Brandlight review: pricing, features & alternatives

Brandlight (founded October 2024 by Imri Marcus, Uri Gafni, and Didi Dvash; NY HQ, Tel Aviv roots) sells an enterprise AI-visibility operating system: it queries the major AI engines from many viewpoints, then reports how a brand appears — mentions, sentiment, bias, which sources engines cite — across every brand, region, and language a global company manages. SOC 2 Type II, SSO, and RBAC make it procurement-ready, and a February 2026 $30M Series A (Pelion Venture Partners) is funding an expansion into tracking AI-native ads and commerce recommendations.

The customer list is the strongest evidence: Kimberly-Clark, LG, The Hartford, and Estée Lauder are named in funding materials, with Volkswagen Group and Caesars appearing in third-party datasets.

For a company claiming hundreds of top brands, the independent evidence is remarkably thin: roughly 19 G2 reviews (4.7/5), zero on Capterra, TrustRadius, or Trustpilot. The homepage's '#1 AEO platform globally' claim names no analyst or report — it's echoed only by directories quoting Brandlight's own copy, while G2's Winter 2026 grid reportedly crowns Profound instead. And pricing is fully opaque: the pricing URL has returned a 404, and third-party estimates span $199/mo entry (plus setup fees) to $4,000–15,000/mo for enterprise scope.

The substantive product criticisms are consistent: Brandlight diagnoses without executing ('clear direction on where we can improve, but our internal bandwidth is limited,' as one G2 VP put it), and revenue attribution was still listed as coming soon in early 2026 — so the dashboards end where the work begins.

Global consumer and B2B enterprises that want a managed, compliant AI-visibility layer with strategist support — and can negotiate scope, price, and proof-of-value through procurement.

Mid-market and smaller teams are structurally excluded: no self-serve, no trial, no published price. The self-serve trackers in this list deliver the core measurement for 1–5% of the estimated cost.

Every row below is a capability we already listed for Brandlight. It is the published feature set for this page, not a guess at an unpublished demo. Compare it with the Promptwatch checklist that follows.

On this listing, Brandlight is a Enterprise GEO platform for Fortune 500 marketing teams that want white-glove AI visibility intelligence and have procurement to negotiate opaque pricing.. What we sourced here: Mention, sentiment, and citation tracking across major AI engines; Multi-brand, multi-region command center; SOC 2 Type II, SSO, role-based access; Expanding into AI ads and AI commerce tracking after Series A.

If you buy Brandlight for the job above, plan a second tool (or Promptwatch) for the layers this listing does not document. Still missing here: AI crawler logs (Agent Analytics), crawl-to-citation path, bot errors; Visitor analytics: AI-referred sessions and conversions; Content Agents that plan, write, and publish GEO pages to a CMS with a review inbox; Citation analytics at page, domain, Reddit, YouTube, and offsite level; Prompt volumes, difficulty, query fan-outs, personas, city-level geo; Prompt and citation trends (what changed between checks); ChatGPT Shopping tracking and Ads Radar; Agent Chat, Unified Actions, MCP server / REST API on paid plans. Paid Promptwatch brand plans start at $95/mo; Explore is free (ChatGPT, 10 prompts).

Brandlight's public pages (and this listing) describe a narrower job than Promptwatch. Most rivals answer whether you were mentioned. Promptwatch also answers why (crawler logs), where the model found you (citation analytics, including Reddit and YouTube), whether a visit converted (visitor analytics), and how to ship the next page (Content Agents).

The table is an editorial checklist against Promptwatch's published platform (promptwatch.com/llms.txt, 2026-08-23). It is not a hidden feature list for this vendor. 'Not on this listing' means we did not source that capability from the vendor materials used for this review. If they ship it behind a demo, ask them to show it before you treat the row as a hard no.