How AI Search Is Recommending Virtual Receptionist Services: Monthly Trends

Executive Summary

Ruby holds the category lead in October 2026 with valid recommendation coverage of 53.6%, and the gap to Smith.ai at 52.7% is 0.9 points, unchanged from September's margin. Leadership remains contested at the top of the category, and the two-brand race that compressed in September stayed tight through October.

The strongest upward movement in coverage this month belongs to Ruby, which added 3.6 points from September to October (50.0% to 53.6%), breaking a prior decline. Smith.ai also rose 3.6 points (49.1% to 52.7%), holding its near-tie at the top. Neither move registers as a significant month-over-month shift, so the category reads as stable at the top even as the placement metrics beneath it moved.

The sharpest decliner from baseline remains Abby Connect, down 22.9 points from 40.9% in July to 18.0% in October, the third consecutive monthly decline. Moneypenny, Nexa, and Posh Virtual Receptionists also remain on multi-month decline streaks. Against the prior month, however, AnswerConnect, Davinci Virtual, Ruby, and Smith.ai all moved upward, and Nexa's 3.9-point drop from September was the only prior-month move that registered as significant.

This month sits within a four-month series in which Ruby has held the top spot throughout, though its lead over Smith.ai has compressed from 13.3 points in July to 0.9 points in October, and the declines from July's baseline are steep and widely distributed. Ruby ended October at 53.6% against 69.9% at baseline, Smith.ai at 52.7% against 56.6%, and AnswerConnect at 41.6% against 63.4%. Seven of ten tracked brands carry a significant-decliner classification against baseline; none rose significantly.

Each monthly run begins with 800 prompt-surface observations (599 unique questions in October, compared with 522 in July, 573 in August, and 571 in September) across the benchmark's defined AI/search surface universe. Of those, 800 mentioned a tracked brand or competitor; 358 were relevant and 442 were irrelevant in October. The public metrics use the 317 qualified observations in October, against 279 in July, 303 in August, and 330 in September.

Key Findings

AI Response Inconsistency Alerts

The benchmark detected 16 critical or high-severity factual inconsistencies across 5 AI platforms, concentrated in pricing claims that vary materially from one surface to the next.

Smith.ai

Twelve of the sixteen conflicts involve Smith.ai, spread across ChatGPT, Copilot, Gemini, AI Mode, and AI Overviews simultaneously. Nearly every conflict concerns the starting monthly price.

When asked "What is the best answering service for small business?", ChatGPT stated $300/mo for 30 calls, citing the company's own pricing page, while Copilot stated $97.50 for 30 calls. The flagged source on the first side quotes a Starter tier at "30 calls $300 / month."

When asked "What is the best AI answering service?", ChatGPT stated that paid plans start at $150/month, citing the AI receptionist pricing page and a third-party comparison, while Copilot stated $95/mo for AI and $292.50/mo for live. The flagged pricing page excerpt reads "Pro Set it up yourself with dedicated support. $150 /mo."

When asked "How much does a phone answering service cost?", Copilot stated Smith.ai starts at $255/month for 20 calls with overages at $12.75/call, while Gemini stated per-call plans cost around $7.00 to $9.75 per call, bundled into tiers of roughly $95 to $300/month. Flagged sources on both sides describe a $255 base tier and a from-$95 AI-first tier.

When asked "What is the best AI phone answering service?", ChatGPT stated Pro at $150/mo plus per-call fees, while Copilot stated $95/mo for AI only and $292.50/mo for AI plus human. The flagged pricing page excerpt reads "Pro Set it up yourself with dedicated support. $150 /mo $2.00/call."

When asked "What is the difference between Smith.ai and ChatGPT?", AI Mode stated plans start around $95/mo for basic AI receptionists, while AI Overviews stated pricing starts around $285 to $292/month. Flagged sources reference both a $95 self-service AI tier and a $292.50 human tier for 30 calls.

When asked "What is the best answering service for small business?", ChatGPT stated $300/mo for 30 calls, while AI Overviews stated it starts at $95/month. The flagged pricing page excerpt again reads "30 calls $300 / month."

