Call Answering Services AI Visibility Ranking Trends - November 2026

Answer capsule

The November 2026 Call Answering Services benchmark compares 10 companies across 288 AI answers that met its inclusion rules. Ruby leads with 114 recommendations (39.58% of answers). At least one tracked company was recommended in 144 answers; some answers recommended several companies.

The rankings distinguish being mentioned from receiving an affirmative recommendation, then examine placement and the wording used about each company. Earlier monthly results show how counts and rates changed, with each month using its own answer total. These results describe the answers reviewed, not product quality or customer satisfaction. Differences between monthly samples limit comparisons and do not establish why a result changed.

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

Industry leaderboard

Questions This Section Answers

• How do Call Answering Services rank by recorded AI recommendations in November 2026?

The leaderboard orders companies by how many recorded answers affirmatively recommended them. That count answers a narrower question than reach: a company can appear often in answers without being endorsed in most of them. Ties should be read as equal recommendation counts, not as shared or split credit.

Mentions, recommendations, shortlist placements and first-place placements measure different things. Placements are nested inside recommendations, so they should not be added to recommendation totals. Recommendation rates use the full answer total as their base, which keeps companies comparable even when their mention volumes differ.

Key findings

Tracked-company answer coverage

Questions This Section Answers

• How often did the November 2026 Call Answering Services answer sample recommend any tracked company?

144 distinct answers recommended at least one tracked company. Counting each company recommendation separately gives 458, because an answer can recommend several companies. Answers with no tracked recommendation may still have recommended companies outside this comparison.

Every qualified answer mentioned at least one tracked company, but only half recommended one. That gap is the central distinction in this section: appearing in an answer is not the same as being put forward as a choice.

Multiple tracked companies were recommended in many answers, so company-level totals exceed the number of answers carrying a recommendation. Overlap is expected and should not be read as market dominance. Answers without a tracked recommendation may still have named companies outside this comparison.

Reach and affirmative recommendation

Ruby ranks 1 by recommendation count. Mentions: 244 (84.72%); recommendations: 114 (39.58%). Top 3 placements: 97 (33.68%); first-place placements: 48 (16.67%).

Wording about the company: 121 positive, 123 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 46.7%. The net framing score was 0.50 (defined in Methodology).

130 mentions had no recorded recommendation. Of recorded recommendations, 85.09% were Top 3 and 42.11% were first-place.

Ruby leads on recommendation count, but its mentions outnumber its recommendations by a wide margin. Most of its appearances carried no recorded endorsement, so reach and affirmative recommendation answer different questions here.

A recommendation-to-mention ratio describes the recorded answers only. It is not a conversion rate, a customer measure or evidence of preference. Where a ratio rests on a small base, it should be read cautiously rather than as a strong signal.

Recommendation order and placement

Placement describes where a recommended company appeared in an answer's ordering. Shortlist placements sit inside recommendations, and first-place placements sit inside shortlist placements, so these are nested subsets rather than separate wins.

A company can be recommended often while rarely appearing first. Placement shares use the same answer denominator as recommendation rates, which keeps the nesting consistent. They do not measure consumer choice, purchase behavior or product performance.

Classified framing in context

Framing records how the selected answers worded their references: positive, neutral or negative. It is a property of the answer text, not a measure of customer sentiment, reputation or company quality.

Neutral wording is common and does not imply weak endorsement; positive wording does not establish why a recommendation occurred. The net framing score summarizes the balance of positive and negative wording across mentions. It should be read alongside recommendation counts, not as a substitute for them.

Company results in detail

Ruby

Questions This Section Answers

• What do the November 2026 AI answer results show about Ruby mentions, recommendations and placement?

Ruby ranks 1 by recommendation count. Mentions: 244 (84.72%); recommendations: 114 (39.58%). Top 3 placements: 97 (33.68%); first-place placements: 48 (16.67%).

Wording about the company: 121 positive, 123 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 46.7%. The net framing score was 0.50 (defined in Methodology).

130 mentions had no recorded recommendation. Of recorded recommendations, 85.09% were Top 3 and 42.11% were first-place.

From October 2026 to November 2026, Ruby's mention count fell from 252 to 244, while its recommendation count fell from 168 to 114. The recommendation rate was 56.57% and 39.58% respectively.

Ruby holds the top recommendation count and the broadest mention reach in this sample. Its recommendation-to-mention ratio is moderate: many appearances did not carry an endorsement, so reach and recommendation diverge noticeably.

