- Visibility0
- Citations0
- Sentiment50
- Site quality80
- AI readiness36
AI assistants do not name Marathon Digital Holdings in any of their 60 answers, while the typical company in its sector is named in 3.3% of answers. Its overall score is 27 out of 100, ranking 1,601st of 2,047 companies in the study. The company's website passes 5 out of 14 technical checks for AI tool readiness.
We asked 15 unbranded questions about the energy market: none named Marathon Digital Holdings. AI assistants named it in none of 60 answers. The typical energy company is named in 3.3% of answers. By how often it is named, Marathon Digital Holdings ranks 74 of 109 in its sector.
Named fewer than three times in total. Read this as a sign that AI assistants rarely bring this company up, not as an exact comparison with its rivals.
Every dot is a company in the study. Hover one to see it; click to open its report.
This companySame sectorEvery other companyNever named
Five parts, each scored out of 100.
How often AI assistants name the company
How often AI uses the company's own website as a source
How positively AI describes the company
How well built the website's own pages are
How easily AI tools can read and use the website
Visibility is calculated from the answers on this page. How the score is built.
109 companies in this sector. The line on each bar is the typical company.
The company is not named in this answer
“Best solutions for enterprises looking to balance and optimize their energy systems”
For enterprises, the best solutions are typically enterprise energy management platforms that combine real-time monitoring, analytics, automation, and optimization across buildings, plants, or portfolios. The strongest options in the results are Facilio for unified portfolio operations, ABB Ability Energy and Asset Manager for cloud-based building and asset management, Honeywell Forge for IoT-based building performance optimization, and Schneider Electric for industrial power management.
The best fit depends on the enterprise use case:
Enterpri …
The AI’s answer as we received it, shortened and not checked for accuracy.
How the mentions in these answers were split between this company and its rivals
Answers naming it, out of answers received
What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.
- 0
- 1
- 2
- 3
- 4
- 5
Level 0: Not yet agent-ready. Agents struggle to find basic information about the site.
- 2 of robots.txt, Sitemap, Link headers
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtNot met
Publish a valid robots.txt with crawl rules and a Sitemap line.
- study: 86.8%SitemapPassed
An XML sitemap listing the pages, ideally referenced from robots.txt.
- study: 0.3%Link headersNot met
Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.
- study: 5.5%DNS for AI DiscoveryNot present
Advertise agent endpoints with SVCB records under _agents.
- study: 1.9%Markdown negotiationPassed
Requests sent with Accept: text/markdown receive a markdown version of the page.
- study: 25.1%llms.txtNot met
Publish an llms.txt with a short summary and the key links.
- study: 93.4%AI bot rulesPartly met
Add explicit robots.txt groups for the main AI crawlers.
- study: 1.4%Content SignalsNot met
Add a Content-Signal line for search, ai-input and ai-train to robots.txt.
- study: 0.2%Web Bot AuthNot present
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogNot met
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryNot met
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceNot met
Serve protected resource metadata at /.well-known/oauth-protected-resource.
- study: 0.0%auth.mdNot met
Publish an auth.md describing how agents sign in.
- study: 0.2%MCP Server CardNot met
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardNot met
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsNot met
Publish a skills index with the main tasks agents can do.
- study: 33.7%WebMCPCould not check
Register key actions such as search or cart as WebMCP tools.
- study: 0.1%ARD manifestNot met
Publish an ai-catalog.json listing every agent interface.
Score 26 of 100 · 97% of checks could run · checked on October 5, 2026 · www.mara.com · 2,047 companies scanned
5 of 14 passed. This list is for your technical team. The percentage is how many companies in the study pass each check.
These 14 checks ran during the audit and are what the AI readiness part of the overall score counts. The scan above ran later and is stricter on Link headers and Markdown, so a check can pass here and not there.
Show the 14 checksHide them
- Passedrobots.txt publishedstudy: 85.0%
- PassedXML sitemapstudy: 73.2%
- PassedHTTP Link headers (RFC 8288)study: 29.0%
- PassedMarkdown content negotiationstudy: 8.3%
- Not metllms.txt publishedstudy: 21.7%
- PassedToken budget (page weight)study: 95.2%
- Not metExplicit AI bot rulesstudy: 13.0%
- Not metAPI Catalog (.well-known/api-catalog)study: 0.2%
- Not metOAuth Authorization Server discoverystudy: 10.1%
- Not metOAuth Protected Resource discoverystudy: 9.3%
- Not metMCP Server Cardstudy: 0.0%
- Not metA2A Agent Cardstudy: 0.0%
- Not metWebMCP toolsstudy: 4.2%
- Not metAgent Skills declaredstudy: 0.0%
How many times each website was listed as a source in these answers
In the study as US-listed.
- Best solutions for enterprises looking to balance and optimize their energy systems
- What are the most promising digital energy technologies for improving grid stability in the US?
- What are the leading trends in digital technologies for advancing energy systems in the US market?
- Are advanced digital energy management systems worth the investment for medium-sized businesses?
- What is the typical price range for digital energy optimization platforms aimed at US enterprises?
- What are some alternatives to large-scale digital energy platforms for companies seeking better energy management?
- How can digital energy platforms help businesses achieve more efficient energy usage?
- How can enterprises address integration challenges with new energy balancing solutions?
- Common issues when deploying digital energy technologies for system optimization
- Examples of how advanced energy system solutions support grid modernization efforts
- Digital energy system control vs traditional grid management—what are the main differences?
- How do costs for fully managed digital energy solutions compare to DIY implementation?
- How do different digital energy platforms compare for managing decentralized energy resources?
- Alternatives to centralized digital energy systems for balancing local grids
- What factors should energy professionals consider before adopting digital energy balancing technologies?
Columns, left to right: OpenAI, Claude, Perplexity, Gemini. The AI assistants often disagree. Across the study, on 40% of the questions where a company is named, only one of the four names it.