# Digraph

> Digraph is an evidence-led AI search visibility platform. It helps brand, SEO, content, and marketing teams measure how AI systems mention, recommend, compare, and cite their company across the buyer questions that matter.

Digraph is also referred to as Digraph AI or Digraph.dev. The canonical website is https://www.digraph.dev/.

## Start here

- [AI search visibility software](https://www.digraph.dev/ai-search-visibility-software): Overview of Digraph's platform for prompt-level visibility, answer evidence, citations, competitors, and follow-up work.
- [AI search monitoring](https://www.digraph.dev/ai-search-monitoring): Monitoring workflow for buyer prompts, AI answers, cited sources, competitors, and changes over time.
- [AI brand monitoring](https://www.digraph.dev/ai-brand-monitoring): Review how AI systems represent, recommend, compare, and cite a brand.
- [ChatGPT visibility tracker](https://www.digraph.dev/chatgpt-visibility-tracker): Track brand mentions, recommendations, citations, and competitive context in ChatGPT answers.
- [Generative engine optimization](https://www.digraph.dev/generative-engine-optimization): Evidence-led GEO framework for improving how a brand is represented in generative answers.
- [Answer engine optimization](https://www.digraph.dev/answer-engine-optimization): AEO framework connecting buyer questions, generated answers, source evidence, and actions.

## Audience workflows

- [AI search visibility for agencies](https://www.digraph.dev/ai-search-visibility-for-agencies): Repeatable client reporting with prompt, answer, citation, competitor, and action evidence.
- [Enterprise AI search visibility](https://www.digraph.dev/enterprise-ai-search-visibility): Multi-brand and multi-market visibility measurement with shared governance and evidence.
- [AI visibility software for SEO teams](https://www.digraph.dev/ai-visibility-software-for-seo-teams): Extend organic-search programs with AI-answer, citation, and competitor evidence.
- [AI visibility software for content teams](https://www.digraph.dev/ai-visibility-software-for-content-teams): Turn buyer-question and citation gaps into focused content briefs and refresh decisions.
- [AI visibility software for SaaS](https://www.digraph.dev/ai-visibility-software-for-saas): Monitor SaaS category discovery, product fit, integrations, and competitor framing in AI answers.
- [AI visibility software for ecommerce](https://www.digraph.dev/ai-visibility-software-for-ecommerce): Monitor product discovery, shopping shortlists, category answers, and cited sources.
- [Perplexity visibility tracker](https://www.digraph.dev/perplexity-visibility-tracker): Track how Perplexity represents, recommends, and cites a brand across research and buying questions.
- [Gemini visibility tracker](https://www.digraph.dev/gemini-visibility-tracker): Monitor how Gemini represents, recommends, and sources a brand across defined buyer questions.
- [Claude visibility tracker](https://www.digraph.dev/claude-visibility-tracker): Track how Claude describes, compares, and recommends a brand across decision questions.
- [Copilot visibility tracker](https://www.digraph.dev/copilot-visibility-tracker): Monitor how Copilot represents, compares, and recommends a brand across defined buyer questions.

## Evidence and comparisons

- [AI search visibility benchmark](https://www.digraph.dev/benchmarks/ai-search-visibility): Dated benchmark methodology and observed search visibility signals.
- [AI search visibility benchmark dataset](https://www.digraph.dev/benchmarks/ai-search-visibility-2026-09.json): Machine-readable benchmark observations with scope, result pages, and limitations.
- [Best AI search visibility tools](https://www.digraph.dev/compare/best-ai-search-visibility-tools): Category-level shortlist with transparent comparison criteria and official product sources.
- [Digraph comparisons](https://www.digraph.dev/compare): Comparison hub for AI search visibility platforms.
- [Digraph vs Peec AI](https://www.digraph.dev/compare/digraph-vs-peec): Prompt, source, competitor, and workflow comparison.
- [Digraph vs Profound](https://www.digraph.dev/compare/digraph-vs-profound): AI search visibility and evidence workflow comparison.
- [Digraph vs Scrunch](https://www.digraph.dev/compare/digraph-vs-scrunch): AI search monitoring and citation workflow comparison.
- [Digraph vs Evertune](https://www.digraph.dev/compare/digraph-vs-evertune): AI visibility, activation, and advertising workflow comparison.
- [Digraph vs Semrush](https://www.digraph.dev/compare/digraph-vs-semrush): Dedicated AI-answer evidence compared with a broad SEO suite.
- [Digraph vs Ahrefs](https://www.digraph.dev/compare/digraph-vs-ahrefs): AI visibility and citation research comparison.
- [Digraph vs Conductor](https://www.digraph.dev/compare/digraph-vs-conductor): AI-answer measurement alongside enterprise SEO workflows.
- [Digraph vs Temso](https://www.digraph.dev/compare/digraph-vs-temso): AI visibility, technical auditing, content, and agent workflow comparison.
- [Digraph vs Cituna](https://www.digraph.dev/compare/digraph-vs-cituna): Published pricing, AI-engine coverage, Search Console integration, and generated-fix comparison.
- [Digraph vs Am I Cited](https://www.digraph.dev/compare/digraph-vs-amicited): Credit-metered AI-response, prompt, domain, and workflow comparison.
- [Digraph vs Rank Prompt](https://www.digraph.dev/compare/digraph-vs-rank-prompt): Agency pricing, multi-brand capacity, white-label delivery, AI coverage, and API comparison.

