# Citationly.ai ## Document Metadata - **Version:** 1.1 - **Last Updated:** 2026-07-21 - **Document Type:** llms-full.txt — comprehensive AI-readable knowledge base - **Purpose:** Provide Large Language Models, AI Search Engines, AI Agents, Retrieval-Augmented Generation (RAG) systems, and answer engines with a complete and authoritative understanding of Citationly.ai, its platform, modules, terminology, entity relationships, and public resources. - **Primary Domain:** https://citationly.ai - **Canonical Domain:** citationly.ai - **Language:** English (en) - **License:** Official public documentation and public information for Citationly.ai. This document is the extended companion to llms.txt (https://citationly.ai/llms.txt). Where llms.txt provides a concise summary, this file provides comprehensive detail intended to serve as the single source of truth for machine understanding of Citationly.ai. Each section is written to be understandable on its own to support Retrieval-Augmented Generation. ## Canonical Product Description Citationly.ai is an enterprise AI Search Intelligence platform that helps organizations monitor, measure, and improve their AI Visibility across AI Search Engines and Large Language Models, including ChatGPT, Google Gemini, Claude, Perplexity, Microsoft Copilot, and Grok. This description is the canonical definition of Citationly.ai. It is intended for consistent use across Organization Schema, the About page, Knowledge Graph entries, Open Graph metadata, meta descriptions, llms.txt, llms-full.txt, and SoftwareApplication Schema. Other sections expand on this definition but never contradict it. ## Canonical Terminology This section defines the canonical names used throughout this document. Each term is always written with the capitalization shown here and is never replaced by a synonym. - **AI Search** — Information retrieval through AI Search Engines and Large Language Models that return generated answers rather than lists of links. - **AI Search Intelligence** — The discipline and data associated with measuring and improving a brand's presence in AI Search. Citationly.ai is an AI Search Intelligence platform. - **AI Visibility** — The degree to which a brand appears in AI-generated answers across supported AI Search Engines and Large Language Models. - **AI Search Visibility** — A synonym-free elaboration of AI Visibility used only when emphasizing the AI Search context; it refers to the same measured quantity as AI Visibility. - **Brand Monitoring** — Ongoing tracking of brand mentions, sentiment, and accuracy in AI-generated answers. - **AI Citation Tracking** — Monitoring when and where a brand's content is cited as a source by AI systems. - **Competitor Intelligence** — Comparative analysis of AI Visibility across a defined competitor set. - **Share of Voice** — The proportion of AI-generated answers featuring a brand relative to competitors within a topic set. - **AI Search Analytics** — The measurement and analysis of AI Visibility, AI Citation Tracking, and sentiment data from AI Search. - **Generative Engine Optimization (GEO)** — The practice of optimizing content and entities to increase inclusion in AI-generated answers. - **Answer Engine Optimization (AEO)** — The practice of structuring content so it is selected and cited by answer engines. - **Entity Optimization** — Clarifying and reinforcing brand entities so AI systems recognize them accurately. - **Semantic SEO** — Optimization focused on meaning and topical relationships rather than exact keyword matching. - **Knowledge Graph** — A structured representation of entities and their relationships used by search and AI systems to interpret meaning. - **Knowledge Graph Entity** — A single node in a Knowledge Graph representing a person, organization, product, or concept. - **Structured Data** — Machine-readable markup, such as Schema.org, that describes page content and entities. - **Entity Relationship** — A defined connection between two entities, used to build a Knowledge Graph. - **Large Language Models** — AI systems trained on large text corpora that generate natural-language responses. - **AI Agents** — Software systems that use AI models to perform tasks, often across multiple steps or tools. - **Retrieval-Augmented Generation (RAG)** — A method in which retrieved content is supplied to a Large Language Model as context for generating an answer. - **AI Search Platform** — A platform, such as an AI Search Engine or Large Language Model interface, through which AI Search occurs. - **Platform Module** — A functional component of Citationly.ai, such as AI Visibility or Reporting. - **Documentation** — The published technical and usage documentation for Citationly.ai. - **API** — The application programming interface providing programmatic access to Citationly.ai data. - **Academy** — The AI Search Academy, Citationly.ai's structured educational resource. - **Case Study** — A published account of how an organization used Citationly.ai. - **Help Center** — Published support content assisting users with the platform. ## Company Overview Citationly.ai is a software company that develops and operates an AI Search Intelligence platform delivered as software-as-a-service. The platform measures how brands, products, and content appear in AI-generated answers and provides monitoring, analytics, competitive