Scenario and query baseline
Freeze monitoring queries and engine scope with the brand; establish visibility and representation baselines — no “feel-based” optimization.
Be seen and understood correctly in generative answers
Brand presence in major LLMs and answer engines was inconsistent or missing. GEO (Metis AI·Thousand Eyes) built a measurable visibility and representation loop — explicitly a Marketing product path.
Challenge
Invisible, uncontrolled, unmeasured on the answer side
More category and brand discovery happens inside conversational AI. The brand’s appearance was unstable, key facts were misstated, and there was no durable monitoring/optimization loop — classic SEO reports don’t cover generative scenarios.
Approach
Probe baseline → knowledge & signals → monitor & iterate
Knowledge structure and signal work
Curate authoritative facts and scenario content; advance semantic alignment and signals under GEO methods; publish steps need client confirmation.
Monitor and re-probe
Use Console probes and re-tests to compare before/after and keep an explainable iteration backlog.
Outcome
Re-probeable generative visibility capability
- A confirmed monitoring query set and engine coverage.
- Optimization actions reviewable against probe results; methods explainable — no black-box content ops.
- GEO positioned as a Marketing solution: combinable with consulting, not a substitute for ops or data programs.
Capabilities
Marketing · GEOBrand knowledge structureAnswer-engine monitoring
Book Discovery
Share industry and goals — we arrange a reference conversation against similar cases.
Client names are anonymized (e.g. “a national retail group”). Representative cases from company materials are client-authorized and desensitized. We do not invent Fortune 500 logos or unauthorized ROI percentages; outcomes are qualitative results and deliverable forms you can discuss in Discovery.