Cloro

What is cloro?

cloro is an AI search and search-engine data API designed for developers, marketing teams, agencies, and analytics organizations that want programmatic access to AI-generated answers, citations, source URLs, positions, and other search data.

Unlike many AI visibility products that provide a hosted dashboard with predefined metrics, cloro focuses on the underlying data layer. Teams query AI search engines through a unified API, receive structured results, store those results in their own systems, and define their own visibility metrics and reporting methodology.

This makes cloro relevant to organizations building custom AI visibility tracking, competitive intelligence, citation monitoring, white-label reporting, data warehouses, and AI search products.

What does cloro do?

cloro sends prompts to supported AI search engines and returns structured information about the generated answer and the sources associated with it.

Teams can use the resulting data to calculate metrics such as mention rate, citation rate, Share of Voice, citation position, and cross-engine coverage.

Instead of requiring separate integrations for each AI search provider, cloro provides a shared authentication model and a consistent response structure across multiple engines.

What are the key features of cloro?

cloro combines AI search retrieval, citation parsing, regional targeting, and structured data delivery through a unified API.

• Unified AI Search API: Provides access to multiple AI search engines through one API and a consistent response structure.
• AI Visibility Tracking: Supplies the raw response data needed to calculate brand visibility metrics over time.
• Source and Citation Parsing: Extracts cited domains, URLs, labels, descriptions, and source positions from AI-generated answers.
• Mention Rate: Allows teams to calculate how frequently a brand appears across a fixed set of monitored responses.
• Citation Rate: Allows organizations to measure how frequently their domain is returned as an AI source.
• Share of Voice: Enables competitive visibility calculations across the same prompts and engines.
• Citation Position: Provides information about where sources appear within an AI engine's cited-source list.
• Cross-Engine Coverage: Helps teams measure how consistently a brand appears across multiple AI search platforms.
• Regional Targeting: Supports country-level sampling for comparing AI search performance across markets.
• Webhooks: Supports asynchronous and scheduled workflows for larger monitoring programs.
• Warehouse Integration: Structured outputs can be stored in systems such as BigQuery and analyzed with custom BI workflows.
• White-Label Infrastructure: Agencies can build client-facing AI visibility products and reports on top of cloro's API.
• SERP Data: Extends beyond AI visibility into traditional Google search, Google News, local search, and related search-data workflows.

Which AI platforms does cloro support?

cloro provides structured AI search data across major consumer-facing AI engines.

Its current AI visibility materials reference support for:

• ChatGPT.
• Perplexity.
• Gemini.
• Google AI Overviews.
• Google AI Mode.
• Microsoft Copilot.
• Grok.

These engines are available through the same API environment, allowing organizations to expand monitoring across platforms without building an entirely separate integration for each provider.

cloro also provides traditional search endpoints, including Google Search and Google News, making it possible to combine conventional search and AI search data within the same data infrastructure.

How does AI visibility tracking work with cloro?

cloro does not require organizations to use a predefined AI Visibility Score.

Instead, teams select a stable set of prompts, query supported AI engines, collect the resulting answer text and source data, and calculate their own metrics over repeated runs.

A typical monitoring workflow can include:

• Select a fixed set of relevant prompts.
• Run those prompts across multiple AI engines.
• Store the generated responses and citations.
• Detect brand and competitor mentions.
• Calculate citation and mention rates.
• Compare performance across engines.
• Repeat the process on a recurring schedule.
• Track changes rather than relying on one response.

This makes cloro a measurement infrastructure rather than a fixed reporting methodology.

What AI visibility metrics can be calculated with cloro?

Because cloro returns raw AI responses and structured source information, teams can calculate multiple AI visibility metrics according to their own methodology.

Common measurements include:

• Mention Rate: The percentage of monitored responses in which the tracked brand appears.
• Citation Rate: The percentage of responses that cite or link to the tracked domain.
• Share of Voice: The tracked brand's visibility relative to selected competitors across the same prompt set.
• Citation Position: Where a brand or domain appears within the source list associated with an AI response.
• Cross-Engine Coverage: The number of AI engines that surface or cite a brand for a particular prompt or topic.
• Entity Recognition: Whether an AI engine correctly associates claims or information with the intended brand.