Its wording is split almost evenly between positive and neutral, with no negative mentions recorded. Placements are concentrated within its recommendations, with first-place results forming a smaller subset. The October-to-November comparison shows both counts lower, but the samples differ and the change should not be attributed to a cause.

Smith.ai

Questions This Section Answers

• What do the November 2026 AI answer results show about Smith.ai mentions, recommendations and placement?

Smith.ai ranks 2 by recommendation count. Mentions: 260 (90.28%); recommendations: 99 (34.38%). Top 3 placements: 79 (27.43%); first-place placements: 39 (13.54%).

Wording about the company: 103 positive, 157 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 38.1%. The net framing score was 0.40 (defined in Methodology).

161 mentions had no recorded recommendation. Of recorded recommendations, 79.80% were Top 3 and 39.39% were first-place.

From October 2026 to November 2026, Smith.ai's mention count rose from 225 to 260, while its recommendation count fell from 148 to 99. The recommendation rate was 49.83% and 34.38% respectively.

Smith.ai records the highest mention count in the sample while ranking second on recommendations. Its recommendation-to-mention ratio is comparatively low, meaning most appearances did not include an affirmative endorsement.

Neutral wording outweighs positive wording in its mentions, and no negative mentions were recorded. First-place placements form a modest share of its recommendations. The month-to-month comparison shows mentions higher and recommendations lower; because the samples differ, that pattern is a recorded change, not an explanation.

AnswerConnect

Questions This Section Answers

• What do the November 2026 AI answer results show about AnswerConnect mentions, recommendations and placement?

AnswerConnect ranks 3 by recommendation count. Mentions: 159 (55.21%); recommendations: 90 (31.25%). Top 3 placements: 65 (22.57%); first-place placements: 22 (7.64%).

Wording about the company: 91 positive, 68 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 56.6%. The net framing score was 0.57 (defined in Methodology).

69 mentions had no recorded recommendation. Of recorded recommendations, 72.22% were Top 3 and 24.44% were first-place.

From October 2026 to November 2026, AnswerConnect's mention count fell from 178 to 159, while its recommendation count fell from 147 to 90. The recommendation rate was 49.49% and 31.25% respectively.

AnswerConnect combines a mid-level mention count with a comparatively high recommendation-to-mention ratio, so a larger share of its appearances carried an endorsement than for most peers. It ranks third on recommendations.

Positive wording outnumbers neutral wording in its mentions, and no negative mentions were recorded. First-place placements are a smaller subset of its recommendations. Both mentions and recommendations were lower than in the prior month, but the differing samples mean the comparison does not establish why.

PATLive

Questions This Section Answers

• What do the November 2026 AI answer results show about PATLive mentions, recommendations and placement?

PATLive ranks 4 by recommendation count. Mentions: 122 (42.36%); recommendations: 48 (16.67%). Top 3 placements: 30 (10.42%); first-place placements: 12 (4.17%).

Wording about the company: 50 positive, 72 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 39.3%. The net framing score was 0.41 (defined in Methodology).

74 mentions had no recorded recommendation. Of recorded recommendations, 62.50% were Top 3 and 25.00% were first-place.

From October 2026 to November 2026, PATLive's mention count rose from 63 to 122, while its recommendation count rose from 44 to 48. The recommendation rate was 14.81% and 16.67% respectively.

PATLive ranks fourth on recommendations with a moderate mention count. Its recommendation-to-mention ratio sits in the middle of the field, and its placements are modest relative to its recommendation total.

Neutral wording is more common than positive wording in its mentions, with no negative mentions recorded. Both mentions and recommendations were higher than in the prior month, though the samples differ and the comparison does not identify a cause.

Abby Connect

Questions This Section Answers

• What do the November 2026 AI answer results show about Abby Connect mentions, recommendations and placement?

Abby Connect ranks 5 by recommendation count. Mentions: 81 (28.13%); recommendations: 31 (10.76%). Top 3 placements: 17 (5.90%); first-place placements: 3 (1.04%).

Wording about the company: 33 positive, 48 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 38.3%. The net framing score was 0.41 (defined in Methodology).

50 mentions had no recorded recommendation. Of recorded recommendations, 54.84% were Top 3 and 9.68% were first-place.

From October 2026 to November 2026, Abby Connect's mention count rose from 61 to 81, while its recommendation count fell from 50 to 31. The recommendation rate was 16.84% and 10.76% respectively.

Abby Connect ranks fifth on recommendations. Its recommendation-to-mention ratio is moderate, and first-place placements are rare relative to its recommendation count, so most of its endorsements were not top-ranked.

