How AI Search Is Recommending AI Chatbots: Monthly Trends

Executive Summary

The August 2026 benchmark run shows a stable category: valid recommendation coverage movement for every tracked brand fell within normal month-to-month variation, and WATI remained the coverage leader across both tracked months.

WATI led the category at 20.3% valid recommendation coverage in August, easing from 26.2% in July. The gap between WATI and second-place Yellow.ai narrowed from 15.1 points to 10.1 points across the two-month record.

Interakt recorded the largest coverage gain, rising from 9.3% in July to 9.8% in August, with raw mention presence climbing from 25.8% to 30.9%. WATI recorded the largest coverage decline over the same span, down 5.9 points, and its rank-one recommendation rate moved from 13.3% to 6.4% — the most notable single-metric shift tracked this month.

Three tracked brands — Engati, Gallabox, and Geta.ai — held at or below 1.3% coverage in both months, and Geta.ai recorded no presence at all in August after a single mention in July.

This benchmark's August 2026 run drew from 491 prompt-surface observations (385 unique questions) across the tracked AI/search surface universe. Of those, 435 mentioned a tracked brand or competitor; 312 were relevant to the benchmark's scope and 123 were screened out as irrelevant. The public benchmark uses the 236 observations that survived both qualification stages. July's run began from 397 prompt-surface observations (308 unique questions), with 359 brand or competitor mentions, 269 relevant and 90 irrelevant, yielding 225 qualified observations.

Key Findings

Benchmark Context

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

The qualified benchmark surfaced observations across all six tracked AI surface families in both months, providing a consistent basis for the brand-level metrics below.

Benchmark-Level Metrics

AI Recommendation Trend

The category was stable in August, with WATI retaining the coverage lead while the gap to second-place Yellow.ai narrowed slightly. No brand's coverage moved outside the range of normal month-to-month variation across the two tracked months.

Coverage by Brand

No brand's coverage movement this month fell outside the range of normal month-to-month variation for this two-month record. The category-level change reflects a combination of smaller shifts across several brands, led by WATI's decline and partly offset by Interakt's gain.

What Changed This Month

WATI: Coverage lead holds, placement softens

WATI held the coverage lead in August at 20.3%, down from 26.2% in July. The valid recommendation count fell from 59 to 48 observations.

The change was concentrated in placement strength. Rank-one recommendations fell from 13.3% in July to 6.4% in August, while overall presence eased from 61.8% to 54.2% and top-three rate moved from 17.8% to 14.0%.

The distinction to notice: WATI remained the most-mentioned brand in the category, but a smaller share of those mentions converted into top recommendations in August compared with July.

Highest-priority diagnostic: Which prompt types shifted from recommending WATI first to placing it lower or not at all, and which competitor captured those rank-one slots.

Interakt: Rising presence, softer first-place rate

Interakt's coverage moved from 9.3% in July to 9.8% in August, with valid recommendations up from 21 to 23. Raw mention presence climbed from 25.8% to 30.9%, the largest presence gain among tracked brands.

Top-three placements also moved up, from 6.2% to 7.2%, though rank-one rate eased from 3.6% to 2.5%. The brand's average recommended rank moved from 1.64 to 1.94, still inside the top two.

The distinction to notice: Interakt gained visibility and top-three positioning while its share of first-place recommendations softened.

Highest-priority diagnostic: Which surfaces or prompt types now surface Interakt but stop short of ranking it first, and what evidence source supports the second-and-third-place positioning.

Gupshup: Coverage softens despite stronger presence

Gupshup's coverage moved from 4.9% in July to 3.4% in August, with valid recommendations down from 11 to 8. Raw mention presence, however, rose from 10.7% to 11.9%.

The divergence is notable: the brand is present in more answers but recommended validly less often. Rank-one rate moved from zero to 0.9%, but the overall coverage figure declined. One negative visibility observation appeared in August (0.4% negative visibility rate) after a clean July.

The distinction to notice: Gupshup is being mentioned more but recommended less. The presence-to-coverage gap widened in August, and the data does not establish why AI systems surface the brand without endorsing it as often.

Highest-priority diagnostic: Which prompts mention Gupshup without a valid recommendation, and what comparative or qualifying language accompanies those mentions.

Haptik: Presence and coverage both recede

Haptik's coverage moved from 1.3% in July to 0.4% in August, with valid recommendations dropping from 3 to 1. Raw mention presence also declined from 4.0% to 2.1%.

All placement metrics softened: top-three rate from 0.9% to 0.4%, rank-one rate from 0.4% to zero. Net sentiment by mentions moved from 0.67 to 0.20. The absolute counts are small, so these movements carry less weight, but the direction is consistent across metrics.

The distinction to notice: Haptik's decline is broad-based across presence, placement, and sentiment.

Highest-priority diagnostic: Which surfaces that previously ranked Haptik no longer mention it, and which brand now takes the recommendation those surfaces previously assigned.

Geta.ai: No presence in August

Geta.ai went from 1 raw mention in July (0.4% presence) to zero mentions in August. Valid recommendation coverage was zero in both months, and the brand recorded no positive, neutral, or negative visibility in August.

The distinction to notice: Geta.ai registered no presence across any of the six qualified AI surface families in August.

Highest-priority diagnostic: Whether Geta.ai's absence reflects a prompt-coverage gap, an evidence-source gap, or a genuine change in AI system awareness of the brand.

Buyer-Intent Interpretation

All 236 qualified observations in August fell into the Brand Recommendation cluster, with 7 of 8 tracked companies present. The pricing and value and multi-brand comparison clusters registered zero observations, meaning the public benchmark cannot yet answer questions about how AI systems frame cost, value, or head-to-head tradeoffs for this vertical.

Brand Opportunity Summary

The benchmark identifies where attention is warranted; a company-level analysis is needed to explain why.

Evidence Behind the Benchmark

The aggregate metrics are built from prompt-level observations (query, surface, recommendation outcome, rank, sentiment, and citations where exposed). Company-level analysis can go deeper into prompt, competitor, surface, and evidence patterns. Presence in a benchmark response is not automatically treated as proof of causation.

About This Benchmark

This report is part of CiteWorks Studio's AI Industry Market Discovery research program.

• AI Industry Market Discovery Methodology
• AI Industry Market Discovery Metrics
• AI Industry Market Discovery Standards

Report-Specific Interpretation Notes

• Small-count movement: brands with fewer than 10 valid recommendations have movements that carry less weight; a change of one or two observations can shift their rates materially.
• Qualified denominator: all percentages use the 236 qualified observations as the public denominator, not the 491 raw prompt-surface observations.
• Directional analysis: month-over-month movement identifies changes worth investigating; it does not by itself establish the cause of those changes.

Next Step

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

Beneath the aggregate percentages sit specific questions: which high-intent prompts are won, which competitor takes the recommendation when a brand loses, what attributes AI associates with each option, and which external sources shape those answers. The benchmark shows that WATI's rank-one placement share eased and that Geta.ai had no presence in August, but it cannot by itself explain which evidence sources or prompt patterns drove those outcomes.

A company-specific AI visibility audit maps those prompt, surface, competitor, ranking, sentiment, and evidence-source patterns into a prioritized visibility strategy. It turns the benchmark's directional signals into concrete, actionable answers about where and why a brand is winning or losing AI-driven recommendations.

Request an AI visibility audit