Call Answering Services: AI Visibility Market Discovery Index

Benchmark Summary

Questions This Section Answers

• Which call answering services led the category in October 2026, and how far apart were they?
• Did any brand move beyond normal month-to-month variation this period?

Ruby led the Call Answering Services category in October 2026 with valid recommendation coverage of 56.6%, followed by Smith.ai at 49.8% and AnswerConnect at 49.5%. The gap between Ruby and Smith.ai was 6.8 percentage points, and the gap between Smith.ai and AnswerConnect was 0.3 points. Valid recommendation coverage is the share of qualified observations where a brand appears in a recommendation shortlist.

No tracked brand moved beyond normal month-to-month variation between September 2026 and October 2026. The largest single-month changes were Ruby, up 5.8 points from 50.8% in September 2026 to 56.6% in October 2026, and Smith.ai, up 5.9 points from 43.9% to 49.8% over the same two months. Neither move exceeded the threshold for this category.

Measured against the July 2026 baseline, the category's largest cumulative increase was PATLive, whose valid recommendation coverage rose from 13.5% in July 2026 to 14.8% in October 2026, up 1.3 points, within normal variation. The largest cumulative decline was Abby Connect, down 27.0 points from 43.8% to 16.8%. Seven brands recorded cumulative declines beyond normal variation: Abby Connect, AnswerConnect, Davinci Virtual, Moneypenny, Ruby, Specialty Answering Service (SAS), and VoiceNation. Ruby declined 16.1 points from its July baseline but has moved upward for two consecutive months. AnswerConnect fell 22.4 points and has also posted two consecutive monthly gains. VoiceNation declined in each of the three months since July 2026, from 22.1% to 9.1%.

The benchmark began with 800 prompt-surface observations in July 2026 and produced 267 after qualification, while October 2026 began with 800 prompt-surface observations and produced 297 after qualification.

Current Benchmark at a Glance

Qualified surface breadth counts the canonical AI/search surface families with at least one qualified observation: ChatGPT, Copilot, Gemini, Perplexity, AI Overviews, and AI Mode. All six families were represented in both months. August 2026 recorded 392 qualified observations and September 2026 recorded 319, between the two comparison months.

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

Research Scope and Qualification

Questions This Section Answers

• How many prompts were collected versus how many qualified for the public benchmark?
• Why is the qualified observation count lower than the raw collection?

The public benchmark is narrower than the raw collection universe by design.

Brand-level percentages use the qualified observations as the public denominator, not the raw collection. See the AI Visibility Market Discovery Methodology for full details.

Current Brand Standings

Questions This Section Answers

• Which brands ranked highest on valid recommendation coverage in October 2026?
• How did top-three and rank-one rates differ among the leading call answering services?

How to Read the Standings

• Presence rate: share of qualified observations where the brand is mentioned at all.
• Valid recommendation coverage: share of qualified observations where the brand appears in a recommendation shortlist.
• Top-three rate: share of qualified observations where the brand appears in the first three recommendation positions.
• Rank-one rate: share of qualified observations where the brand is the first recommendation.
• Net sentiment: positive minus negative mentions divided by total brand mentions.

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

Recommendation Coverage Movement

Questions This Section Answers

• Which call answering services declined the most since the July 2026 baseline?
• Which brand recorded the largest cumulative increase in recommendation coverage?
• How did the category leader's coverage change between July and October 2026?

Largest Increase: PATLive

PATLive recorded the category's largest cumulative increase, rising from 13.5% valid recommendation coverage in July 2026 to 14.8% in October 2026, up 1.3 points, within normal month-to-month variation. PATLive also moved upward for two consecutive months, from 14.1% in September 2026 to 14.8% in October 2026, a 0.7 point move. The brand recorded 44 valid recommendations out of 297 qualified observations in October 2026 and a presence rate of 21.2%.

Largest Decline: Abby Connect

Abby Connect recorded the category's largest cumulative decline, down 27.0 points to 16.8%. The brand declined in each of the three months since July 2026. Abby Connect recorded 50 valid recommendations out of 297 qualified observations in October 2026 and a presence rate of 20.5%, down from 47.2% in July 2026.

Category Leader: Ruby

Ruby led the category in October 2026 with valid recommendation coverage of 56.6%, down from 72.7% in July 2026. A stable category lead still came with a 16.1-point decline from baseline. The brand recorded 168 valid recommendations out of 297 qualified observations and a presence rate of 84.9%. Ruby has moved upward for two consecutive months, from 50.8% in September 2026 to 56.6% in October 2026, a 5.8 point move, while remaining below its July baseline.

Recommendation Placement Snapshot

Questions This Section Answers

• How did top-three and rank-one rates differ between Ruby, Smith.ai, and AnswerConnect?
• Can two call answering services have similar coverage but different first-position rates?

Coverage alone does not show how prominently a brand is recommended. Among the three leading brands, placement rates remained close while rank-one rates differed.

Ruby and Smith.ai had similar coverage in October 2026, but Smith.ai's rank-one rate of 18.9% exceeded Ruby's 15.2%, and Ruby's top-three rate of 45.1% exceeded Smith.ai's 39.7%. Close coverage can still hide different first-position rates.

Buyer-Intent Distribution

All qualified observations in both months fell into the brand recommendation class, which captures discovery and consideration queries.

The current public series cannot answer pricing, value, or head-to-head comparison questions. It measures brand recommendation discovery only.

Historical Measurement Record

Questions This Section Answers

• Which call answering service has led the benchmark in each measurement period?
• What is the largest single-measurement coverage movement recorded so far?

This is an evergreen benchmark URL. New measurements are added to the same report.

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, not proof that the source caused the recommendation.

Scope Boundaries

This public benchmark does not establish causality, market share, or attributable sales from a metric movement alone. It does not measure every possible AI response, organic-search ranking, social mention volume, or private and sponsored channels. Movements in tracked metrics document change in the AI discovery environment; they do not, by themselves, establish why a change occurred.

Top 10 Cited Domains

Questions This Section Answers

• Which domains were cited most often across AI platform responses in October 2026?
• How concentrated were citations among the top domains?
• Which cited domains were specific to the call answering and business communications industry?

Across all AI platform responses in October 2026, 5,968 citations were observed across 1,784 unique domains.

Citations are broadly distributed rather than concentrated: the top 10 domains together account for 1,604 citations, about 26.9% of the 5,968 total, across 1,784 unique domains. No domain owned by a tracked brand appeared in the top 10 in October 2026. The list is led by large general-purpose platforms (google.com, youtube.com, reddit.com, en.wikipedia.org) alongside a group of industry-specific sources, including call answering and business communications sites such as nextiva.com, receptionhq.com, getnextphone.com, getaira.io, and cloudtalk.io.

About This Benchmark

The LLM Authority Index AI Visibility Market Discovery Index is a neutral, industry-level benchmark tracking how AI and search surfaces present brands during buyer discovery. It is built from audited, prompt-level observations across six canonical AI surface families and reports presence, recommendation coverage, placement, and sentiment for each tracked brand.

Research links:

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

Get a Company-Level Authority Index

The public industry benchmark shows category-level standings. A company-level Authority Index can go deeper.

The public percentage cannot identify the prompts, competitors, or sources causing the result.

Get my free AI Company Index