Lumos - 2026 Pricing, Features, Reviews & Alternatives

Lumos is a generative engine optimization platform designed to enable brands to monitor, measure, and improve visibility across AI-powered search interfaces such as ChatGPT, Gemini, and Claude. It serves business-to-business companies, growth teams, marketing departments, and search engine optimization professionals in Latin American markets and adapts to organizations seeking influence in conversational search interactions. The platform addresses a challenge in brand discoverability within large language model environments where traditional optimization strategies do not apply.

The platform operates through a continuous four-stage visibility loop that monitors buyer-intent prompts, diagnoses ranking gaps, generates corrective content, and tracks implementation results. It executes more than eighty buyer-intent prompts daily across multiple AI models and tracks metrics that include share of voice, average position, sentiment analysis, and citation frequency. The aggregate visibility score appears as a single metric that represents how often AI models recommend a brand for unbranded category queries. The diagnostic engine identifies competitor brands that outrank the organization, the third-party sources AI models cite, and the content deficiencies that cause lower visibility. The system generates a prioritized action backlog with AI-drafted content including articles, frequently asked question sections, schema markup, and page edits, and each item is assigned an expected impact, a difficulty rating, and an owner. Users receive weekly executive reports limited to six pages that present conclusions first followed by supporting evidence.

The platform includes a brand kit and fact-verification system that maintains canonical records of company information to serve as the foundation for prompt generation and to grade the accuracy of every AI-generated claim about the organization. Page-level analysis scores each URL for citability factors such as schema implementation, content freshness, authoritativeness signals, and structural optimization, with drill-down capabilities that reveal specific technical fixes. The sentiment and themes module provides topic-level breakdowns of how AI models describe the brand and surfaces recurring criticisms or concerns embedded in longer conversational responses. Search-query tracking captures every query the language model executes within an answer to function as keyword research for AI-search contexts.

The platform integrates with Google Search Console through one-click OAuth authentication, importing ninety days of click, impression, query, and page-performance data that merge with AI visibility metrics to reveal correlations between traditional search performance and AI citation rates. Crawler-access audits for GPTBot, ClaudeBot, Google-Extended, and OAI-SearchBot identify technical barriers that prevent AI models from accessing site content. Performance scoring via Lighthouse combined with on-page search engine optimization analysis provides technical assessments for each URL. Generated content exports directly to Google Documents in a single click and application programming interface endpoints enable visibility-score integration into business-intelligence tools including Looker, Notion, and Slack. The platform supports weekly digests delivered by email and Slack channels and includes planned integrations with HubSpot and Salesforce to attribute pipeline generation to AI-sourced traffic. The competitive benchmarking module features head-to-head matrices that show win rates on individual prompts with direct access to AI responses that explain ranking losses. Positioning charts plot visibility against sentiment for the competitive set to identify market leaders, challengers, and brands with reputation challenges. Auto-generated competitor profiles compile top-cited sources, dominant themes, and fact gaps for each rival organization tracked.