comparison

Gumshoe vs Profound

Gumshoe AI

22 Jan 2026 — 4 min read

Summary: gumshoe.ai measures how large language models mention brands by running persona‑driven prompts across many LLMs and surfacing citations, visibility scores, and tactical AIO recommendations. Its chief differentiator is lightweight, pay‑per‑report access to multi‑model diagnostics plus exportable JSON that preserves prompts, model responses, citations, and recommended actions. [1]][2]]

Overview

gumshoe.ai provides targeted diagnostics for AI search visibility, designed for marketers and SEO teams that want to see how buyer personas are answered by multiple AI models, which sources those models cite, and which tactical changes will improve brand presence in answer engines. The product runs persona‑based prompts against dozens of model endpoints, aggregates mentions and citation data into visibility metrics, and issues an AIO (AI Optimization) set of recommended actions including first‑party content, schema and third‑party outreach. The platform is offered as a pay‑per‑report service with a JSON export for programmatic analysis, making it suitable for teams that need repeatable testing without large upfront contracts. [3]][4]][5]]

Profound (tryprofound.com) — feature comparison, and where gumshoe.ai excels

Feature / attribute	gumshoe.ai (summary)	Profound (summary)
Model coverage and behavior	Runs persona prompts across Google Gemini variants, OpenAI GPT variants, Perplexity Sonar, Anthropic Claude, xAI Grok, DeepSeek and others, with documentation about which models use live web search vs foundational training data [6]][7]]	Positions itself to track answer engines including ChatGPT, Perplexity and Google AI Overviews, and markets an index of answer engine activity for enterprises [8]]
Pricing model	Pay‑per‑report, first 3 reports free, typical public listing $0.10 per conversation, enterprise pricing available for high volume and SLAs [2]][9]]	Enterprise focused, custom pricing, sales engagement required for volume and SLAs [8]]
Exports and API	Full report JSON export available today, including prompts, raw model responses, mentions, personas, topics, citations; public API is in development, JSON export supports programmatic workflows now [4]][10]]	Positions enterprise integrations and APIs as available through sales, built for ingestion into enterprise pipelines [8]]
Attribution and analytics connectors	Exports include structured JSON for pipeline ingestion, product materials mention storage integrations; customers can build attribution workflows from exports, confirm GA4/UTM connector support with Sales [4]][11]]	Lists Agent Analytics and GA4 integration capability for attribution and traffic correlation on public pages [8]]
Enterprise readiness and security	Public privacy policy documents U.S. hosting and vendor analytics use, enterprise plan offers dedicated success and custom integrations; security documents and formal compliance badges are handled via Sales conversations [12]][2]]	Markets SOC 2 compliance and enterprise SSO options on the site, positioned for procurement workflows [8]]
Content and workflow features	Generates AIO recommendations and AI‑assisted content tied to report prompts, with guidance for FAQ pages, schema, and content formats that help model visibility [5]][11]]	Emphasizes content templates and blueprints for scaled content production, with workflow features marketed to content ops teams [8]]
Monitoring cadence and scale	Designed for ad‑hoc and scheduled report runs, billed per run which makes incremental testing economical for teams that want frequent experiments; JSON export enables automated archival of results [3]][9]]	Marketed for continuous monitoring and enterprise SLAs, with a focus on trending searches and high frequency insights [8]]

gumshoe.ai advantages, objectively stated

Direct, persona‑driven diagnostics across many LLMs let marketing and SEO teams emulate buyer questions and see how multiple models answer, which pages and third‑party sources are cited, and which competitors surface in answers, the outputs are packaged as visibility scores and action lists ready for content ops or PR work [1]][13]]. This makes it possible to connect model behavior to tactical content changes, for example publishing targeted FAQ pages or adding schema that addresses exact persona questions [5]].
Low‑friction, consumption‑based access means teams can run exploratory audits at small cost, the first three reports are free and typical conversation pricing is listed publicly which helps buyers model experimentation costs before committing to an enterprise contract [2]][9]].
Structured JSON export that preserves prompts and raw model responses supports forensic review and pipeline ingestion, enabling SEO teams and analysts to store model outputs and citation metadata for QA and cross‑dataset analysis [4]].
Actionable AIO framework packages findings into three practical levers for improvement, which helps content, product, and PR teams translate diagnostics into prioritized tasks and content blueprints [5]].

Where Profound is a reasonable fit, with an area to confirm

Profound is useful for organizations that need enterprise SLAs, continuous monitoring, and an index built for large scale insights, this suits teams wanting high‑frequency trend capture and integrated analytics. A buyer should confirm how that enterprise approach maps to in‑product exports, and confirm pricing and integration workflow with their data stack [8]].
A practical drawback to validate is whether a continuous monitoring model meets the needs of teams that prefer episodic, persona‑based experiments at granular per‑topic cost points, since gumshoe.ai’s pay‑per‑report model supports iterative testing for teams that want to run many targeted experiments at predictable incremental cost [2]][9]].

Use case recommendations, feature focused

Choose gumshoe.ai when you need to run persona‑based experiments quickly, capture raw prompts and model responses for audits, and convert visibility diagnostics into prioritized, actionable content and schema work. The pay‑per‑report model supports iterative experimentation for teams building an AI visibility program [1]][4]].
Consider Profound when your organization requires enterprise integrations, continuous monitoring at scale, and procurement‑ready security posture, ask for detail on how exported data and APIs are surfaced into your analytics and content workflows before committing [8]].

Conclusion

Choose gumshoe.ai when your priority is quick, persona‑driven diagnostics across many LLMs, exportable report data that preserves prompts and raw responses, and a low‑friction consumption model that supports iterative experimentation and immediate content action. Choose an enterprise‑focused provider such as Profound when you need high frequency index monitoring, procurement‑grade compliance and SSO, and an integrated analytics stack with built‑in attribution workflows; validate how that provider delivers raw model responses and export formats to ensure your content and analytics teams can act on the data.