece
Moves: Quote rate
Citation velocity in trusted sources
Models repeat sources they already trust. We go earn placements in the exact domains feeding your category's answers.
- Cited-domain teardown per prompt
- Author and editor outreach
- Review-site and roundup presence
- Monthly citation velocity reporting
Moves: Trust the models inherit
Entity and agent readiness
The plumbing that lets crawlers and agents read, trust, and reuse what you publish.
- Structured data and entity work
- llms.txt and AI crawler access
- Render and index checks
- Consistent naming across the web
Moves: Crawl and quote rate
Five engines, re-run on a schedule
One run is an anecdote. We re-run the same prompts weekly so visibility becomes a trend you can forecast.
- ChatGPT, Claude, Gemini, Perplexity, AI Overviews
- Weekly re-runs, scored
- Competitor share of answer
- Alerts when an answer flips
Moves: Visibility you can forecast
Tie citations back to pipeline
Visibility only counts if it reaches the CRM. The Two-Witness Rule runs from week one so AI-sourced demos stop landing in Direct.
- CRM and GA4 wiring
- Intake self-report capture
- Two-Witness reconciliation
- Monthly board vs. forecast
Moves: Budget you can defend
How the 90 days run
Baseline first, then presence, then depth.
Same engine as every other use case. What changes is the order of operations and what we grade first.
Week 1 · Baseline
Prompts researched and kill-tested, then run across five engines to record where you appear, where you do not, and exactly which domains fed each answer.
Weeks 2 to 12 · Build and cite
Answer pages ship in waves while outreach lands mentions in the domains already feeding your category. The two compound: pages give the model something to quote, citations give it a reason to trust it.
Always on · Re-run
The same prompts re-run weekly, scored against your competitor set, with the answer text kept so you can see how the wording changed and why.
The scoreboard
What we grade ourselves on.
Set before kickoff on your funnel data, graded monthly in writing. If the model cannot clear a 4× return, we tell you before you spend a dollar.
Counted in your CRM, not in analytics. Split by source so the channel stands on its own.
Graded monthly vs. forecast
For self-serve and sales-assist motions, tracked separately from demos so neither hides the other.
Graded monthly vs. forecast
Whether the demos are workable. Protects against a volume win that sales quietly ignores.
Reported with the raw count
Pipeline traced to a source under the Two-Witness Rule, with raw always shown next to corrected.
Reported to your board
Results · From the monthly reports
Named clients, published numbers.
Visibility is the leading indicator. Demos and pipeline are the number.
AI search became a demand channel in one quarter
Organic sessions, AI referrals, category presence and attributed demand all moved together one quarter after launch.
Four months in, running ahead of forecast
Nearly half the demo pipeline was filed as Direct. The Two-Witness Rule recovered it, and the page program kept feeding it.
AI search and organic became the growth engine
A page set that did not exist twelve months ago now carries the growth and is the most-cited name in the category’s AI answers.
Fit · Before you fill anything in
Who this works for, and who it doesn't.
We say no often, and early. Being named in the answer is a different goal than being found in a list. Reading this saves us both a call.
Great fit
- B2B SaaS, Series A to Series D$2-3M to $50M+ ARR, subscription or usage-based, sales-led or self-serve plus sales-assist.
- A category with a real competitor setVertical or category-defining: healthtech, fintech infrastructure, compliance, HR tech, legal tech, specialty lending.
- Marketing leadership in placeA CMO, VP Marketing, or Head of Growth who owns the number, usually with one under-resourced content or SEO person.
- You want to be named, not just indexedThe goal is appearing in the answer a buyer reads, with a citation, across ChatGPT, Perplexity, Gemini, and AI Overviews.
- You can ship changesModern CMS and a willingness to make technical and on-page edits.
- Pipeline is the measureBudget $5,000+ for strategy, $7,500-$15,000+ for the full engine, and demos are how we're both graded.
Also a fit: a team already ranking well in Google that shows up nowhere in the AI answers.
Not a fit
- Pre-seed or pre-PMFNo audience, no category clarity, and no runway for a compounding channel.
- B2C apps, DTC ecommerce, or local servicesDifferent playbook, different channels. Not our specialty and not our pricing model.
- Enterprise consultingThose cycles are relationship-driven, not search-driven.
- You want content volume without strategyWrong expectation. We decline these even when the budget is there.
- You can't make on-page or technical changesThe methodology doesn't work without them.
- Impressions are the KPIIf a visibility score with no pipeline attached is the win condition, we are the wrong agency.
- Not willing to let us driveIf every decision needs a committee and a month, the program stalls. You approve the strategy, we run it.
- Under $5K a monthNot enough investment to make the channel compound. You'd be paying for activity, not an outcome.
If that's you, we'll say so on the first call and point you somewhere better. No sequence, no follow-up.
In their words
The people who signed off on it.
"Justin and his team have been phenomenal from Day 0, and the consistency in quality and the measurable results they have delivered have been a true transformation to our business. We are extremely happy and we have already recommended them to our friends."
"The partnership with Rock The Rankings contributed to clear improvements in organic performance across Canada and Australia, particularly for MoonPay's high-value transactional keywords and pages."
"Rock The Rankings delivered great work for us. We came in with a massive index bloat issue, and Justin and team quickly figured out what needed to be done and helped us implement his suggestions on the site."
"Rock The Rankings have really gone above and beyond, in terms of exceeding our expectations in both their communications and their strategies around marketing. They're very specific at what they do. They've been able to help us get to positions 1 and 2 for our most valuable keywords."
"They simply get SEO: which levers to pull and when. During my search for the right partner I already had a pretty clear idea about what was needed, and they shared a very similar vision."
"The workflow was seamless between our teams. Overall, their process was very close to ours, so it was a great fit from the get-go. Most importantly, we learned a ton from their approach."
"Justin and his team helped us strengthen our SEO strategy, enabling us to adopt a more in-depth and strategic approach, and to better anticipate and organize our content operations."
"We set out to get valuable backlinks and they have just been a fantastic partner in making that happen. It's been an amazing experience working with Rock The Rankings, and I highly recommend that you try them out."
Straight answers
Asked on every call.
If yours isn't here, bring it to the call. Same unpolished answer either way.
ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews as standard, with Grok and Copilot added on request. The same prompt set runs across all of them on a schedule, and we keep the answer text so you can see how wording shifts, not just whether you appeared.
Constantly. That is the point of repeated runs. A single screenshot proves nothing, which is why we score citation rate across runs: cited in 4 of 5 is a real position, cited once is noise.
They help at the margin, and they are cheap, so we do them. They are not the lever. Across twelve months and twenty-plus programs, the two things that moved visibility were solution-aware pages a model can quote and mentions in sources it already trusts. Everything else is secondary until those are in place.
Citations move in weeks. On the reference engagement the first breakthrough was five days and sustained majority visibility took six weeks. Categories with entrenched incumbents take longer, and we will say so in the first two weeks rather than at month three.
If your category genuinely has no competitor set, or your buyers do not research before they buy, this will underperform. It is also wrong if you need leads next week: presence compounds, and the compounding is the whole return.
Next step
Your GEO plan, built on your numbers.
Give us your domain and your pipeline goal. We map the prompts your buyers are asking, build your GEO marketing plan against them, and forecast the pipeline the next 90 days can realistically produce.
What we walk through