The Catalyst AI Voice Share Index

What: Multiplier applied to Google search volume to estimate total AI query volume for a category.

Why: B2B buyers increasingly start research in AI engines before or instead of Google. For enterprise buying intent queries, AI platforms collectively receive roughly 3× the query volume of Google search (1 + 2.0 = 3×). This coefficient is applied uniformly across auto-research scans and is updated as AI adoption data matures.

What: Estimated percentage of AI responses that result in a click-through to a vendor website.

Why: AI engines frequently answer questions without linking out. When they do cite a vendor, a fraction of users click through. 2.5% reflects conservative early-stage industry estimates for B2B category queries. This will increase as AI engines adopt more link-forward formats (e.g. Perplexity's citations model).

What: Estimated percentage of citation visits that become a qualified pipeline opportunity.

Why: Calibrated to B2B SaaS inbound conversion benchmarks. A buyer arriving via an AI citation is already in active research mode — higher intent than average organic traffic — but not every visit converts to a qualified opportunity.

What: Annual contract value used to translate opportunity volume into dollar pipeline.

Why: Derived in priority order: (1) pricing page — if the company publishes per-seat or per-agent prices, we multiply by a typical enterprise seat count; (2) Apollo enrichment — employee count and funding stage provide a reliable proxy when pricing is hidden; (3) category benchmarks — industry median ACV for the classified product category as a last resort.

Context-free measurement

Voice share is measured via standardized, context-free API calls — no conversation history, no account state, fresh sessions per query. This is the most reproducible and comparable baseline, but it differs from what individual users see. A buyer who has been researching your category for 20 minutes will get different results than a cold query. Our score measures baseline citation behavior, not personalized results.

Non-determinism

LLM outputs are stochastic. The same query can return different results across runs. We run each prompt twice per engine (2 runs × 10 prompts × 5 engines = 100 data points) and take the union of both runs — a company mentioned in either run receives credit. Each run uses a different buyer persona (decision-maker vs. practitioner) to further reduce variance and capture vendor mentions for different buyer roles.

Query coverage

We test 10 buyer-intent queries per scan, generated dynamically from your company's category and buyer persona. Real buyers use thousands of query variations. Our prompts are designed to represent the highest-signal buying intent queries for your category, not to be exhaustive.

Coefficient estimates

Citation CTR (2.5%) and opportunity rate (5%) are calibrated estimates, not audited figures. B2B AI citation behavior is an emerging measurement area with limited published benchmarks. We update these coefficients as more data becomes available and will publish a changelog at this URL.

Point-in-time snapshot

LLM training data, retrieval augmentation, and citation behavior all change over time. A score today reflects today's AI landscape. Companies that improve their entity authority, publish more original research, or earn more citations will see scores improve on future scans. Scores are not permanent rankings.

Pipeline figures are estimates

The monthly pipeline figures are directional estimates intended to size the opportunity, not audited revenue projections. They represent the estimated value of AI-driven buyer attention in your category — not a guarantee of recoverable pipeline. Treat them as a signal for prioritization, not a forecast.