When asked "How much does an AI answering service cost?", Copilot stated roughly $95/month for about 50 calls, while Gemini stated hybrid plans start around $150 to $300+ per month. Flagged sources describe a per-call AI tier near $95/mo and a human tier from $300/month.

When asked "What is the best AI answering service?", AI Mode stated plans start around $300/month while Copilot stated $95/mo AI and $292.50/mo live.

When asked "What is the best AI answering service?", ChatGPT stated paid plans start at $150/month while AI Mode stated plans start around $300/month, both citing pages associated with the company's own pricing.

When asked "What is the best phone answering service for small businesses?", ChatGPT stated human plans start around $300/month, citing a company comparison page that lists "Starting cost $300/month," while AI Overviews stated starting at $95/month.

When asked "What's the best AI virtual receptionist for appointment scheduling?", Copilot stated $292.50/mo for 30 calls while Gemini stated it starts around $95/month for basic call handling.

When asked "What is the best answering service?", ChatGPT stated it starts around $300/month while Gemini stated roughly $95/mo plus per-call fees. Flagged sources on the first side reference "$300/mo, 30 calls" and a $300/month starting cost.

AnswerConnect

One conflict involves Copilot and Gemini on the entry-plan monthly price. When asked "How much does AnswerConnect cost?", Copilot stated the Entry Plan at 150 minutes for £205/month plus a £49.99 setup fee, citing the company's own plans page, while Gemini stated around $350 per month for 200 minutes, with a possible 100-minute tier near $325/month, plus a one-time $49.99 setup fee. Flagged sources list both a £205 entry tier and a $350/200-minute entry tier.

PATLive

One conflict involves ChatGPT and AI Mode on the starting monthly price. When asked "What is the best phone answering service for small businesses?", ChatGPT stated roughly $250/mo starting, with a 200-minute plan near $349/mo, while AI Mode stated tiered minute plans starting around $75/mo. Flagged sources describe a $349 figure for 200 minutes and a $75-plus-usage tier.

Posh Virtual Receptionists

One conflict involves AI Mode and Gemini on the entry monthly price. When asked "What is the cheapest virtual answering service?", AI Mode stated it starts around $65/month, citing the company's pricing page showing a Chïc 0 tier at $65.00 per month, while Gemini stated entry-level live receptionist tiers generally start around $137 to $150 per month.

Ruby

One conflict involves Copilot and Gemini on the monthly price and overage rate. When asked "How much does a phone answering service cost?", Copilot stated Ruby charges $319/month for 100 minutes with overages at $3.19/minute, while Gemini stated entry packages start around $135 to $250/month for roughly 50 to 100 minutes, with per-minute rates of $0.75 to $3.50. Flagged sources list both a $319/100-minute tier and a $250/50-minute tier.

Benchmark Context

Questions This Section Answers

• How many prompt-surface observations qualified for the October analysis?
• What changed in the benchmark-level metrics between July and October?

The report separates the raw collection universe from the qualified analysis set. Brand-level recommendation percentages are calculated within the qualified benchmark set.

August sat between these months with 303 qualified observations, 335 relevant prompts, and 465 irrelevant prompts. September recorded 330 qualified observations, 372 relevant prompts, and 428 irrelevant prompts, the highest qualified total in the series before October eased to 317.

Benchmark-Level Metrics

The October recommendation-shaped answer share of 57.1% is the highest in the four-month series, moving sharply against the September reading of 32.1%. The valid recommendation shortlist share recovered to 67.5% from September's 62.7%, still well below July's 80.6%. The benchmark can describe this distribution shift but cannot establish what caused it.

AI Recommendation Trend

Questions This Section Answers

• Who leads the virtual receptionist category, and how tight is the race at the top?
• Which brands declined significantly against the July baseline, and how large were those drops?

