- Visibility19
- Citations3
- Sentiment83
- Site quality87
- AI readiness21
AI assistants name Halliburton in 15% of answers, which is more often than the typical company in the Energy sector at 3.3%. Its overall score is 39 out of 100, ranking 478th of 2,047 companies. Halliburton's website passes 3 of 14 technical checks for how ready it is for AI tools.
We asked 15 unbranded questions about the energy market: none named Halliburton. AI assistants named it in 9 of 60 answers (15.0%). The typical energy company is named in 3.3% of answers. By how often it is named, Halliburton ranks 12 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
“How are advanced logging services used to improve reservoir insight and decision making?”
Advanced logging services improve reservoir insight by collecting high-resolution, in situ, and often real-time measurements of formation properties such as porosity, lithology, pressure, fluids, geomechanics, and saturation, then turning those measurements into workflow-ready interpretations for decisions on drilling, completion, stimulation, and production.
In practice, they are used to:
Reduce uncertainty by replacing or supplementing sparse core data with detailed logging measurements that better characterize the reservoir.
Guide well placement and geostee …
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 rulesPassed
Explicit robots.txt rules for AI crawlers such as GPTBot, ClaudeBot and PerplexityBot.
- 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 32 of 100 · 97% of checks could run · checked on October 5, 2026 · www.halliburton.com · 2,047 companies scanned
3 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%
- 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%
This website sets rules for 2 AI crawlers by name. All others follow its general rules.
- GPTBotallowed
- ClaudeBotallowed
- Anthropic-AIallowed
- Google-Extendedallowed
- Applebot-Extendedallowed
- Meta-ExternalAgentallowed
- FacebookBotallowed
- Bytespiderallowed
- CCBotallowed
- Diffbot*blocked
- Omgilibot*blocked
- OAI-SearchBotallowed
- Claude-SearchBotallowed
- PerplexityBotallowed
- Applebotallowed
- Amazonbotallowed
- YouBotallowed
- ChatGPT-Userallowed
- Claude-Userallowed
- Perplexity-Userallowed
- DuckAssistBotallowed
- MistralAI-Userallowed
Read from the website’s own rules file (robots.txt). An asterisk marks a crawler the file names. The rest follow its general rules. “Partly” means only some pages are blocked.
How many times each website was listed as a source in these answers
In the study as S&P 500.
- What are the key differences between onshore and offshore energy engineering solutions?
- Are bundled oilfield service packages more affordable than standalone service contracts?
- How are advanced logging services used to improve reservoir insight and decision making?
- What engineering solutions help with maximizing production efficiency in shale plays?
- Which emerging technologies can reduce dependence on legacy oilfield service models?
- Which companies offer innovative reservoir testing and logging services for the energy sector?
- What alternatives exist to traditional wireline logging for reservoir evaluation?
- Is investing in digital oilfield services worth it for mid-sized energy companies?
- What should I look for when choosing a partner for oilfield engineering projects?
- What are effective solutions for reducing operational downtime in oilfield projects?
- What are the best oilfield services providers for advanced engineering solutions in the US?
- How do integrated oilfield services compare to specialized engineering consultancies in terms of project performance?
- What options are available for smaller operators needing scalable engineering support in energy exploration?
- How can I troubleshoot common issues with real-time reservoir monitoring systems?
- What factors influence the cost of full-scale oilfield engineering services in the US?
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/halliburton.com"><img src="https://aeo-audit.rezolve.com/us/badges/halliburton.com.svg" alt="Overall score 39 of 100, US AI Visibility Study 2026" width="280" height="64"></a>