## Research and guidance

- [What is AI search visibility?](https://www.digraph.dev/blog/ai-search-visibility): Definitions, measurement scope, and the relationship between AI answers and SEO.
- [AI brand monitoring](https://www.digraph.dev/blog/ai-brand-monitoring): How to review brand representation, accuracy, citations, and competitor framing.
- [How LLMs recommend brands](https://www.digraph.dev/blog/how-llms-recommend-brands): Research-oriented guide to brand recommendations across major answer systems.
- [AI search visibility handbook](https://www.digraph.dev/resources/ai-search-visibility-handbook): Practical reference for building an evidence-led visibility program.
- [How to Measure AI Search Visibility](https://www.digraph.dev/guides/how-to-measure-ai-search-visibility): A practical framework for prompt portfolios, answer metrics, competitors, citations, and reporting.
- [GEO vs SEO](https://www.digraph.dev/guides/geo-vs-seo): A comparison of traditional search optimization and generative engine optimization.
- [How to Get Cited by ChatGPT](https://www.digraph.dev/guides/how-to-get-cited-by-chatgpt): An evidence-led guide to accessible pages, clear entities, and citation monitoring.
- [How to Track Brand Visibility in AI Search](https://www.digraph.dev/guides/how-to-track-brand-visibility-in-ai-search): A repeatable checklist for prompts, answers, sources, competitors, and review cadence.
- [AI Citation Gap Analysis](https://www.digraph.dev/guides/ai-citation-gap-analysis): A claim-level method for finding the evidence behind competitor visibility.
- [AI Visibility Tools for Agencies](https://www.digraph.dev/guides/ai-visibility-tools-for-agencies): A source-linked comparison of agency platforms, client workflows, reporting, evidence, and plan limits.
- [GEO vs AEO](https://www.digraph.dev/guides/geo-vs-aeo): A practical comparison of generative and answer engine optimization.
- [How AI Search Engines Work](https://www.digraph.dev/guides/how-ai-search-engines-work): Retrieval, synthesis, citations, variance, and measurable answer signals.
- [How to Optimize Content for AI Search](https://www.digraph.dev/guides/how-to-optimize-content-for-ai-search): An evidence-led workflow for access, direct answers, sources, and validation.
- [LLM Optimization vs GEO](https://www.digraph.dev/guides/llm-optimization-vs-geo): What teams can improve around model behavior, retrieval, evidence, and generated representation.
- [AI Search Visibility Case Study](https://www.digraph.dev/resources/ai-search-visibility-case-study): An illustrative workflow from citation gap to bounded content action and validation.
- [AI Search Visibility Report Template](https://www.digraph.dev/resources/ai-search-visibility-report-template): A structured template for prompts, answers, citations, competitors, actions, and limits.
- [AI Search Monitoring Checklist](https://www.digraph.dev/resources/ai-search-monitoring-checklist): Twenty-four checks for scope, answer evidence, sources, site readiness, and validation.
- [Documentation](https://www.digraph.dev/docs): Product and methodology documentation.

## Source and editorial policy

Digraph's public comparisons identify the review date and link to official competitor documentation where claims depend on provider capabilities. AI-search observations are time-, prompt-, platform-, and market-dependent; they should be treated as measured snapshots rather than permanent rankings.

## Contact

- [Contact Digraph](https://www.digraph.dev/contact): Contact the Digraph team about product coverage, evidence, or an AI search visibility program.