The Two-Brand Race at the Top Holds Steady While the Mid-Field Keeps Contracting

October leaves the top of the category essentially where September placed it. Ruby and Smith.ai both gained 3.6 points from the prior month and remain 0.9 points apart, while the broader field continued to lose coverage against July's baseline.

Seven of the ten tracked brands moved far enough from their July baseline to register as significant declines, while most prior-month changes stayed within normal variation. The category-level change since July reflects the combination of those seven declines rather than any single brand's shift, and the October readings represent a partial recovery within that longer contraction for brands such as Ruby, AnswerConnect, and Davinci Virtual that posted prior-month gains.

What Changed This Month

Questions This Section Answers

• What moved for Ruby and Smith.ai between September and October?
• Where did this month's declines concentrate across the tracked brands?
• What separates a brand losing first-place placements from one losing shortlist presence entirely?

Ruby

Ruby's valid recommendation coverage rose 3.6 points from 50.0% in September to 53.6% in October, a non-significant move that nonetheless restored it to the category lead. Against July's baseline of 69.9%, Ruby remains down 16.3 points, a significant decline that has accumulated across the series.

Placement strengthened alongside coverage. Top-three recommendations rose from 38.2% in September to 42.6% in October, and rank-one recommendations rose from 10.0% to 15.8%. Raw mention presence held at 81.4%, essentially unchanged from 80.9% in September and near July's 85.0%. Ruby's valid recommendation count moved from 195 of 279 observations in July to 170 of 317 in October.

The distinction to notice is that Ruby's presence was never the constraint. Its raw mention presence has stayed above 80% every month while coverage fell 16.3 points, which points to placement dynamics rather than visibility loss. Net sentiment eased from 0.9 to 0.7 from July to October, a secondary signal worth monitoring beside the coverage figure.

Highest-priority diagnostic: Which prompt patterns still keep Ruby out of top-three placements, and which providers hold the slots it does not?

Smith.ai

Smith.ai's valid recommendation coverage rose 3.6 points from 49.1% in September to 52.7% in October, a non-significant prior-month move that keeps it within a point of the lead. Against July's baseline of 56.6%, the decline of 3.9 points is not significant, making Smith.ai one of only three brands classified as stable.

The more notable movement is in rank-one recommendations, which climbed from 7.5% in July to 20.5% in October, a 13.0-point increase since baseline. Raw mention presence also rose, up 8.6 points from 69.9% to 78.5%. Top-three rate moved from 36.6% to 38.8%. Smith.ai's valid recommendation count moved from 158 of 279 observations in July to 167 of 317 in October.

The distinction for Smith.ai is that its aggregate coverage looks flat while its first-place share and its presence both moved upward from baseline. That combination suggests AI surfaces are surfacing Smith.ai more often and placing it first more often, even as its overall shortlist coverage holds near baseline. Net sentiment eased from 0.8 to 0.7 from July to October.

Highest-priority diagnostic: Which prompt categories drove the rank-one and presence gains, and do those overlap with the prompts where AnswerConnect and Abby Connect lost placement?

Abby Connect

Abby Connect was the largest baseline-to-current decliner in the category, with valid recommendation coverage down 22.9 points from 40.9% in July to 18.0% in October. The decline is significant and has now run three consecutive months, though the prior-month move of 3.8 points from September did not clear its significance threshold.

Every tier of visibility weakened. Raw mention presence fell 23.3 points from 44.4% to 21.1%. Top-three recommendations fell 8.2 points from 16.1% to 7.9%. Rank-one recommendations eased 0.9 points from 1.8% to 0.9%. Abby Connect's valid recommendation count dropped from 114 of 279 observations in July to 57 of 317 in October.

The distinction is that Abby Connect lost shortlist presence and top-three placement together, rather than losing first-place positioning specifically. Its rank-one rate was already low at baseline, so the bulk of the contraction happened in the broader recommendation tiers. Net sentiment held at 0.9, down only slightly from 1.0 at baseline.

Highest-priority diagnostic: Which prompt categories dropped Abby Connect out of shortlists, and which brands absorbed the slots it vacated?

