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.
See the method applied