Neutral wording outweighs positive wording in its mentions, and no negative mentions were recorded. Mentions were higher than in the prior month while recommendations were lower; because the monthly samples differ, that divergence is a recorded comparison rather than an explanation.

Specialty Answering Service (SAS)

Questions This Section Answers

• What do the November 2026 AI answer results show about Specialty Answering Service (SAS) mentions, recommendations and placement?

Specialty Answering Service (SAS) ranks 6 by recommendation count. Mentions: 57 (19.79%); recommendations: 28 (9.72%). Top 3 placements: 17 (5.90%); first-place placements: 5 (1.74%).

Wording about the company: 28 positive, 29 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 49.1%. The net framing score was 0.49 (defined in Methodology).

29 mentions had no recorded recommendation. Of recorded recommendations, 60.71% were Top 3 and 17.86% were first-place.

From October 2026 to November 2026, Specialty Answering Service (SAS)'s mention count rose from 42 to 57, while its recommendation count fell from 35 to 28. The recommendation rate was 11.78% and 9.72% respectively.

Specialty Answering Service ranks sixth on recommendations with a comparatively small mention base. Its recommendation-to-mention ratio is relatively high, so a larger share of its appearances carried an endorsement than for several higher-ranked peers.

Positive and neutral wording are nearly balanced, with no negative mentions recorded. First-place placements are a small subset of its recommendations. Mentions were higher than in the prior month while recommendations were lower, a recorded change whose cause the data do not establish.

MAP Communications

Questions This Section Answers

• What do the November 2026 AI answer results show about MAP Communications mentions, recommendations and placement?

MAP Communications ranks 7 by recommendation count. Mentions: 70 (24.31%); recommendations: 16 (5.56%). Top 3 placements: 11 (3.82%); first-place placements: 6 (2.08%).

Wording about the company: 17 positive, 53 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 22.9%. The net framing score was 0.24 (defined in Methodology).

54 mentions had no recorded recommendation. Of recorded recommendations, 68.75% were Top 3 and 37.50% were first-place.

From October 2026 to November 2026, MAP Communications' mention count rose from 51 to 70, while its recommendation count fell from 34 to 16. The recommendation rate was 11.45% and 5.56% respectively.

MAP Communications ranks seventh on recommendations despite a mention count above several higher-ranked peers. Its recommendation-to-mention ratio is among the lowest in the sample, so most appearances did not carry an endorsement.

Neutral wording strongly outweighs positive wording in its mentions, and no negative mentions were recorded. First-place placements form a notable share of its small recommendation base, which warrants caution given the sparse counts. Mentions were higher than in the prior month while recommendations were lower; the samples differ, so no cause is inferred.

VoiceNation

Questions This Section Answers

• What do the November 2026 AI answer results show about VoiceNation mentions, recommendations and placement?

VoiceNation ranks 8 by recommendation count. Mentions: 30 (10.42%); recommendations: 13 (4.51%). Top 3 placements: 7 (2.43%); first-place placements: 2 (0.69%).

Wording about the company: 13 positive, 17 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 43.3%. The net framing score was 0.43 (defined in Methodology).

17 mentions had no recorded recommendation. Of recorded recommendations, 53.85% were Top 3 and 15.38% were first-place.

From October 2026 to November 2026, VoiceNation's mention count fell from 35 to 30, while its recommendation count fell from 27 to 13. The recommendation rate was 9.09% and 4.51% respectively.

VoiceNation ranks eighth on recommendations with a small mention base. Its recommendation-to-mention ratio is moderate, and its placement counts are correspondingly small, so each figure rests on limited observations.

Positive and neutral wording are fairly balanced, with no negative mentions recorded. Both mentions and recommendations were lower than in the prior month. Because the monthly samples differ, that comparison describes recorded results and does not establish why they changed.

Moneypenny

Questions This Section Answers

• What do the November 2026 AI answer results show about Moneypenny mentions, recommendations and placement?

Moneypenny ranks 9 by recommendation count. Mentions: 47 (16.32%); recommendations: 11 (3.82%). Top 3 placements: 6 (2.08%); first-place placements: 3 (1.04%).

Wording about the company: 13 positive, 34 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 23.4%. The net framing score was 0.28 (defined in Methodology).

36 mentions had no recorded recommendation. Of recorded recommendations, 54.55% were Top 3 and 27.27% were first-place.

From October 2026 to November 2026, Moneypenny's mention count stayed at 47, while its recommendation count fell from 29 to 11. The recommendation rate was 9.76% and 3.82% respectively.

