molthub is a hands-on Web3 GEO studio for early-stage teams. We audit and improve the public information that helps AI-powered search systems discover, understand, and cite a project.
Discover → Understand → CiteFree automated scan
Enter a public website. See every automated check, buyer-question ideas and evidence-based next steps immediately. Copy or print the report, then request a focused human review if useful.
Full basic report · No signup · No card
No account or email required. All basic findings are shown here; this report is not saved to an account or published as a public link.
Start small, upgrade when useful
Automated tools for a fast first answer. Human verification and implementation when the project matters more.
See the evidence first. Confirm the deliverable, timing and price before paying.
A fast website-readiness check for Web3 teams starting with GEO.
Best for: finding obvious gaps before spending.
A focused human review of one page and one buying question. Agree the scope and delivery time before payment.
Best for: teams that want a small, clearly scoped first engagement.
What the 9.99 USDT review looks like
A public-source RateUp study from 24 October 2026 shows the format. This is independent research, not a client endorsement or an implemented result.
The reviewed pricing table said “Unlimited orders”; the Free-plan FAQ described a 50-order monthly allowance.
Open the evidence image at full size. The live source may have changed.
The public page did not establish which limit was enforced. A merchant deciding whether to configure the shop needs one consistent answer. No lost orders or incorrect AI answers were measured.
03 · Draft, not liveIf capped: Free includes up to 50 shop orders per calendar month. Upgrade for a higher order allowance.
If unlimited: Shop orders are unlimited on Free; active-contact and campaign-credit limits still apply.
Acceptance: Confirm the actual rule, then align the table, FAQ and in-product explanation. Neither draft is a verified product policy until the owner confirms it.
Your deliverable: one-page review, source evidence for up to three findings, and one prioritized correction draft. No artificial minimum number of problems. Implementation, product-account testing and AI-platform tests are outside this scope.
Read the complete study →No subscription required. Each review has an agreed scope; implementation and AI-platform tests are quoted separately where needed.
The visibility problem
Your project is missing from relevant AI-generated recommendations.
AI may confuse your product, protocol, token, supported networks, or custody model.
Your website may contain claims, but not enough clear and verifiable information for AI systems to cite confidently.
What we actually do
We do not stop at vague recommendations.
See how AI describes your project and which competitors appear first.
Check Web3 facts, competitor positioning, and the sources shaping answers.
Improve priority pages, then retest the same high-value questions.
Evidence-led deliverables
Evidence, context, priority, action.
Where your project appears—and where it does not.
Who gets recommended first and why.
What AI misunderstands or cannot verify.
What to improve first.
Case studies & research
Six practical studies in source clarity, answer accuracy, buyer discovery and measurement.
A merchant comparing a free shop plan encountered conflicting order limits on the same pricing page. We documented the conflict and drafted two corrections for the product owner to choose between.
The pricing page showed both Engage and Service OS, while the reviewed software-offer data represented Engage only. We mapped a minimal way to bring the product explanation and offer data into agreement.
An economic-audit service and its structured offer pointed to different audit scopes. We documented the exact mismatch and proposed a small correction that preserves the rest of the service data.
A delivery-policy review found conflicting AI answers to a purchase-critical question. The useful next step was to clarify the service facts before writing more content.
The website described the requested appointment workflow, but two captured AI answers named other tools. We turned that small observation into a focused product-page verification plan.
Our own site became the test case: record the baseline, keep access failures visible and repair the scanner so that an unreadable robots policy cannot count as a pass.
Three public website analyses, two anonymized diagnostics and our own website audit. Each case states what was observed, what changed and what still needs testing.
A hands-on studio
Speak directly with the person doing the work.
Product and technical claims are checked manually.
We can apply improvements, not just report them.
Start with one review—no long contract.
Evidence-led editorial library
Beginner guides explain the fundamentals. Professional notes document measurement, source and implementation decisions.
Our public pages returned HTTP 403 to audit requests even though our source policy permits search crawling. Here is what that observation proves, and what it leaves unknown.
A plain-English introduction to how AI search discovers, classifies, and explains Web3 products.
Why the same project must be described consistently across its homepage, docs, GitHub, ecosystem listings, and social profiles.
Three practical reasons a smaller Web3 product can disappear from an answer while a better-documented competitor gets the mention.
Beyond Web3
Molthub also supports selected regulated and ad-restricted brands with compliant organic and AI-search visibility. Every higher-risk engagement is reviewed before scope or payment.
About molthub
Clearer for people. Easier for AI to understand.
Questions before you start
The work is designed to reduce uncertainty, not replace it with guarantees.
We do not guarantee rankings, citations, or recommendations from any AI platform.
Generative Engine Optimization improves how clearly AI search systems can discover, understand, and cite information about a project. It complements traditional SEO but focuses more directly on answer quality, factual clarity, source strength, and citation readiness.
All automated website-readiness findings, three buyer-question ideas and actions only for flagged signals are available without an account or email. You can copy or print the report. It does not test live AI answers. An optional 9.99 USDT human review covers one page and one buying question; scope and timing are confirmed before payment.
No. We do not guarantee rankings, citations, recommendations, or mentions from any AI platform. We improve the clarity, evidence, and structure that can help AI systems understand a project more accurately.
No. A review can lead into practical website, documentation, or content improvements when implementation support is useful and agreed in scope.
No. One-off reviews and short implementation sprints are welcome. Longer support can be discussed only when it is genuinely useful.
Yes. USDT is accepted for agreed paid engagements. Payment details are confirmed privately before work begins.
The studio is designed for early-stage and growing stablecoin payment products, crypto payment infrastructure, wallets, developer tools, on-chain data products, Web3 SaaS, and infrastructure projects.
Yes. Client visibility and case-study permissions are agreed separately. An audit can remain private or be presented without identifying the client.
Timing depends on the size of the website, documentation, prompt set, and implementation scope. A realistic schedule is confirmed after the initial review rather than promised before the project is understood.
Choose your next step
Send your website and the question you want answered. We confirm the deliverable, timing and fixed price before you pay.
Direct communication · Clear scope · No required long-term contract