Checked against our own brand on 23 August 2026: OpenAI, asked directly whether it would recommend GetIntel, scored it 8 out of 100, said it would not, unless specific details are verified, and framed it as an emerging or niche option. Perplexity and Gemini, asked the identical question the same week, both scored it 68 out of 100 and said yes, explicitly. Same brand, same category, same week. That spread is the actual argument for why "are we mentioned" is the wrong question to stop at.
Disclosure: GetIntel is an AI visibility tool, and the data below is about GetIntel's own brand, pulled from GetIntel's own tracking. It's not flattering everywhere, which is the point of publishing it rather than picking the two engines that make the number look good.
What's the Difference Between Being Mentioned and Being Recommended?
Being mentioned means an engine's answer includes your name somewhere. Being recommended means the engine, asked directly whether it would suggest you, says yes and means it.
Those are different questions with different failure modes. A brand can be named in an answer while the surrounding language hedges, compares unfavorably, or frames it as a fallback rather than a pick. A visibility count that only asks "were we named" can't see that difference, it counts a lukewarm mention and an enthusiastic one identically.
What Does GetIntel's Own Sentiment Data Actually Show?
Four engines, asked directly on 23 August 2026 whether they'd recommend GetIntel, and two different answers.
| Engine | Recommendation verdict | Score | What it actually said |
|---|---|---|---|
| Perplexity | Recommends you | 68/100 | Explicitly says it would recommend GetIntel for solo founders or lean teams |
| Gemini | Recommends you | 68/100 | Calls it "highly recommended" and one of the most user-friendly platforms in category |
| Google AI Overview | No clear signal | 34/100 | No direct recommendation language either way |
| ChatGPT (OpenAI) | Speaks negatively | 8/100 | Says it would not recommend GetIntel as the primary platform unless specific details are verified, framing tied to gaps in third-party sources rather than a settled negative view |
Pulled from GetIntel's own brand-tracking data, read 23 August 2026. Each score comes from the engine's own answer to a direct "would you recommend this" question, not from a blind category prompt.
The gap between Perplexity's 68 and OpenAI's 8 isn't noise, it's two engines giving substantively different answers to the same direct question about the same brand in the same week. A tool that only reported "mentioned in 3 of 4 engines" would flatten that entirely into a single reassuring-looking number.
Why Would the Same Brand Get Opposite Verdicts From Different Engines?
Because sentiment isn't just about the brand, it's about what each engine has actually found to read, and GetIntel's own gap points at a specific, checkable cause.
Per the same read, GetIntel is missing from several of the sources AI engines lean on to build a confident picture of a brand: Wikipedia, Wikidata, Reddit, G2, Product Hunt, and LinkedIn all came back without a strong footprint. The open web shows no clear consensus. That absence doesn't affect every engine equally, OpenAI's answer explicitly hedged with "unless specific details are verified," language that reads like an engine reaching for corroboration it couldn't find, while Perplexity and Gemini, whose retrieval leans more on live web search, apparently found enough to commit to a direct recommendation anyway.
The practical takeaway generalizes past this one brand: an engine's sentiment toward you is downstream of what it can verify about you, not just whether your name appears in its training data or a retrieved page.
How Do You Actually Measure Sentiment, Not Just Presence?
Ask the direct question, not the category question, and record what the engine actually says, not just whether it said your name.
A blind category prompt ("what's the best tool for X") tells you whether you come up unprompted, a recognition signal. A direct prompt ("would you recommend GetIntel for X") tells you something different: whether the engine, once your name is already on the table, actually endorses you or hedges. GetIntel's own data above used the second kind of prompt specifically, and it's the one that surfaced the OpenAI gap; a blind-only measurement would have missed it entirely. Read the same day, the blind category prompt returned "absent, named competitors instead" for all three engines, OpenAI, Perplexity, and Gemini alike, with no distinction between them. Only the direct-ask recommendation question split them 8 versus 68 versus 68. Recognition and endorsement aren't just different questions; on this brand, on this day, one of them showed no variation at all while the other showed the entire gap.
Recording the actual language matters too. "8/100" on its own doesn't tell you why. The reasoning behind it, that OpenAI treats GetIntel as unverified and niche, is the part that's actually actionable: it points at specific missing third-party sources rather than a vague instruction to "improve visibility."
What Doesn't This Data Settle?
Two things worth naming, since this is one brand's numbers, not a category rule.
- Whether this pattern holds for other brands. GetIntel's specific gap traces to specific missing third-party sources. A different brand with a different footprint could see the engines split in the opposite direction, or not split at all.
- Whether the missing-sources theory fully explains the OpenAI gap. It's the most concrete, checkable explanation this piece has, corroborated by OpenAI's own hedging language, but it hasn't been tested by fixing the gaps and re-measuring yet.
Where Did This Sentiment Data Come From?
All figures came from GetIntel's own brand-tracking data, read 23 August 2026. The recommendation verdicts and scores come from each engine's own answer to a direct recommendation question; the missing-sources finding comes from the same read's footprint check against Wikipedia, Wikidata, Reddit, G2, Product Hunt, and LinkedIn.
For a related but distinct axis, whether an engine links back to you at all once it names you, see mentioned vs cited by AI, the difference explained. For what happens after a gap like this is found, see why monitoring AI visibility alone isn't enough. And for a check on where the sources behind these citations actually concentrate, is YouTube really the top AI citation source found one platform's apparent lead was almost entirely a single piece of content.