Stop watching AI.Start shaping it.
RankEcho is an AI search visibility optimization platform for agencies delivering AI visibility reports to clients and B2B SaaS teams fixing missing AI citations on their category pages. It finds the prompts where ChatGPT, Claude, Gemini, Perplexity and Google AI ignore your brand, generates the deploy-ready fix, and re-tests the same prompts to prove the citation appeared.
Watch buyer prompts route through five engines - to you, or to a rival.
Every audit traces this network with real answers: which engines answer which buyer prompts, and whether the citation lands on your brand or a competitor.
Find - the matrix shows every red path: the exact prompts where engines route buyers to a rival.
Fix - each gap becomes a deploy-ready answer block, schema, and source plan that rewires the path.
Prove - the same prompts are re-tested on a schedule, so you see the citation appear and hold.
Start sitewide for brand intelligence. Go page-level when a URL needs to win.
Sitewide audit
Use this when you need the market-level view: which buyer prompts mention you, which prompts replace you with competitors, and which sources AI engines trust for the category.
Page-level audit
Use this when one product, pricing, comparison, solution, or guide page needs to become more citable for a specific AI-search prompt.
Show buyers the engine, not just the promise.
Sample audit: B2B SaaS analytics platform across 12 buyer prompts and 4 engine adapters. RankEcho turns the run into AI search visibility optimization dashboard figures, a prompt-by-engine matrix, a ranked fix package, and proof-loop language a buyer can understand.
Prompt x engine excerpt
Each cell shows whether the sample brand was cited, mentioned, absent, or replaced by a rival for the same buyer prompt.
Fix Engine output
Top gap: best customer analytics platform for PLG SaaS. RankEcho turns that absence into deployable work instead of leaving the team with another score.
- Answer block for the missing PLG analytics category prompt
- FAQPage + SoftwareApplication schema checklist
- Page workstream for /resources/customer-analytics-platform
- Off-site source plan for the review pages engines already cite
Proof Loop readout
After the fix ships, the same prompt can be re-tested so the report shows observed movement with confidence notes.
Before the fix, rivals owned the prompt. After, the citation appears - and stays measured.
Every fix becomes a tracked experiment: the same buyer prompt, re-run on a schedule, scored against its own pre-ship baseline. This chart is the language your stakeholder report speaks - movement, not promises.
Baseline - three pre-ship runs establish how often the prompt cited you before the change.
Re-tests - scheduled runs after shipping show whether the citation appeared and whether it holds.
Pathway - fast movement points to retrieval; slow movement points to training-cycle effects.
The missing workflow between SEO dashboards and actual AI citations.
Find the gap
See prompt-by-prompt where AI engines cite you, ignore you, or recommend a competitor.
Generate the fix
Turn each missing citation into a deploy-ready answer block, schema, source plan or content brief.
Track movement
Track the same prompt after shipping the fix and separate fast retrieval wins from slower model behavior.
Monitoring tools tell you what happened. RankEcho tells you what to ship next.
Start with the free audit. Upgrade when you are ready to fix and track.
Built for the way your team works.
See it step by step: run your first audit · ship a fix · prove it moved · run client work
Is this SEO?
It overlaps with SEO, but the target is AI answer inclusion: citations, mentions and recommendations inside generated responses.
Can you guarantee citations?
No. RankEcho uses controlled prompt tests and before/after re-tests to show movement, not fake guarantees.
What does the Fix Engine create?
Answer blocks, schema suggestions, source plans and content briefs matched to each missing-citation prompt.
Who is it for?
SaaS teams, agencies, SEO teams and founders who need to know whether AI recommends them or their competitors.