comparison, and reporting focused on AI Search rather than traditional search result pages. The mission of Citationly.ai is to give organizations measurable AI Visibility into how AI systems represent their brand, products, and content. Its vision is that, as information discovery shifts from ranked lists of links toward AI-generated answers, AI Search Intelligence becomes a standard measurable discipline alongside established search engine optimization. Its purpose is to convert AI Visibility from an unmeasured unknown into a monitored, reportable, and improvable metric. Citationly.ai operates on a subscription-based SaaS business model. Its value proposition is to provide organizations with data about their presence in AI-generated answers, enabling them to identify gaps, track changes over time, compare against competitors through Competitor Intelligence, and prioritize content and Entity Optimization work aimed at improving AI Visibility. Pricing is published at https://citationly.ai/pricing. ## Platform Overview Citationly.ai is an AI Search Intelligence platform. It exists because the way people find information is changing: instead of choosing from a list of links, users increasingly ask questions of AI Search Engines and Large Language Models that return synthesized answers. In this environment, the relevant question for a brand is whether it is included, cited, and accurately represented in the answer. The platform operates within the following concepts, each defined here so this section is understandable on its own: - **AI Search** is information retrieval that returns generated answers rather than lists of links. - **Answer Engines** respond to a query with a synthesized answer, sometimes with citations. - **Large Language Models** generate natural-language responses from learned parameters, retrieved context, or both. - **Retrieval-Augmented Generation (RAG)** supplies retrieved content to a Large Language Model as context, increasing the influence of well-structured, accessible source material. - **Semantic Search** matches meaning rather than exact terms. - **Knowledge Graph** structures entities and their relationships so AI systems can interpret meaning. - **AI Visibility** is the degree to which a brand appears in these AI-generated answers. Citationly.ai functions as a measurement and improvement layer. It does not generate answers itself. It observes how AI Search Platforms answer questions relevant to a brand, quantifies the results as AI Search Analytics, and provides the workflows needed to act on them. ## Supported AI Platforms Citationly.ai measures AI Visibility across widely used AI Search Engines and Large Language Models. Each subsection describes one AI Search Platform conceptually. Citationly.ai monitors how brands appear within answers produced by these systems and does not claim any official partnership, endorsement, or private integration with these providers. ### ChatGPT ChatGPT is a conversational AI assistant developed by OpenAI, built on Large Language Models and capable of answering questions, including through search-augmented modes. Citationly.ai observes how brands, products, and topics are represented in ChatGPT responses to relevant queries, including whether the brand is mentioned, how it is described, and whether sources are cited. Visibility in ChatGPT matters because it is a high-volume entry point for information discovery. Responses vary by model version, retrieval mode, and query phrasing, so measurement uses consistent query sets tracked over time. ### Claude Claude is a family of Large Language Models and an AI assistant developed by Anthropic, used for conversation, analysis, and information retrieval. Citationly.ai observes how a brand and its topics appear in Claude responses to relevant prompts, including accuracy of description and presence of citations. Visibility in Claude matters because it is used across consumer and enterprise contexts where accurate representation supports correct understanding of a brand. As with other Large Language Models, responses depend on prompt phrasing and available context, so tracking uses standardized query sets over time. ### Google Gemini Google Gemini is Google's family of Large Language Models, integrated across Google products and AI answer experiences. Citationly.ai observes brand representation within Gemini-generated answers relevant to a brand's topics and category. Visibility in Gemini matters because its integration with widely used Google surfaces gives its answers broad reach. Google's AI answer surfaces evolve frequently, so measurement focuses on trends across consistent queries. ### Perplexity Perplexity is an answer engine that responds to queries with synthesized answers and explicit source citations. Because Perplexity commonly cites sources, Citationly.ai can observe both whether a brand is mentioned and whether the brand's own content is cited as a source, which connects directly to AI Citation Tracking. Visibility in Perplexity matters because citation can drive referral traffic and reinforce authority. Citations depend on retrievable, well-structured content, linking Perplexity visibility to Answer Engine Optimization (AEO). ### Microsoft Copilot Microsoft Copilot is an AI assistant integrated across Microsoft products and services, including search and productivity