GEO 2.0 is our methodology and capability approach for GEO within the VGO. In generative AI contexts, it connects customer decision scenarios, verified brand facts and traceable evidence with diagnosis, prioritized action, execution, ongoing verification and maintenance. Its goal is to help brands be accurately understood, credibly cited, fairly compared and appropriately recommended in relevant contexts.
VGO is the overarching open methodology, covering SEO, AEO, GEO, SMO and emerging discovery surfaces such as agents. GEO 2.0 names the GEO approach within VGO; it does not denote the whole framework or change VGO’s definition or hierarchy. Omseek is a product implementation based on VGO.
GEO is the general term for Generative Engine Optimization. Here, GEO 2.0 refers specifically to our approach within VGO, rather than an industry-wide version standard. Other GEO practices may also address facts, evidence and long-term outcomes. Effective GEO describes the intended outcome; GEO 2.0 describes the method for pursuing and maintaining it. The name is not a VGO version number.
Record mentions, factual accuracy, whether cited evidence supports the claim, and recommendation relevance separately. None alone proves selection or conversion.
Outputs and evidence:Full answers, verified claims, citation excerpts and scenario-specific conclusions.
Define audience, need, market, language and decision stage; use question variants and repeated comparable sampling.
Outputs and evidence:Versioned scenario and question sets, sample counts, failures and coverage limits.
Connect approved facts to traceable evidence and maintainable owned assets. Publishing remains one possible action; independent corroboration remains valuable.
Outputs and evidence:Fact and evidence inventory, source versions, owners and review conditions.
Connect each finding to a rationale, owner, authorized action, change record and comparable retest.
Outputs and evidence:Prioritized plan, execution record, verification window and contrary evidence.
Review stale facts, perception drift and evidence gaps; carry learning into the next cycle.
Outputs and evidence:Review schedule, unresolved issues, asset history and next actions.
Example: an AI answer to a procurement question cites an obsolete product capability. Record the question, full answer, citations and time; compare with current verified facts. Assign an owner to update owned sources and request external corrections where appropriate. Retest under comparable conditions, retain failures and contrary evidence, and schedule the next fact review. This is a fictional method example, not a customer result or causal proof.
Record mention, citation, recommendation, selection, referral and conversion separately. Before/after change alone is not causal proof; there is no universal GEO 2.0 score.
This page describes the method and delivery approach, not a claim that every step is automated. Availability depends on release, entitlement, integrations and deployment. Full touchpoint attribution and experimental uplift computation are outside the scope promised here; additional integrations follow their own release schedules.
An existing website can be connected without first migrating to Omseek Site. Execution follows authorization and review requirements.
VGO is the overarching open methodology, covering SEO, AEO, GEO, SMO and emerging discovery surfaces such as agents. GEO 2.0 names the GEO approach within VGO; it does not denote the whole framework or change VGO’s definition or hierarchy. Omseek is a product implementation based on VGO.
No. Relevant recommendation is an intended outcome; third-party system behavior requires ongoing observation and verification.
Read the open methodology ↗