Laboratory · 03

Conversational Search Laboratory.

The Conversational Search Laboratory is our research practice for Answer Engine Optimization (AEO): the question-led content structures, FAQ schema, passage design, and entity scoping that allow AI assistants and voice interfaces to extract a clean, attributable answer. It tests how Google AI Overviews, ChatGPT, Perplexity, Alexa, and Siri parse, summarize, and cite branded answers across informational and local queries.

Focus areas

What this laboratory investigates

Question taxonomies for category and brand queries.
Atomic answer block design for retrieval.
FAQ schema and structured Q&A architecture.
Voice and assistant-surface answer behavior.
Local service answer extraction.
Cross-engine consistency of cited answers.

Methodology

How the laboratory operates

01

Question mining

Build representative prompt sets across category, comparison, brand, support, and local intents.

02

Answer engineering

Design atomic answer blocks and FAQ structures that match the way assistants extract content.

03

Cross-engine probing

Probe ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude with the same prompt sets to compare cited sources.

Active experiments

What we are currently testing

Answer block length vs extraction probability

Measuring the effect of answer length and structure on extraction into AI assistant responses.

FAQ depth and recommendation behavior

Studying how depth of FAQ coverage influences whether assistants recommend the brand as an authoritative source.

Local service answer extraction

Examining how local services optimize structured answers for assistant-driven local queries.

Applied to

Professional ServicesSaaS CompaniesLocal ServicesHealthcareFinance

FAQ

Frequently asked questions

Apply this laboratory's research to your brand.

Start with a diagnostic Organic Visibility Audit to see how machines currently perceive, understand, and retrieve your business.

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