- Visibility24
- Citations3
- Sentiment67
- Site quality73
- AI readiness21
AI assistants name Baker Hughes in 10% of answers, which is more often than the typical company in the Energy sector. Its overall score is 36 out of 100, ranking 752nd of 2,047. The company's website passes 1 of 14 technical checks for AI tool readiness.
We asked 15 unbranded questions about the energy market: none named Baker Hughes. AI assistants named it in 6 of 60 answers (10.0%). The typical energy company is named in 3.3% of answers. By how often it is named, Baker Hughes ranks 20 of 109 in its sector.
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.
An answer that names the company
“Is investing in autonomous well construction systems worth it for mid-sized oil and gas operators?”
For many mid-sized oil and gas operators, yes—but only if the drilling program is large, repeatable, and expensive enough to justify the upfront integration and change-management costs. The strongest case is when autonomous systems can reduce non-productive time, improve rate of penetration, and deliver more consistent well placement, which vendors and industry case studies say can shorten well delivery time and lower well construction costs.
The value proposition is mainly operational and economic:
Lower drilling time and cost: Baker Hughes says its autonomous well …
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 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.
- Content Signals
Show every check, for your technical teamHide the checks
- study: 92.0%robots.txtPassed
A valid robots.txt with crawl rules at the site root.
- 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 negotiationNot met
Return a markdown version when asked with Accept: text/markdown, for example at the CDN.
- 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 25 of 100 · 97% of checks could run · checked on October 5, 2026 · www.bakerhughes.com · 2,047 companies scanned
1 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
- Not metrobots.txt publishedstudy: 85.0%
- Not metXML sitemapstudy: 73.2%
- Not metHTTP Link headers (RFC 8288)study: 29.0%
- Not metMarkdown 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 S&P 500.
- How can integrated monitoring solutions be used to reduce CO2 emissions in refinery operations?
- Are there substitutes for end-to-end autonomous optimization in well construction?
- What are practical applications of advanced hydrogen technologies in industrial settings?
- What are the main alternatives to remote operations for monitoring industrial facilities?
- How much should a medium-sized manufacturer expect to pay for process efficiency upgrades using digital technology?
- What are the best industrial technology solutions to improve manufacturing efficiency in the U.S.?
- Do emissions abatement technologies pay off for large-scale industrial facilities?
- Is investing in autonomous well construction systems worth it for mid-sized oil and gas operators?
- What is the typical cost range for deploying carbon capture and storage solutions in the U.S.?
- Remote industrial operations vs. on-site management—what are the advantages and disadvantages?
- What are common challenges when implementing digital operations in energy production?
- How do you address reliability issues in geothermal solutions for power generation?
- How do carbon capture and storage solutions compare to traditional emissions reduction methods?
- Which companies offer advanced hydrogen deployment technologies for energy producers?
- What options exist besides emissions abatement technologies for meeting industrial emissions targets?
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.
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<a href="https://aeo-audit.rezolve.com/us/bakerhughes.com"><img src="https://aeo-audit.rezolve.com/us/badges/bakerhughes.com.svg" alt="Overall score 36 of 100, US AI Visibility Study 2026" width="280" height="64"></a>