Teams can also create custom weighting systems if their internal reporting methodology gives greater importance to certain engines, markets, topics, or prompt types.

How does cloro track citations?

cloro parses source information associated with AI-generated answers and returns it as structured data.

Citation records can include:

• Source URL.
• Source position.
• Source label.
• Source description.
• Engine-specific metadata.

This allows organizations to identify which websites AI systems rely on when answering important questions.

Teams can use citation data to compare owned pages, competitor domains, and third-party sources and to investigate which websites repeatedly influence AI-generated recommendations.

What is the difference between mention rate and citation rate in cloro?

Mention rate and citation rate describe different types of AI visibility.

Mention rate measures how frequently an AI-generated answer names or references the tracked brand.

Citation rate measures how frequently the brand's domain appears as a source or clickable reference within AI-generated responses.

A brand may therefore receive strong mention visibility without receiving many citations, or its website may influence AI-generated answers without the brand being prominently named.

Tracking both metrics can help organizations distinguish brand awareness from source-level authority and referral potential.

How does cloro support Share of Voice analysis?

cloro provides the raw answer and source data required to calculate competitive Share of Voice across AI search.

Teams define a set of competitors and evaluate how frequently each brand appears across the same prompts and AI engines.

This can reveal:

• Which competitors appear most frequently.
• Which engines favor particular brands.
• Where the tracked brand loses visibility.
• Which competitor domains receive more citations.
• How competitive visibility changes over time.

Because Share of Voice is calculated from raw results, organizations can apply their own weighting rules instead of relying on a vendor-defined formula.

Why does cloro recommend repeated AI search sampling?

AI-generated answers are non-deterministic, which means that the same prompt can produce different answers and citations across separate executions.

For that reason, cloro emphasizes repeated sampling rather than treating one answer as a stable ranking result.

A monitoring program can run the same prompt set weekly or daily and aggregate the results to create trends over time.

This helps reduce the risk of drawing conclusions from one unusually favorable or unfavorable AI-generated answer.

How does regional AI visibility tracking work in cloro?

cloro supports country-level targeting across its AI search endpoints.

Organizations can run the same prompt in different markets and compare how AI answers, sources, citations, and competitors change by country.

This can be useful for:

• International brands.
• Regional marketing teams.
• Multi-country SEO and GEO programs.
• Agencies serving clients across different markets.
• Products whose recommendations differ geographically.

Because regional targeting uses a shared API parameter, teams can expand geographic monitoring without maintaining separate provider integrations.

How can teams build an AI visibility dashboard with cloro?

cloro provides the measurement layer rather than requiring organizations to use a specific dashboard.

Teams can send monitored responses into their own data environment and build reporting according to internal requirements.

A typical architecture can include:

• cloro for AI search collection.
• BigQuery or another warehouse for storage.
• dbt or SQL for metric calculation.
• Looker Studio or another BI platform for visualization.
• Slack or another messaging platform for alerts.

This approach can be particularly useful for organizations that already maintain centralized marketing or search analytics infrastructure.

How can agencies use cloro?

Agencies can use cloro as the infrastructure behind their own AI visibility reporting products.

Instead of giving clients access to a third-party AI visibility dashboard, an agency can collect data through cloro, calculate metrics using its own methodology, and present results through agency-branded reports or dashboards.

Potential agency workflows include:

• White-label AI visibility dashboards.
• Weekly client reporting.
• Competitive Share of Voice monitoring.
• Citation-loss alerts.
• Market-specific reporting.
• Before-and-after GEO measurement.

This gives agencies greater control over the client experience, data model, and methodology.

How does cloro support GEO measurement?

Generative Engine Optimization requires teams to understand whether changes to content, authority, or distribution actually affect AI-generated answers.

cloro can support this measurement by running the same prompt set before and after an optimization initiative and comparing the resulting mentions and citations.

