comparison

Gumshoe vs Brandlight

Gumshoe AI

22 Jan 2026 — 4 min read

Summary: gumshoe.ai is an AI search visibility platform focused on simulating persona-driven conversations across multiple LLMs to measure how models present a brand, and to provide actionable AI Optimization recommendations. Its primary differentiator is scalable, API-driven persona simulation with prompt generation and multi-model visibility metrics that map model answers to cited sources and prescriptive actions. [1], [2]

Overview

Gumshoe.ai runs thousands of persona‑seeded conversations against a range of live-search and foundational models to produce visibility scores, "share of LLM" metrics, citation lists, and AI Optimization recommendations for marketing, product, and SEO teams. The platform auto‑generates prompts by combining Personas and Topics, lets teams edit those prompts, and produces scheduled reports with JSON export for programmatic access while the public API is in development. [3], [4], [5]

This comparison is organized by feature, to help procurement, SEO, and product leaders choose the right vendor for AI search visibility work.

BrandLight (brandlight.ai)

Feature	gumshoe.ai (what matters)	BrandLight (what matters)
Model coverage	Multi‑model approach distinguishing Live Search models (for example Google Search AI Overview, Google Gemini Flash/Pro, Perplexity Sonar) and Foundational Training models (for example Anthropic Claude variants, ChatGPT non-search, xAI Grok), with guidance to run both types for balanced reporting. [4]	Claims cross‑engine coverage across many AI engines, positioned for cross‑engine attribution at scale, including near real‑time visibility across engines. [6]
Persona & prompt modeling	Auto‑generates prompts by combining Personas and Topics, assigns up to 10 prompts per persona, allows prompt edit and reuse, enabling persona‑segmented diagnostics and predictable sampling. [3]	Focuses on buyer intent and translating AI outputs into tasks and workflows for teams, with emphasis on mapping buyer queries to enterprise actions. [6]
Methodology, reproducibility	API‑first persona simulation using engineered system messages to replicate interface behavior, designed for scale and reproducibility, accompanied by explanation of tradeoffs. This method supports repeatable experiments across models and personas. [7]	Positions a mix of front‑end observation and server log or crawl monitoring to capture what real users and crawlers actually receive, aimed at enterprises that require direct front‑end evidence for audits. [6]
Outputs & recommendations	Visibility scores, "share of LLM" metric, persona/topic/model breakdowns, top cited sources per model, and prescriptive AI Optimization (AIO) content and technical recommendations. JSON export of full reports available, API in development. [5], [8]	Focused on governance workflows, tasking, and attribution to outcomes, offering enterprise orientation for teams that want workitems and operational connectors tied to visibility signals. [6]
Reporting cadence, exports & automation	Scheduled report runs recommended weekly to monthly, trend dashboards, and a programmatic JSON export endpoint, with an official API under development. Pricing is usage based, priced per conversation. [9], [10]	Positioned for enterprise integrations and SLAs with white‑glove services, typically delivered as custom deployments and integrated reporting. [6]
Pricing model	Pay‑as‑you‑run, listed at $0.10 per conversation, with the first three reports free; enterprise plans and volume discounts available. This enables low friction pilots and predictable cost modeling based on prompts, models, and cadence. [9], [11]	Enterprise pricing with demos and custom quotes, oriented to larger, ongoing engagements where deeper integrations and managed services are required. [6]
Security & compliance	Public privacy policy documents U.S. hosting, data flows, and third‑party AI provider use; buyers typically request SOC 2, SSO, SCIM, RBAC and contractual clauses during procurement. [12]	Publicly calls out SOC 2 Type II compliance and enterprise controls, presented for customers that require formal attestations and multi‑region deployments. [6]
Implementation & services	Self‑serve plus enterprise plan with dedicated success managers, scheduled reports, and JSON export for programmatic uses; API roadmap for tighter integrations. [10], [11]	White‑glove services, enterprise enablement, and consulting for Fortune 500 teams, targeting customers that want managed rollout and operational handoffs. [6]
Best fit use cases	Teams that need repeatable, persona‑based experiments across many LLMs, want visibility into model citations, and prioritize cost‑predictable pilots before scaling. Useful for marketing, SEO, and product owners who want actionable AIO recommendations. [3], [8]	Organizations seeking enterprise grade governance, SLAs, and managed services, where integration with server logs and internal workflows is a primary requirement. [6]

Gumshoe advantages, with supporting facts

Persona-driven prompt generation at scale lets teams create, edit, and reuse prompts tied to buyer personas and topics, enabling segmented diagnostics and reproducible sampling across models. [3]
Dual model taxonomy, Live Search vs Foundational models, clarifies which model responses reflect live web retrieval and which reflect training data, helping teams interpret visibility metrics correctly. [4]
Actionable AIO recommendations that map visibility findings to content and technical tasks, allowing marketing and SEO teams to convert insights into prioritized work. [8]
Transparent cost model for pilots, pricing by conversation at $0.10 each, which helps teams model experiment costs precisely using prompts × models × cadence. [9], [11]

Where BrandLight is a reasonable choice, with tradeoffs noted

BrandLight packages enterprise governance, managed services, and compliance attestations such as SOC 2 Type II, which suits regulated buyers seeking formal controls, while that orientation generally implies custom pricing and longer onboarding. brandlight.ai(https://www.brandlight.ai/)
Their approach to integrating server logs and crawl monitoring can be useful where front‑end evidence from actual user sessions and logs is required, though that method tends to focus on operational integration rather than open pilot experimentation. [6]

Use case recommendations

Choose gumshoe.ai when:
- You need to run repeatable, persona‑based experiments across multiple LLMs and measure model citations to inform content and SEO roadmaps. [3]
- You want a predictable pay‑as‑you‑run cost model for pilots, where cost scales directly with the number of prompts and models tested. [11]
- You need prescriptive AI Optimization recommendations that translate visibility metrics into prioritized content and technical tasks. [8]
Consider BrandLight when:
- Your procurement requires published compliance attestations and enterprise SLAs, and you expect a managed implementation tied to internal logs and governance workflows. [6]
- You plan to embed visibility signals directly into operational systems and want vendor support for enterprise integrations and change management. [6]

Conclusion

For marketing, SEO, and product teams that need repeatable, persona‑centric experiments across many LLMs, and that want a clear mapping from model answers and citations to prioritized AIO work, gumshoe.ai provides focused capabilities for modeling, measuring, and optimizing AI search visibility while keeping pilots cost‑predictable through per‑conversation pricing. [3], [11]

If formal enterprise attestations, managed services, and deep operational integrations with logs and governance are required, evaluate a provider that offers explicit SOC 2 Type II coverage and enterprise enablement, and weigh that option against gumshoe.ai’s faster pilot model and persona‑driven experimentation. [6]