Otterly vs Conductor
Gane, the publisher, sells software in this category. Every product here is measured by the same method as in every other category. How the conflict is handled.
Zero of fourteen models named Otterly first on the direct prompt; zero named Conductor. Otterly was named by fourteen of the fourteen models and Conductor by nine and Otterly carries 46 labels and Conductor 14, so the shares are not directly comparable.
Named in two categories this edition.
New York City, United States, founded 2006. Named in two categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the AI visibility and answer engine tracking tools page.
By framing
OtterlyFirst choices, of fourteen modelsConductor
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | | | ||
| GPT-5.4 mini | | | | |||
| Gemini 3.5 Flash | | | ||||
| Perplexity Sonar | | | | |||
| Grok 4.20 | | | | |||
| Mistral Small | | | | |||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | | | ||
| Qwen 3.7 Flash | | | ||||
| Kimi K2 | | | | |||
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | | | |||
| GPT-6 Luna | | | ||||
| Muse Glimmer 30B | | | |
Otterly Conductor first choice named as an alternative argued againstblank: not namedEach cell is one answer, Otterly on the left and Conductor on the right.The direct prompt
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Otterly51%#1 of 8Conductor—not namedOtterly20%#2 of 10Conductor2%#7 of 10Conductor4%#4 of 11Otterly2%#9 of 11What the models said about Otterly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“some tools to be cautious about or avoid include Otterly, HubSpot AEO Grader, and Peec AI” Llama 4 Maverick · negative prompt · hard negative
“Some tools marketed broadly (like Otterly.AI, Peec AI, Rankscale) were flagged as "highly problematic for enterprise" use” MiniMax M2.5 · negative prompt · soft negative
“Monitoring-only tools (Otterly, SE Visible, Nightwatch) reveal visibility is low but don't tell you how to fix it.” Claude Haiku 4.5 · negative prompt · soft negative
“The best AI visibility and answer engine tracking tool for a company with a limited budget is Otterly.AI, which costs $29 a month.” Llama 4 Maverick · budget prompt · first choice
“the best AI‑visibility and answer‑engine tracking tool for a company with a limited budget is Otterly.ai” GLM 4.7 FlashX · budget prompt · first choice
“the best AI visibility and answer engine tracking tool is Otterly.ai (starting at ~$29/month)” Grok 4.20 · budget prompt · first choice
What the models said about Conductor
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“Enterprise-priced ($50k+), overkill for mid-market” DeepSeek V4 Flash · direct prompt · hard negative
“without the enterprise overhead of tools like BrightEdge or Conductor” Grok 4.20 · paraphrase prompt · soft negative
“Enterprise | Profound, Scrunch AI, Conductor, Botify | 500+ person orgs needing compliance, scale, and dedicated support” Kimi K2 · scale prompt · first choice
“Choose Profound or Conductor if you have a large budget and need comprehensive, multi-language, multi-engine data” Qwen 3.7 Flash · comparative prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.