Virtual Receptionist Services: AI Visibility Market Discovery Index

Benchmark Summary

Ruby led the Virtual Receptionist Services benchmark in October 2026 with valid recommendation coverage of 53.6%, down from 69.9% in July 2026. The gap to the next brand was 0.9 percentage points over Smith.ai at 52.7% in October 2026, down from 56.6% in July 2026. Ruby's leadership is now marginal.

No tracked brand recorded a significant coverage increase since the July 2026 baseline. Seven of ten brands recorded significant coverage declines since July. The largest three-month decline was Abby Connect at 18.0% in October 2026, down 22.9 percentage points from 40.9% in July 2026. Nexa recorded the largest single-month decline at 2.5% in October 2026, down 3.9 percentage points from 6.4% in September 2026. PATLive, Smith.ai, and Conversational were stable within normal month-to-month variation.

Since July 2026, Ruby has declined 16.3 percentage points in valid recommendation coverage, with declines in the first two months of the series followed by an increase of 3.6 points in the most recent month. Smith.ai declined 3.9 percentage points across the same series, though it moved within normal month-to-month variation.

The October 2026 benchmark comprises 317 qualified observations, up from 279 in July 2026.

Current Benchmark at a Glance

August 2026 and September 2026 sat between these two months, with 303 and 330 qualified observations respectively.

Qualified surface breadth counts the six canonical AI/search surface families with at least one qualified observation; these families are ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode.

For the strategic interpretation of this benchmark, read CiteWorks Studio's analysis of How AI Search Is Recommending Virtual Receptionist Services

Current Brand Standings

Questions This Section Answers

• Which virtual receptionist brands lead the October 2026 standings by valid recommendation coverage?
• How do Ruby and Smith.ai differ on first-position recommendations despite nearly identical coverage?
• What do presence rate and top-three rate show about each brand beyond coverage?

Sorted by valid recommendation coverage.

Ruby and Smith.ai sit within 0.9 percentage points of each other on coverage in October 2026, but Smith.ai holds a higher share of first-position recommendations at 20.5% versus Ruby's 15.8%.

How to Read the Standings

Presence rate is the share of qualified observations where a brand appears anywhere in the response. Valid recommendation coverage is the share of qualified observations where a brand appears in a valid recommendation shortlist, which is the primary benchmark metric. Top-three rate is the share of observations where a brand appears among the top three recommended options. Rank-one rate is the share of observations where a brand is recommended first. Net sentiment is the positive minus negative mention balance divided by total mentions, ranging from 0 to 1.

For formulas and denominator rules, see AI Visibility Market Discovery Metric Definitions

Recommendation Coverage Movement

Questions This Section Answers

• Which virtual receptionist brands recorded significant coverage declines since the July 2026 baseline?
• Which brand posted the largest three-month decline, and what drove it?
• Did any brand improve significantly, or did the market move mostly downward?

Seven of ten brands recorded significant three-month coverage declines, while no brand improved significantly. Abby Connect declined 22.9 percentage points from 40.9% to 18.0%, Moneypenny declined 21.1 percentage points from 29.0% to 7.9%, and AnswerConnect declined 21.8 percentage points from 63.4% to 41.6%. PATLive, Smith.ai, and Conversational moved within normal month-to-month variation.

In the most recent month, Nexa declined 3.9 percentage points to 2.5% in October 2026 from 6.4% in September 2026, marking the largest single-month move in the benchmark for October. Ruby increased 3.6 percentage points to 53.6% in October 2026 from 50.0% in September 2026, and Smith.ai increased 3.6 percentage points to 52.7% from 49.1%.

Largest Decline: Abby Connect

Abby Connect recorded the largest three-month decline in the benchmark at 22.9 percentage points, from 40.9% to 18.0% in valid recommendation coverage between July 2026 and October 2026. The brand declined in each of the three months since July 2026. The decline was supported by drops across other metrics: presence rate fell 23.3 percentage points to 21.1% in October 2026 from 44.4% in July 2026, and top-three rate fell 8.2 percentage points to 7.9% from 16.1%. Net sentiment was 0.85 in October 2026, compared with 1.00 in July 2026.

Category Leader: Ruby

Ruby remained the category leader in October 2026 with valid recommendation coverage of 53.6%, down from 69.9% in July 2026. Ruby also led presence rate at 81.4% in October 2026, down from 85.0% in July 2026. The gap to Smith.ai narrowed to 0.9 percentage points from 13.3 points in July 2026.

Recommendation Placement Snapshot

Questions This Section Answers

• Which brands convert coverage into first-position recommendations most often?
• How does Smith.ai's rank-one rate compare with Ruby's and AnswerConnect's in October 2026?

Coverage alone does not show how prominently a brand is recommended. Among the three brands with the highest coverage in October 2026, Smith.ai had the highest rank-one rate at 20.5%, ahead of Ruby at 15.8% and AnswerConnect at 11.0%.

Smith.ai and Ruby show how close coverage can still hide different first-position rates: Ruby edged Smith.ai on coverage at 53.6% versus 52.7% in October 2026, but Smith.ai held a higher share of first-position recommendations at 20.5% versus 15.8%.

Buyer-Intent Distribution

Questions This Section Answers

• Which buyer-intent classes produced qualified observations in this benchmark?
• What does the absence of pricing and multi-brand comparison questions mean for reading the data?

All qualified observations in both months fell into the brand recommendation class of discovery and consideration.

The current public series measures brand recommendation discovery only; pricing, value, and multi-brand comparison questions have no qualified signal in this benchmark. This limits what the data can say about later-stage buying decisions.

Historical Measurement Record

Questions This Section Answers

• How did qualified observations and the coverage leader change across the four monthly measurements?
• Which months recorded the largest single coverage movements, and which brand drove them?

Evidence and Source Layer

The benchmark is built from prompt-level observations that retain the query, AI/search surface, answer, brand outcome, recommendation placement, sentiment, and, where exposed, citations or attributable evidence sources. Source presence is evidence about the information environment. It is not automatically proof that the source caused the recommendation.

Scope Boundaries

This public benchmark does not measure market share, attributable sales, every possible AI response, organic-search ranking, social mention volume, or private and sponsored channels. A movement in a metric alone does not establish causality.

Top 10 Cited Domains

Questions This Section Answers

• Which domains were cited most often across AI platform responses in October 2026?
• How concentrated are citations, and what does the long tail say about the source landscape?
• Do any tracked brand domains appear among the top cited sources?

Across all AI platform responses in October 2026, 6,044 citations were observed referencing 1,898 unique domains.

Citations are distributed across a long tail, with the top 10 domains accounting for roughly 24% of all citations observed. The remaining citations are spread across approximately 1,888 additional domains, indicating that AI recommendations in this vertical draw from a broad and diverse set of sources. No tracked brand's own domain appears in the top 10. General platforms like YouTube and Google lead overall, while industry-specific review and comparison sites such as nextiva.com, receptionhq.com, and cloudtalk.io play a notable role in shaping AI recommendations.

About This Benchmark

The LLM Authority Index AI Visibility Market Discovery Index tracks how prominently brands appear in AI-generated recommendations across major AI and search surfaces. New measurements are added to the same evergreen report.

• AI Visibility Market Discovery Methodology
• AI Visibility Market Discovery Metric Definitions
• AI Visibility Market Discovery Research Standards
• Modeled AI Visibility Authority Value

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