Moneypenny ranks ninth on recommendations. Its recommendation-to-mention ratio is low, meaning most of its appearances did not include an affirmative endorsement, and its placement counts are small.

Neutral wording substantially outweighs positive wording in its mentions, with no negative mentions recorded. Its mention count was unchanged from the prior month while recommendations were lower; only the recommendation count moved, so the two should not be described as jointly stable.

Davinci Virtual

Questions This Section Answers

• What do the November 2026 AI answer results show about Davinci Virtual mentions, recommendations and placement?

Davinci Virtual ranks 10 by recommendation count. Mentions: 33 (11.46%); recommendations: 8 (2.78%). Top 3 placements: 4 (1.39%); first-place placements: 1 (0.35%).

Wording about the company: 11 positive, 22 neutral, 0 negative mentions. The proportion of mentions that also included a recommendation was 24.2%. The net framing score was 0.33 (defined in Methodology).

25 mentions had no recorded recommendation. Of recorded recommendations, 50.00% were Top 3 and 12.50% were first-place.

From October 2026 to November 2026, Davinci Virtual's mention count rose from 18 to 33, while its recommendation count fell from 17 to 8. The recommendation rate was 5.72% and 2.78% respectively.

Davinci Virtual ranks tenth on recommendations with a small mention base. Its recommendation-to-mention ratio is low, and its placement counts are correspondingly small, so the figures rest on limited observations and should be read with that in mind.

Neutral wording outweighs positive wording in its mentions, and no negative mentions were recorded. Mentions were higher than in the prior month while recommendations were lower. The differing samples mean the comparison shows recorded change, not its cause.

Earlier ranking snapshot context

Questions This Section Answers

• How do the available Call Answering Services snapshots from July 2026 to November 2026 differ in recommendation coverage and measurement scope?

Only months with verified results are shown. Monthly sample coverage is listed in Methodology.

Each month is a separate sample of AI answers, and the number of answers included varies between months. Counts and rates should therefore be read together rather than as a continuous trend line.

Rank positions can shift between months without implying stable standing or equivalent sampling. The comparisons show how recorded results changed; they do not establish why any company's counts or rates rose or fell.

What the November Rankings Show

The November results answer several distinct questions. Reach asks how often a company appeared; recommendation asks how often an answer affirmatively endorsed it; placement asks where endorsements fell in an answer's ordering; framing asks how the wording was classified. These measures can point in different directions for the same company.

A high mention count with a modest recommendation count describes wide but often non-endorsing appearances. A high recommendation-to-mention ratio describes a larger endorsement share within a smaller base. Neither is a measure of customer preference, sales or company quality, and the recorded changes across months do not identify causes.

Methodology

Questions This Section Answers

• How were the November 2026 Call Answering Services AI visibility measures counted, and what do they not establish?

This benchmark has partial coverage: 3 of 3 planned question categories contributed usable observations; 0 contributed none. This describes question-category coverage, not demonstrated platform outages. Findings apply to the available answer set.

The month in the title is the reporting period. The exact dates when the original AI answers were collected have not been verified.

Each company is counted at most once in each answer that meets the benchmark rules. A recommendation requires both an affirmative endorsement and positive wording. Mention, recommendation, Top 3 and first-place rates use the full benchmark answer total. Top 3 and first-place results are included within recommendations, not added to them.

The recommendation-to-mention ratio divides recommendations by mentions. It describes AI answer wording, not customer conversion. Net framing is positive minus negative mentions, divided by all mentions, on a scale from -1 to +1. It does not measure customer satisfaction or company quality.

Market: US; 10 tracked companies. AI services represented: ChatGPT, Copilot, Gemini, Google AI Mode, Google AI Overviews, Perplexity.

Research screening totals (answer observations can include repeated questions):

The source does not provide a reader-facing definition of “reserved”; that screening label is reported without inferring a reason for exclusion. Only the included benchmark answers form the rate denominator.

One answer can mention or recommend several companies. Adding company totals would therefore count some answers more than once. The results describe the AI answers reviewed, not independently verified facts about the companies or their products.

Monthly sample coverage:

August 2026 measured a different set of companies, so it is excluded from this comparison.

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The benchmark covers a partial set of answers, and the exact collection dates for the original AI answers have not been verified. Findings apply to the available answer set rather than to all possible AI responses.

Each company is counted at most once per answer. Recommendation requires both affirmative endorsement and positive wording, and placement results are nested within recommendations rather than added to them. The recommendation-to-mention ratio and net framing describe answer wording, not customers, conversion or company quality. Monthly samples differ, so comparisons show recorded change without establishing why it occurred.