Measurement with the limits attached
Know what AI answers are doing with your brand

Some of the 500+ brands we've worked with
See all referencesSix signals, six questions
Decide what changed before deciding what to do


Brand Mention & Share-of-Model Tracking


Citation & Source Tracking


Answer Accuracy & Claim Monitoring


Sentiment & Recommendation Analysis


Competitor Benchmarking & Regression Alerts


AI Referral & Assisted Conversion Measurement
Evidence before escalation
Noise does not become a task by default
The record shows what appeared, how often it appeared in the sampled panel, and which evidence sits behind the label. We compare the movement with a threshold agreed before collection. If it does not clear that threshold, it stays in the record without becoming an action item.
Where findings go next
The person who can change the cause receives the finding
Strategy defines the question universe. Technical, content, entity, and authority teams improve the conditions within their control. Monitoring reruns the panel, reports uncertainty and variance, then routes a supported finding to the person responsible for the relevant change.
Scope and ownership
Rules before dashboards, and a threshold agreed before collection
Noise does not become a task by default; a movement has to clear the agreed threshold to earn an owner.
Questions, platforms, markets, event definitions, denominators, validity rules, and known blind spots are fixed before the baseline is collected, and every repeated sample keeps its full answer context — failures included — on the same timeline as platform and interface changes. A specialist resolves unclear labels against the exact claims and sources, and mentions, citations, referrals, and outcomes keep their own separate measures rather than collapsing into one score. Strategy defines the question universe; technical, content, entity, and authority teams improve the conditions within their control; monitoring reruns the panel after an approved change and routes a supported finding to the person who can act on it.
Measurement needs rules before it needs a dashboard
Write down the panel rules
Questions, platforms, markets, event definitions, denominators, validity rules, and known blind spots are fixed before the baseline is collected.
Keep failed runs in the record
Every repeated sample retains its full answer context, including failures. Platform and interface changes stay on the same timeline.
A specialist resolves the grey areas
Unclear labels are checked against the exact claims and sources. Mentions, citations, referrals, and outcomes continue to use their own measures.
Escalate, assign, rerun
A movement goes to an owner only after it clears the agreed threshold. The panel runs again after any approved change is made.
Clients on the work
What it is like to work with Zeo
Our clients describe the work in their own words.
Adjacent search evidence
Case Studies
Organic search engagements establishing the indexation, content depth, and domain authority that AI answer engines draw from.
People who watch how AI cites a brand
GEO work starts with recording what AI answers actually say about a brand today, then moves to the parts you can influence. The consultants below work on the specific capability this page covers.

Samet Özsüleyman
SEO Manager

Hande Parmaksız
SEO Manager

Ezgi Gülsen Yaylı
SEO Manager

Sena Önder
Senior SEO Executive

Elif Naz Akan Karakoç
Senior SEO Executive

Sinem Bakır Yavaş
Senior SEO Executive

Zafer Yıldız
Web Analytics Manager

Aybüke Göktuna
Senior SEO Analyst

Ruhan Tiryaki
Senior SEO Analyst

Burak Pehlivan
Co-founder & CEO

Mehmet Aktuğ
Co-Founder & COO

Metehan Urhan
New Business & Partnership Manager

Ataberk Yüzat
SEO Executive
Tools we use
What we watch AI answers with
Generative search leaves less to read than a rankings report does, so most of this work is assembling evidence from tools that were never built for it.
AI answer and citation tracking
- Otterly.AIThis page's own opening point, that one visibility score cannot say whether an answer mentioned, recommended, cited, or converted, is exactly what Otterly.AI's event-level tracking is built to preserve; its per-engine, per-event breakdown is what feeds the separate measurement described in the page's four child methods.
- ProfoundThe measurement panel this page's roadmap runs on, and the sample size, denominator, and known blind-spot disclosure it insists accompany every result, is built from Profound's run-condition logging, the record that lets a specialist say what a given result can and cannot support before anyone acts on it.
- Google AnalyticsThe conversion half of this page's measurement, distinguishing an AI-referred visit from an assisted or unattributed one, runs against GA4's own consented event data, since a referrer log alone cannot say whether that visit went on to convert.
- SimilarwebWhere retained referrer strings alone are ambiguous about which platform actually sent the visit, Similarweb's AI-channel segmentation gives the escalation step a second, cross-checkable read before a movement is labeled a genuine competitor gain rather than referrer noise.
Content evidence and sourcing
- AirtableThis page's own second section, keep failed runs in the record, is a discipline Airtable's structured rows enforce directly: a failed or ambiguous prompt run is logged with a status rather than silently excluded, which is what lets a specialist later distinguish a real regression from a bad sample.
Set the decision first
Build a measurement your team can use


Interpreting a changing sample
