AnswerConnect

AnswerConnect's valid recommendation coverage fell 21.8 points from 63.4% in July to 41.6% in October, a significant decline against baseline. Unlike the three-month slide through September, October brought a 1.0-point prior-month gain from September's 40.6%, the first upward move in the series.

Placement weakened across the series even as presence recovered slightly. Raw mention presence fell 19.5 points from 72.8% to 53.3%. Top-three recommendations fell 19.9 points from 53.0% to 33.1%. Rank-one recommendations fell 21.3 points from 32.3% to 11.0%, the largest drop in the rank-one tier anywhere in the tracked series. AnswerConnect's valid recommendation count moved from 177 of 279 observations in July to 132 of 317 in October.

The distinction is that AnswerConnect's losses concentrated in rank-one positioning more than any other brand: it lost roughly two-thirds of its first-place share while retaining a top-three rate above 33%. That pattern separates it from brands that lost broader shortlist presence. Net sentiment eased from 0.9 to 0.8.

Highest-priority diagnostic: Which prompts did AnswerConnect stop winning outright, and which providers took those first-place placements?

Moneypenny

Moneypenny's valid recommendation coverage fell 21.1 points from 29.0% in July to 7.9% in October, a significant decline that has run three consecutive months. The prior-month move of 2.7 points from September did not clear its threshold.

The decline combined thinner presence with weaker placement. Raw mention presence fell 19.7 points from 33.3% to 13.6%. Top-three recommendations fell 7.1 points from 11.8% to 4.7%. Rank-one recommendations eased 0.9 points from 1.8% to 0.9%. Moneypenny's valid recommendation count fell from 81 of 279 observations in July to 25 of 317 in October, a small absolute base that warrants caution when reading the percentage movement. Net sentiment eased more sharply than most peers, from 0.9 to 0.6.

The distinction is that Moneypenny lost shortlist presence more than first-place positioning. Its rank-one rate stayed near 1%, so the contraction came from disappearing out of recommended shortlists rather than being demoted within them.

Highest-priority diagnostic: Which prompts dropped Moneypenny out of shortlists entirely, and is its sentiment decline concentrated in specific AI surfaces?

Nexa

Nexa's valid recommendation coverage fell 11.5 points from 14.0% in July to 2.5% in October, a significant decline that has run three consecutive months. Nexa was also the only brand whose prior-month move registered as significant, down 3.9 points from September's 6.4%, making it the sharpest single-month mover in October.

Presence and placement both collapsed. Raw mention presence fell 13.0 points from 15.8% to 2.8%. Top-three recommendations fell 3.7 points from 5.0% to 1.3%. Rank-one recommendations fell 1.8 points from 1.8% to 0.0%, leaving Nexa with no first-place placements this month. Nexa's valid recommendation count fell from 39 of 279 observations in July to 8 of 317 in October.

These are small absolute counts, so the percentage movements should be read with caution. The distinction for Nexa is that its net sentiment score held at 1.0 across the series even as coverage collapsed, which means AI systems that do mention Nexa describe it favorably, but they mention it far less often.

Highest-priority diagnostic: Is Nexa losing recommendation eligibility across specific surfaces, or is its thinner presence concentrated in one part of the prompt set?

Davinci Virtual, Posh Virtual Receptionists, and PATLive

Davinci Virtual fell 10.7 points from 16.1% in July to 5.4% in October, a significant decline against baseline, though October brought a 1.2-point gain from September. Its raw mention presence fell 11.0 points to 6.6%. Its top-three rate rose 2.4 points from 1.1% to 3.5%, and its rank-one rate moved from 0.0% to 0.9%. Davinci Virtual's valid recommendation count fell from 45 of 279 observations in July to 17 of 317 in October.

Posh Virtual Receptionists fell 9.6 points from 22.2% in July to 12.6% in October, a significant decline against baseline; its prior-month move of 2.8 points was not significant. Raw mention presence fell 8.4 points to 16.7%. Top-three rate eased 1.2 points to 6.3%. Posh's valid recommendation count fell from 62 of 279 observations in July to 40 of 317 in October.