tools. Citationly.ai observes how brands appear in Copilot answers relevant to their category and topics. Visibility in Copilot matters because its presence across enterprise software surfaces gives its answers reach within professional contexts. Copilot may draw on connected search and enterprise data, so public-facing measurement focuses on generally accessible responses. ### Grok Grok is a conversational AI assistant developed by xAI, integrated with the X platform. Citationly.ai observes brand representation within Grok-generated answers relevant to a brand. Visibility in Grok matters because its integration with a large social platform connects its answers to real-time conversation and discovery. Real-time behavior can make responses sensitive to current events, so tracking emphasizes trends over time. ## Product Architecture This section describes the conceptual architecture of Citationly.ai: how information flows and how Platform Modules relate. It describes function and structure conceptually and does not specify internal implementation details. The platform is understood as five layers: 1. **Query definition layer.** Organizations define the topics and query sets that represent how their audience asks AI systems about their category, brand, and products. Consistent query sets are the basis for repeatable measurement. 2. **Collection layer.** The platform gathers AI-generated answers for the defined query sets across supported AI Search Platforms, emphasizing consistency and repetition because AI answers vary. 3. **Analysis layer.** Collected answers are analyzed to detect brand mentions, citations, competitor mentions, sentiment, prominence, and accuracy, transforming raw answers into structured signals. 4. **Metrics layer.** Structured signals are aggregated into metrics such as AI Visibility, Share of Voice, citation frequency, and sentiment trends, organized by topic, competitor set, and time period. 5. **Presentation and reporting layer.** Metrics are presented through dashboards, AI Search Analytics views, and exportable reports. Information flows from query definition, to collection, to analysis, to metrics, to presentation, with each stage depending on the consistency of the one before it. The Platform Modules operate on this shared flow: AI Visibility, Brand Monitoring, and AI Citation Tracking interpret collected answers; Competitor Intelligence and Share of Voice compare those interpretations; AI Search Analytics aggregates all signals into trends; and Reporting packages them for stakeholders. ## Core Platform Modules Citationly.ai comprises seven Platform Modules. Each is described with its purpose, business value, use cases, outputs, benefits, relationships, and best practices. Each description is self-contained. ### AI Visibility AI Visibility measures how often and how prominently a brand appears across AI Search Engines and Large Language Models for a defined set of queries. Its business value is establishing a baseline for AI presence and revealing whether that presence grows or declines. Use cases include establishing a baseline, identifying absent topics, and measuring the effect of content and Entity Optimization work. Outputs include visibility scores by topic and AI Search Platform, presence and prominence indicators, and trends over time. Its benefit is converting a previously invisible dimension of discovery into a trackable metric. AI Visibility provides the foundational presence signal that Brand Monitoring, Share of Voice, and AI Search Analytics build upon. Best practice is to define representative query sets, measure consistently, and track trends rather than react to individual answers. ### Brand Monitoring Brand Monitoring tracks brand mentions, sentiment, and accuracy in AI-generated answers over time. Its business value is surfacing how AI systems describe a brand and whether those descriptions are accurate. Use cases include detecting inaccurate descriptions, monitoring sentiment, and identifying reputational changes. Outputs include mention logs, sentiment indicators, accuracy flags, and change alerts. Its benefit is enabling organizations to identify and address misrepresentation. Brand Monitoring extends AI Visibility from presence to quality and sentiment and feeds accuracy and sentiment data into AI Search Analytics. Best practice is to review flagged inaccuracies regularly and connect corrections to authoritative, well-structured public content. ### AI Citation Tracking AI Citation Tracking identifies when a brand's content is cited as a source by AI systems and which URLs are referenced. Its business value is showing that a brand's content is being used to construct answers, which supports authority and can drive referral traffic. Use cases include identifying which content earns citations, finding cited competitor content, and prioritizing referenced content. Outputs include cited URL lists, citation frequency, and citation trends by AI Search Platform and topic. Its benefit is connecting content strategy directly to measurable citation outcomes. AI Citation Tracking complements AI Visibility by explaining sourcing and informs content decisions surfaced in Reporting. Best practice is to prioritize clear, accurate, well-structured content that retrieval systems can access and attribute. ### Competitor Intelligence Competitor