{"id":13,"date":"2025-12-22T14:18:35","date_gmt":"2025-12-22T14:18:35","guid":{"rendered":"https:\/\/gumshoeaiblog.wpenginepowered.com\/?p=13"},"modified":"2026-04-02T11:34:34","modified_gmt":"2026-04-02T18:34:34","slug":"gumshoe-vs-evertune","status":"publish","type":"post","link":"https:\/\/gumshoe.ai\/blog\/gumshoe-vs-evertune\/","title":{"rendered":"Gumshoe vs Evertune"},"content":{"rendered":"

Summary:<\/strong> gumshoe.ai is a persona\u2011first AI search visibility platform that runs live multi\u2011model queries to show where and why LLMs mention a brand, and what tactical levers will improve visibility. Its key differentiator is persona\u2011driven, model\u2011comparative diagnostics plus on\u2011platform optimization guidance<\/strong> for teams that need actionable brand management for AI search.<\/p><\/blockquote>\n

Overview<\/h2>\n

Gumshoe.ai provides scheduled, live diagnostic runs that query major LLMs, capture fresh answers, surface brand share of voice and cited sources, and produce prioritized recommendations to improve AI search visibility [1]<\/a>, [2]<\/a>. The product emphasizes building reports around business personas and topics, recommending multiple prompts per persona to reduce variance, and combining measurement with three tactical levers: first\u2011party content, technical fixes, and third\u2011party citation strategy [3]<\/a>, [4]<\/a>.<\/p>\n

Gumshoe documents pay\u2011as\u2011you\u2011go billing, with the first three runs free and an example per\u2011conversation rate of $0.10, plus a JSON export today and an API planned for release [5]<\/a>, [6]<\/a>. The company was founded in 2024 in Seattle, completed a $2M pre\u2011seed round, and launched a public beta with hundreds of customers as of 2025 [7]<\/a>, [8]<\/a>.<\/p>\n

Evertune \u2014 feature comparison<\/h2>\n

<\/p>\n\n\n\n\n\n\n\n\n\n\n
Attribute<\/th>\ngumshoe.ai<\/th>\nEvertune<\/th>\n<\/tr>\n<\/thead>\n
Models \/ coverage<\/td>\nLive prompts against major LLMs including Google Gemini, OpenAI ChatGPT, Perplexity, Anthropic\/Claude, with per\u2011run fresh answers and multi\u2011model comparison surfaces [2]<\/a><\/td>\nPositions coverage across major LLMs plus consumer app sampling and panel inputs, described as both base model and app\u2011level coverage [9]<\/a>, [10]<\/a><\/td>\n<\/tr>\n
Sampling \/ scale<\/td>\nPersona\u2011driven sampling, recommended 10+ prompts per persona, cost scales by conversations; best for targeted persona insights and iterative measurement [3]<\/a>, [11]<\/a><\/td>\nEmphasizes high volume sampling, reporting support for more than 1,000,000 AI responses per brand monthly, positioned for enterprise statistical confidence [10]<\/a><\/td>\n<\/tr>\n
Actionability<\/td>\nBuilt\u2011in Playbook, page audit checks, optimization recommendations, plus AI\u2011assisted content generation for formats LLMs prefer (FAQ, knowledge articles, how\u2011to) [4]<\/a><\/td>\nPositions deeper activation capabilities, partnerships for distribution and partner\u2011based activations, and content strategy services [12]<\/a>, [13]<\/a><\/td>\n<\/tr>\n
Integrations & exports<\/td>\nJSON export available, storage integrations referenced, API coming soon; no public GA\/Search Console direct connector documented as of reporting [6]<\/a><\/td>\nEmphasizes direct model API access plus consumer app and panel data, built for enterprise integration workflows [12]<\/a><\/td>\n<\/tr>\n
Pricing model<\/td>\nUsage based, first 3 runs free, example cost $0.10 per conversation; scheduled runs billed when executed [5]<\/a><\/td>\nEnterprise subscription pricing geared to higher scale implementations, public reporting references larger price points for enterprise customers [10]<\/a><\/td>\n<\/tr>\n
Ideal customer profile<\/td>\nMarketing and SEO teams that want persona\u2011focused diagnostics, tactical recommendations, and iterative measurement across models [1]<\/a><\/td>\nEnterprise brands that require very large sample sizes, panel data, and activation programs tied to distribution partners [12]<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/p>\n

Gumshoe.ai advantages, objectively stated<\/p>\n