For example, teams can measure:

• Whether a page begins receiving citations.
• Whether mention rate increases.
• Whether citation position improves.
• Whether competitor visibility decreases.
• Whether results improve across several AI engines rather than only one.

This provides a quantitative feedback loop for GEO experiments.

How is cloro different from hosted AI visibility platforms?

Many AI visibility platforms combine data collection, metric calculation, dashboards, and workflow tools within one hosted product.

cloro takes a different approach by focusing on the data infrastructure underneath those workflows.

Instead of providing a fixed dashboard and methodology, cloro provides:

• Raw AI-generated answers.
• Structured citations.
• Source positions.
• Engine-specific results.
• Regional targeting.
• API access.

Organizations then decide how the data should be stored, scored, visualized, and combined with other marketing systems.

This can provide more flexibility but also requires more technical implementation than using a fully packaged AI visibility dashboard.

How does cloro fit into AI SEO, AEO, and GEO?

cloro operates as a measurement and infrastructure layer within AI SEO, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO).

It does not primarily provide content creation or optimization recommendations. Instead, it gives teams structured evidence about how AI search systems actually respond to prompts and which sources they use.

That data can support workflows such as:

• AI visibility measurement.
• Prompt monitoring.
• Citation intelligence.
• Competitive benchmarking.
• GEO experimentation.
• AI search reporting.
• Custom AI visibility products.

This makes cloro particularly relevant to organizations that want to build their own AI search intelligence layer rather than rely entirely on a packaged monitoring platform.

How is cloro different from traditional SEO APIs?

Traditional SEO APIs primarily return information about rankings, search results, keywords, ads, local results, or backlinks.

cloro extends this model into AI-generated search experiences.

Teams can investigate questions such as:

• Does ChatGPT mention our brand?
• Which sources does Perplexity cite?
• Which competitor appears most frequently?
• How does Gemini visibility differ from ChatGPT?
• Which domains influence AI answers?
• How does visibility change between countries?
• Did a GEO optimization improve citations?

Because cloro also provides traditional search data, teams can use the same underlying infrastructure for both conventional SERP tracking and AI search measurement.

Who is cloro for?

cloro is designed primarily for teams that want programmatic control over search and AI visibility data.

Potential users include:

• Developers.
• SEO engineering teams.
• AI Search and GEO teams.
• Marketing analytics teams.
• Business intelligence teams.
• Agencies.
• Enterprise marketing organizations.
• AI visibility software companies.
• Search intelligence platforms.
• Teams building custom dashboards or internal tools.

The API-first model can be particularly useful when raw data ownership, custom metrics, warehouse integration, or white-label reporting are important requirements.

What should teams consider when evaluating cloro?

Organizations evaluating cloro should first decide whether they want a ready-made AI visibility application or the infrastructure for building their own measurement system.

Important considerations include:

• Engineering resources.
• Required AI engine coverage.
• Monitoring frequency.
• Prompt volume.
• Regional requirements.
• Warehouse and BI infrastructure.
• Custom metric requirements.
• White-label reporting needs.
• Data ownership requirements.

Teams that want a fully packaged interface with built-in recommendations and workflow management may prefer a hosted AI visibility platform.

Teams that want raw search data, custom metrics, and full control over the analytics layer may find an API-first approach more appropriate.

cloro and the AI Search tools ecosystem

cloro occupies a different layer of the AI search tools ecosystem from many dedicated AI visibility dashboards.

Its focus is the collection and normalization layer: retrieving AI-generated answers, extracting sources and citations, and returning the resulting information through a common API.

This infrastructure can then power internal dashboards, agency reporting, competitive monitoring, GEO experiments, or other AI search products.

The broader ecosystem also includes packaged AI visibility platforms, prompt analytics products, citation intelligence tools, content optimization systems, AI traffic analytics platforms, and established SEO products expanding into AI search.

Ansvisor maintains a broader directory of AI SEO, AEO, GEO, AI visibility, and AI search tools to help teams understand these different layers and evaluate platforms according to their specific requirements.