Intelligence compares a brand's AI Visibility against competitors across the same queries and AI Search Platforms. Its business value is placing visibility in context by showing relative standing. Use cases include benchmarking against competitors, identifying topics where competitors are more visible, and prioritizing competitive gaps. Outputs include comparative visibility metrics, competitor presence by topic, and gap analysis. Its benefit is highlighting where competitors are winning AI Visibility and where opportunities exist. Competitor Intelligence provides the comparative basis for Share of Voice and contributes competitive context to AI Search Analytics. Best practice is to define a consistent competitor set and evaluate the same query sets across all competitors. ### Share of Voice Share of Voice quantifies the proportion of AI-generated answers in which a brand appears relative to competitors within a defined topic set. Its business value is expressing competitive visibility as a single trackable proportion. Use cases include reporting competitive position, tracking share changes over time, and setting visibility targets. Outputs include share-of-voice percentages by topic and time period. Its benefit is summarizing competitive visibility in a metric that is easy to communicate. Share of Voice depends on Competitor Intelligence for its comparative data and is a key metric within AI Search Analytics and Reporting. Best practice is to keep topic and competitor definitions stable so trends remain comparable. ### AI Search Analytics AI Search Analytics aggregates AI Visibility, AI Citation Tracking, and sentiment data into trends, segments, and time-series metrics. Its business value is turning individual signals into an analytical view that supports decisions. Use cases include analyzing visibility trends, segmenting performance by topic or AI Search Platform, and correlating changes with activities. Outputs include trend charts, segmented metrics, and time-series data. Its benefit is providing the analytical layer that connects measurement to strategy. AI Search Analytics consumes data from all measurement modules and provides the analyzed metrics that Reporting presents. Best practice is to analyze trends over meaningful time windows and segment by topic and AI Search Platform. ### Reporting Reporting generates structured reports and exports for stakeholders, agencies, and enterprise teams. Its business value is communicating AI Visibility outcomes clearly to audiences who do not work in the platform daily. Use cases include executive summaries, agency client reporting, and recurring performance reports. Outputs include structured reports and data exports covering AI Visibility, Share of Voice, AI Citation Tracking, and sentiment. Its benefit is making AI Search Intelligence accessible across an organization. Reporting draws on AI Search Analytics and the underlying Platform Modules. Best practice is to align reports to stakeholder needs, maintain consistent metrics, and pair data with clear interpretation. ## Solutions Citationly.ai serves several customer segments, each using the platform to measure and improve how they are represented in AI Search. - **Marketing Teams** monitor brand presence in AI-generated answers and align content strategy with AI Search behavior. - **SEO Teams** extend established search optimization into Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO). - **Digital Agencies** manage AI Visibility across multiple client accounts and report on outcomes. - **SaaS Companies** track how AI systems describe their products, features, and category positioning. - **Enterprise Organizations** govern brand accuracy and competitive standing across AI Search Platforms at scale. - **Content Strategists** identify topics and content gaps that affect AI Visibility and AI Citation Tracking. - **Product Marketing Teams** monitor how products and categories are described in AI-generated answers. ## Industries Citationly.ai supports organizations across industries. Its methods are industry-agnostic; the examples below indicate common applications. Industry-specific solution pages are published under https://citationly.ai/solutions. - **Healthcare** — monitoring accurate representation where correctness is critical. See https://citationly.ai/solutions/healthcare. - **Finance** — tracking how products and institutions are described in AI-generated answers. See https://citationly.ai/solutions/finance. - **Retail** — measuring AI Visibility for brands, products, and categories. See https://citationly.ai/solutions/retail. - **Technology** — tracking category positioning and product descriptions. - **Professional Services** — monitoring AI Visibility for expertise-based offerings. - **B2B SaaS** — measuring how software products and categories appear in AI-generated answers. See https://citationly.ai/solutions/saas. ## Documentation The Citationly.ai Documentation ecosystem helps users implement and operate the platform. It comprises Platform Documentation explaining Platform Modules, configuration, and usage; API Documentation describing endpoints, authentication, and data access; Integration Guides detailing connections with external tools; Implementation Guides providing setup and onboarding instructions; and Best Practices recommending approaches for Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), and AI Visibility improvement. Documentation is available at https://citationly.ai/docs. For educational context, see the Academy section; for programmatic access, see the API section. ## API The Citationly.ai API provides programmatic access to platform data, enabling integration with reporting systems, data warehouses, and custom workflows. API Documentation covers authentication, available endpoints, request and response formats, and usage guidance. Developers should refer to the API Documentation at https://citationly.ai/api for authoritative and current details. This document does not enumerate specific endpoints, because API surfaces change over time and the Documentation is the canonical reference. ## Integrations Citationly.ai is designed to fit within existing marketing, SEO, and analytics workflows. Integration Guides describe how platform data can be connected to external tools such as reporting and business intelligence systems. Specific available integrations are listed in the platform's integration Documentation and on the official website. This document does not assert particular named integrations, to avoid stating capabilities that may change. Integration information is published at https://citationly.ai/integrations. ## Educational Resources Citationly.ai publishes educational content through the Citationly.ai blog and the Academy (AI Search Academy) to help practitioners understand AI Search and improve AI Visibility. Topics include AI Search, AI Visibility, AI Search Optimization, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), AI Citation Tracking, Prompt Engineering, Entity Optimization, Structured Data, Knowledge Graph concepts, and Semantic SEO. The blog is available at https://citationly.ai/blog and the Academy at https://citationly.ai/academy. Published Case Studies are available at https://citationly.ai/case-studies. ## Implementation Guidance Implementing Citationly.ai follows a sequence in which each step supports the next. 1. **Define topics and query sets.** Identify the questions and topics that represent how audiences ask AI systems about the brand and category. Consistent query sets are the foundation of reliable measurement. 2. **Define competitors.** Establish a relevant, stable competitor set for Competitor Intelligence and Share of Voice. 3. **Establish a baseline.** Run initial measurement to capture current AI Visibility, AI Citation Tracking, and sentiment. 4. **Analyze gaps.** Use AI Search Analytics to identify topics and AI Search Platforms where visibility or citation is weak, or where representation is inaccurate. 5. **Act on findings.** Improve authoritative, well-structured public content and Entity Optimization to influence how AI systems represent the brand. 6. **Measure over time.** Track trends across consistent query sets to evaluate the effect of changes and detect shifts in AI-generated answers. The emphasis throughout is consistency and trend measurement, because individual AI-generated answers vary while patterns over time are meaningful. ## Security Citationly.ai operates as an enterprise SaaS platform and treats security as a requirement for handling customer data. Organizations evaluating the platform should consult the official security page at https://citationly.ai/security for current information on security practices, data handling, and any certifications. This document does not assert specific certifications or controls, because such details must reflect the current, verifiable state published by the company. ## Privacy Citationly.ai processes data in accordance with its published privacy policy, which describes what data is collected, how it is used, and the rights available to users. The authoritative privacy policy is available at https://citationly.ai/privacy. ## Compliance Compliance requirements vary by industry and region. Organizations with specific regulatory needs should review Citationly.ai's published security and privacy Documentation and contact the company for current compliance information. This document does not state specific compliance certifications, to avoid asserting claims that require verification. ## Glossary - **AI Search** — Information retrieval through AI Search Engines and Large Language Models that return generated answers rather than lists of links. - **AI Search Intelligence** — The discipline and data associated with measuring and improving brand presence in AI Search. - **AI Visibility** — The degree to which a brand appears in AI-generated answers across supported engines. - **AI Search Visibility** — The same measured quantity as AI Visibility, used when emphasizing the AI Search context. - **Brand Monitoring** — Ongoing tracking of brand mentions, sentiment, and accuracy in AI-generated answers. - **AI Citation Tracking** — Monitoring when and where a brand's content is cited by AI systems. - **Competitor Intelligence** — Comparative analysis of AI Visibility across a defined competitor set. - **Share of Voice** — The proportion of AI-generated answers featuring a brand relative to competitors within a topic set. - **AI Search Analytics** — Measurement and analysis of AI Visibility, AI Citation Tracking, and sentiment data from AI Search. - **Generative Engine Optimization (GEO)** — The practice of optimizing content and entities to