PATLive held stable at 14.8% in October against 15.8% in July, a change that was not significant, and the second-smallest baseline-to-current move in the category. Its top-three rate rose 4.6 points from 3.9% to 8.5%, and its rank-one rate moved from 0.0% to 0.6%. Its valid recommendation count moved from 44 of 279 observations in July to 47 of 317 in October.

The distinction across these three is that their coverage fell (or held) while their placement quality moved in the opposite direction. Davinci Virtual's top-three rate rose even as its coverage dropped, and PATLive's did the same while its coverage held. All three sit on small absolute counts, so the percentage moves are best read as directional. PATLive's net sentiment eased from 0.9 to 0.7.

Highest-priority diagnostic: For each of these brands, is the coverage decline a loss of recommendation eligibility or thinner overall presence in AI-generated answers?

Conversational

Conversational remained at 0.0% coverage across all four months, with zero valid recommendations at every reading and no presence in October. The category remains effectively closed to Conversational in AI recommendation outputs.

Highest-priority diagnostic: What evidence source, if any, would establish eligibility for Conversational in AI-generated recommendations?

Buyer-Intent Interpretation

Questions This Section Answers

• What buyer-intent clusters does the benchmark capture, and what does each one measure?
• Why can't the benchmark answer pricing or head-to-head comparison questions yet?

In October, all 317 qualified observations fell into the brand recommendation cluster, as they did in every prior month of the series. The response-type mix within those observations shifted, with recommendation shortlists rising to 181 observations from 97 in September and pricing analysis rising to 67 from 56, while comparison analysis fell to 15 from 66. None of these constituted a separate qualified cluster.

The public benchmark therefore cannot yet answer the commercial questions that sit beside brand recommendation: whether price sensitivity changes which providers are surfaced, how providers compare when users ask for them head to head, and how value claims influence recommendation outcomes. Those questions remain outside the qualified set even as pricing-shaped responses grew within it.

Brand Opportunity Summary

Questions This Section Answers

• Which brands show the strongest signal in October, and which carry the steepest declines?
• What is the priority diagnostic for each brand in the category?

The benchmark identifies where attention is warranted across the category; a company-level analysis is needed to explain why individual brands moved as they did.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations capturing the query, the AI surface used, the recommendation outcome, the rank of any brand mentioned, sentiment, and citations where exposed. Company-level analysis can go deeper into prompt wording, which competitors appear when a brand is absent, and which external sources AI systems draw upon. Source presence is not automatically treated as proof of causation.

About This Benchmark

This report is part of the LLM Authority Index AI Visibility Market Discovery research program.

• AI Visibility Industry Market Index
• AI Visibility Industry Market Methodology
• AI Visibility Industry Market Metrics
• AI Visibility Industry Market Standards

Report-Specific Interpretation Notes

• Small-count brands such as Nexa, Davinci Virtual, and Moneypenny carry valid recommendation counts in the low tens or below, so their percentage movements should be read as directional rather than definitive.
• The qualified denominator of 317 observations in October differs from the raw collection of 800 prompt-surface pairs; brand-level percentages reflect only the qualified set.
• Movement is not cause. The benchmark identifies changes worth investigating; a brand's coverage decline alongside stable presence, as with Ruby's 81.4% presence against a 16.3-point baseline coverage drop, points to placement dynamics rather than simple visibility loss.

Next Step

The Public Benchmark Shows Where a Brand Is Winning or Losing. A Company-Level Audit Shows Why.

Beneath the aggregate percentages sit questions this benchmark cannot answer: which high-intent prompts does a brand win, which competitor takes the recommendation when it loses, what attributes do AI systems associate with each provider, and which external sources shape those answers. The October pattern, with a tight two-brand race at the top and seven brands still below their July baseline, makes those questions pressing for any brand whose valid recommendation coverage has fallen or whose first-place share has shifted.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy, turning a category-wide pattern into a brand-level action plan.

Request an AI visibility audit