AI vendor recommendations can be narrow: what 40 ChatGPT and Gemini answers did (and didn’t) name

By Outranker · Published 2026-10-06 · Version 2

Sample: 40 AI answers to 20 questions · Engines: ChatGPT, Gemini · Collected 2026-10-06 – 2026-10-06

Executive summary

AI assistants can produce confident “vendor shortlists,” but this benchmark suggests those shortlists may be narrower than they look.

Findings (mentions, not citations):

• Across 40 successful answers (20 ChatGPT, 20 Gemini), Profound appeared more often than any other tracked competitor (4 of 40).
• Peec AI was mentioned in 2 of 40 answers.
• Otterly.AI was mentioned in 2 of 40 answers.
• Outranker was mentioned in 0 of 40 answers overall.
• Outranker’s mention rate was 0 of 20 in ChatGPT and 0 of 20 in Gemini, indicating no difference between the 2 tested engines for this outcome.

Implication: If procurement teams use ChatGPT/Gemini as an informal first-pass shortlist, they may be seeing a concentrated slice of the category rather than broad market coverage—so AI-generated lists should be validated with a structured vendor scan, especially given this dataset is limited to 40 answers and includes no source-linked outputs (mentions, not citations).

Key findings

• Gemini: Outranker mentioned in 0 of 20 answers (0%).
• ChatGPT: Outranker mentioned in 0 of 20 answers (0%).
• Competitor Profound was mentioned in 4 of 40 answers, versus 0 for Outranker.
• Competitor Peec AI was mentioned in 2 of 40 answers, versus 0 for Outranker.
• For "Comparison" questions, Outranker was mentioned in 0 of 20 answers (0%).
• Outranker was mentioned in 0 of 40 successful AI answers (0%).
• Mention rates were the same across Gemini, ChatGPT (0% each).
• Competitor Otterly.AI was mentioned in 2 of 40 answers, versus 0 for Outranker.

Procurement teams are increasingly asking AI assistants for “the best tools” in a category—expecting a wide scan that surfaces a meaningful share of the market.

This benchmark focuses on a simpler, buyer-relevant check:

> When you ask ChatGPT and Gemini about AI-visibility tracking tools, how often do they name specific vendors—and how concentrated are those mentions?

A critical caveat up front: this dataset captures mentions, not citations. The recorded outputs included 0 source-linked answers, so the counts reflect which brands were *named* in model-generated text—not which brands were supported by a linked source.

The procurement risk: shortlists that look complete Assistant-generated vendor lists often arrive in polished formats—tables, pros/cons, “best for” categories, and confident summaries.

Even when an answer looks comprehensive, the vendor naming can still be selective. For buyers, that creates an operational risk:

• A shortlist can *read* like a market scan.
• The underlying vendor set can be narrow.

That matters most at the start of the buying process, when teams form the initial option set.

What we measured This benchmark measured basic visibility: whether a vendor name appeared.

Scope (at a glance)

• Engines tested: ChatGPT, Gemini
• Sample: 40 successful answers total
• Tracked brands: Outranker + 3 competitors (Profound, Peec AI, Otterly.AI)
• Counted outcome: brand mentions in the assistant’s text output

No attempt was made to infer *why* a brand was mentioned or not mentioned. The objective is descriptive: what surfaced in this sample.

Findings: sparse mentions, concentrated among a few names Across 40 successful answers, competitor naming was sporadic.

Tracked competitors surfaced at low rates In this sample, the tracked competitors appeared as follows:

• Profound: 4 of 40 answers
• Peec AI: 2 of 40 answers
• Otterly.AI: 2 of 40 answers

Within the tracked set, Profound appeared more frequently than the other tracked competitors.

Outranker did not appear in this run - Outranker: 0 of 40 answers

This does not provide an explanation. It records that Outranker did not appear in the successful answers collected for this benchmark.

Engine split: Outranker mention rate was unchanged across ChatGPT and Gemini Outranker’s mention rate was the same across the 2 tested engines:

• ChatGPT: 0 of 20
• Gemini: 0 of 20

Prompt cluster check: “Comparison” questions For the recorded “Comparison” question cluster:

• Outranker: 0 of 20 answers

How to use assistant shortlists in procurement (without over-weighting them) If your organization uses ChatGPT or Gemini outputs as an informal shortlist, treat those outputs as inputs, not a substitute for a vendor scan.

1) Validate the option set with a structured scan If the assistant produces a clean list of vendors, assume it may be incomplete—especially when the observed mention counts are low (for example, 4 of 40 for the most-mentioned tracked competitor in this sample).

A structured scan can be lightweight:

• define evaluation criteria
• identify vendors beyond the assistant’s named set
• confirm fit through demos and references

2) Separate “discovery” from “comparison” Operationally, keep 2 distinct steps:

• Discovery: prompts intended to expand the option set
• Comparison: prompts intended to evaluate tradeoffs across a candidate list you already assembled

3) Track mentions as a signal—with explicit caveats Mentions can be measured and monitored over time.

But in a dataset with 0 source-linked answers, mention counts are best treated as a visibility indicator—not as validation, proof, or citation.

4) Add a lightweight internal gate before procurement decisions If internal stakeholders bring AI-generated shortlists into procurement conversations:

• require an independent vendor scan alongside the assistant output
• record which engine and prompt cluster produced the list
• repeat the same prompt set over time and compare mention counts

Limitations Keep conclusions proportional to the dataset:

• 40 successful answers across 2 engines (ChatGPT, Gemini)
• 0 source-linked answers, so results are mentions, not citations
• Each prompt was run once per engine; results can differ with repeated runs, additional engines, or different prompt sets

Data summary (tracked brands)

| Brand (tracked) | Mentions | Total answers | |---|---:|---:| | Profound | 4 | 40 | | Peec AI | 2 | 40 | | Otterly.AI | 2 | 40 | | Outranker | 0 | 40 |

| Engine | Outranker mentions | Total answers | |---|---:|---:| | ChatGPT | 0 | 20 | | Gemini | 0 | 20 |

CTA: Read the full research on Outranker’s site: https://www.outranker.com

Limitations

• Gemini, ChatGPT answer from model knowledge without live sources; a mention there is not a citation.

Suggested citation

**Plain:** Outranker Research (2026). “AI vendor recommendations look concentrated: few competitor names surfaced across ChatGPT and Gemini.” https://www.outranker.com. **APA style:** Outranker Research. (2026). *AI vendor recommendations look concentrated: few competitor names surfaced across ChatGPT and Gemini*. Outranker. https://www.outranker.com

Methodology