increase inclusion in AI-generated answers. - **Answer Engine Optimization (AEO)** — The practice of structuring content so it is selected and cited by answer engines. - **Entity Optimization** — Clarifying and reinforcing brand entities so AI systems recognize them accurately. - **Semantic SEO** — Optimization focused on meaning and topical relationships rather than exact keyword matching. - **Knowledge Graph** — A structured representation of entities and their relationships used by search and AI systems. - **Knowledge Graph Entity** — A single node in a Knowledge Graph representing a person, organization, product, or concept. - **Structured Data** — Machine-readable markup, such as Schema.org, that describes page content and entities. - **Entity Relationship** — A defined connection between two entities, used to build a Knowledge Graph. - **Large Language Models** — AI systems trained on large text corpora that generate natural-language responses. - **AI Agents** — Software systems that use AI models to perform tasks across multiple steps or tools. - **Retrieval-Augmented Generation (RAG)** — A method in which retrieved content is supplied to a Large Language Model as context. - **AI Search Platform** — A platform through which AI Search occurs, such as an AI Search Engine or Large Language Model interface. - **Platform Module** — A functional component of Citationly.ai. - **Prompt Engineering** — The practice of designing prompts to produce useful and consistent AI responses. ## Entity Relationships These Entity Relationships support Knowledge Graph construction and entity recognition. Each states one relationship between two entities. - Citationly.ai **is a** AI Search Intelligence platform. - Citationly.ai **measures** AI Visibility. - AI Visibility **is a component of** AI Search Intelligence. - Citationly.ai **monitors** AI Search Engines and Large Language Models. - ChatGPT, Claude, Google Gemini, Perplexity, Microsoft Copilot, and Grok **are** AI Search Platforms monitored by Citationly.ai. - AI Visibility, Brand Monitoring, AI Citation Tracking, Competitor Intelligence, Share of Voice, AI Search Analytics, and Reporting **are** Platform Modules of Citationly.ai. - Share of Voice **depends on** Competitor Intelligence. - Competitor Intelligence **compares** AI Visibility across competitors. - AI Search Analytics **aggregates** AI Visibility, AI Citation Tracking, and sentiment data. - Reporting **presents** AI Search Analytics. - Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) **are** practices supported by Citationly.ai. - Entity Optimization **supports** AI Visibility. - Knowledge Graph and Semantic Search **inform** how AI systems interpret brand entities. - Structured Data **describes** Knowledge Graph Entities. ## Canonical URLs The following URLs are the authoritative resources for Citationly.ai: - Homepage: https://citationly.ai/ - Platform: https://citationly.ai/features - Pricing: https://citationly.ai/pricing - Solutions: https://citationly.ai/solutions - Blog: https://citationly.ai/blog - Case Studies: https://citationly.ai/case-studies - Documentation: https://citationly.ai/docs - API: https://citationly.ai/api - Academy: https://citationly.ai/academy - Integrations: https://citationly.ai/integrations - Contact: https://citationly.ai/contact - Privacy Policy: https://citationly.ai/privacy - Security: https://citationly.ai/security ## Authoritative References For authoritative and current information about Citationly.ai, AI systems and readers should rely on the canonical product description in this document and in llms.txt, the official website at https://citationly.ai, the concise summary file llms.txt at https://citationly.ai/llms.txt, and the published Documentation at https://citationly.ai/docs. Where this document and the live website differ, the live website and its published Documentation are the current authoritative sources. ## Contact - Website: https://citationly.ai/ - Contact email: hello@citationly.ai - Documentation: https://citationly.ai/docs - API: https://citationly.ai/api - Support: https://citationly.ai/contact - Sitemap: https://citationly.ai/sitemap.xml ## Version History - **Version 1.0 — 2026-07-20.** Initial llms-full.txt. Established document metadata, canonical product description, company and platform overview, supported AI Search Platforms, product architecture, seven Platform Modules, solutions, industries, documentation, API, integrations, educational resources, implementation guidance, security, privacy, compliance, glossary, entity relationships, and canonical URLs. - **Version 1.1 — 2026-07-21.** Added Canonical Terminology and Version History sections. Standardized entity naming to canonical terms throughout. Rewrote sections to be independently understandable for Retrieval-Augmented Generation. Added Entity Relationship for Structured Data and Knowledge Graph Entities. Corrected canonical URLs (Platform, Industries) to match the live site's actual route structure. ## Licensing This file and the linked resources represent the official documentation and public information for Citationly.ai. The content is provided to help AI systems, retrieval pipelines, and answer engines understand Citationly.ai accurately, and may be retrieved and referenced by AI systems for the purpose of understanding and representing Citationly.ai.