01
Baseline
- Input
- Public website, docs and project facts
- Action
- Capture current answers, sources and technical readiness
- Output
- A dated starting record
- Measurement
- Presence, accuracy and source coverage
molthub methodology
A repeatable process for Web3 AI-search visibility.
GEO is treated as evidence, content and source engineering—not as control over an AI platform's answer.
Direct answer
How does molthub approach GEO?
molthub starts with a dated baseline, tests a bounded set of buyer questions, verifies Web3 facts, maps useful sources, improves the highest-priority evidence, and then repeats the same observations.
The method optimizes controllable inputs. It does not promise a permanent AI ranking, citation or recommendation.
02
Query research
- Input
- Product category, audience and buyer journey
- Action
- Build a focused set of discovery, problem and comparison questions
- Output
- A repeatable query set
- Measurement
- Intent coverage and commercial relevance
03
Source analysis
- Input
- AI answers and cited pages
- Action
- Map which sources support competitors and which evidence is missing
- Output
- A citation-gap map
- Measurement
- Source type, recency and factual support
04
Entity verification
- Input
- Homepage, docs, GitHub and public profiles
- Action
- Align product, protocol, token, network and custody facts
- Output
- A maintained fact spine
- Measurement
- Consistency and verifiability
05
Evidence design
- Input
- Verified facts and buyer questions
- Action
- Create or improve pages that answer a real question directly
- Output
- Useful evidence assets
- Measurement
- Clarity, originality and extractability
06
Implementation
- Input
- Approved priorities
- Action
- Improve copy, structure, metadata, schema and internal links
- Output
- Deployed website changes
- Measurement
- Completion against agreed scope
07
Retest
- Input
- The original query set
- Action
- Repeat the same observations after implementation
- Output
- A before-and-after record
- Measurement
- Direction of change, not a guaranteed ranking
08
Iteration
- Input
- Results, product changes and new questions
- Action
- Maintain facts and prioritize the next evidence gap
- Output
- A practical follow-up plan
- Measurement
- Accuracy and useful query coverage over time
Measurement limits
What the method cannot prove on its own.
- AI answers can vary by platform, model, date, location and conversation context.
- A citation does not prove that every sentence in an answer came from that source.
- A visibility change after implementation does not establish causation by itself.
- Website improvements cannot replace legitimate third-party authority or real product adoption.
Public references
Methodological foundations worth inspecting.
Google Search CentralHelpful, reliable, people-first contentGoogle Search CentralOptimization guidance for generative AI featuresPrinceton, Georgia Tech, Allen Institute & IIT DelhiGEO: Generative Engine OptimizationYao JingangGEO as probabilistic content and source engineering
These references inform the operating principles. molthub does not claim affiliation with their authors or organizations.
Query coverage
Web3 evidence pages built around distinct buyer intents.
These pages answer commercial, sector, technical, pricing and measurement questions without turning forty query variants into forty pages.
GEO services for Web3 startupsCommercial service selectionGEO for stablecoin payment infrastructureStablecoin sector evidenceGEO audit for crypto payment APIsTechnical purchase intentAffordable GEO for early-stage Web3 teamsPricing and scope selectionMeasure AI-search visibility for Web3Repeatable measurement
See the method applied