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PB

Permian Basin Royalty Trust

pbt-permianbasintrust.com · Energy · PBT

Ranked 1,607 of 2,047 companies in the study

27Overall score
  • Visibility0
  • Citations0
  • Sentiment50
  • Site quality87
  • AI readiness21
What this means for Permian Basin Royalty Trust

AI assistants do not name Permian Basin Royalty Trust in any of their answers to market questions. Its overall score is 27 out of 100, ranking 1,607th of 2,047 companies. The website passes 3 of 14 technical checks for AI tool readiness.

We asked 15 unbranded questions about the energy market: none named Permian Basin Royalty Trust. 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, Permian Basin Royalty Trust 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.

Named by AI in
0.0%
0 of 60 answers
Share of mentions
0.0%
compared with 5 rivals
AI readiness
3/14
technical checks passed. Typical for its sector: 3
Rank in its sector
70 of 109
by overall score, where the typical energy company scores 29
How Permian Basin Royalty Trust compares with all 2,047 companies

Every dot is a company in the study. Hover one to see it; click to open its report.

This companySame sectorEvery other companyNever named

What makes up the score

Five parts, each scored out of 100.

Visibility
0

How often AI assistants name the company

Citations
0

How often AI uses the company's own website as a source

Sentiment
50

How positively AI describes the company

Site quality
87

How well built the website's own pages are

AI readiness
21

How easily AI tools can read and use the website

Visibility is calculated from the answers on this page. How the score is built.

Compared with energy

109 companies in this sector. The line on each bar is the typical company.

Overall score27
Ranks 70 of 109. Typical company: 29
Named in answers0%
Ranks 74 of 109. Typical company: 3%
AI readiness21
Ranks 52 of 109. Typical company: 21
What Gemini actually said

The company is not named in this answer

“Most effective approaches for dewatering sludge in US oilfield water treatment?”

The most effective approaches for dewatering sludge in US oilfield water treatment primarily involve mechanical methods, often preceded by chemical conditioning, to significantly reduce the volume and weight of the sludge. This reduction leads to lower disposal and transportation costs, and can even facilitate the transformation of waste into reusable resources.

Here's a breakdown of effective approaches:

1. Mechanical Dewatering Technologies

These methods physically separate water from solids, producing a concentrated "cake" that is easier to handle.

Filter Presses (Plate and Fra …

The AI’s answer as we received it, shortened and not checked for accuracy.

Share of mentions

How the mentions in these answers were split between this company and its rivals

Oilfield Water Logistics (OWL),
100.0%
Permian Basin Royalty Trust
0.0%
Sourcewater
0.0%
Hydrozonix
0.0%
Select Water Solutions
0.0%
WaterFleet
0.0%
Which AI assistants named the company

Answers naming it, out of answers received

Gemini
0/15
Perplexity
0/15
Claude
0/15
OpenAI
0/15
Ready for AI agents

What this website publishes for an AI agent to read, use and buy from, on a scale of six levels.

  1. 0
  2. 1
  3. 2
  4. 3
  5. 4
  6. 5

Level 1: Basic web presence. Two of robots.txt, a sitemap and Link headers are in place.

To reach level 2:
  • Content Signals
Show every check, for your technical team
Discoverability
77 of 100
  • robots.txtPassed

    A valid robots.txt with crawl rules at the site root.

    study: 92.0%
  • SitemapPassed

    An XML sitemap listing the pages, ideally referenced from robots.txt.

    study: 86.8%
  • Link headersNot met

    Send Link headers such as rel="api-catalog" or rel="describedby" on the homepage.

    study: 0.3%
  • DNS for AI DiscoveryNot present

    Advertise agent endpoints with SVCB records under _agents.

    study: 5.5%
Content accessibility
0 of 100
  • Markdown negotiationNot met

    Return a markdown version when asked with Accept: text/markdown, for example at the CDN.

    study: 1.9%
  • llms.txtNot met

    Publish an llms.txt with a short summary and the key links.

    study: 25.1%
Bot access control
32 of 100
  • AI bot rulesPartly met

    Add explicit robots.txt groups for the main AI crawlers.

    study: 93.4%
  • Content SignalsNot met

    Add a Content-Signal line for search, ai-input and ai-train to robots.txt.

    study: 1.4%
  • Web Bot AuthNot present

    Only relevant if you run your own agents or crawlers: publish a signing key directory.

    study: 0.2%
APIs, auth and MCP
0 of 100
  • API CatalogNot met

    List public APIs in a linkset at /.well-known/api-catalog.

    study: 0.3%
  • OAuth discoveryNot met

    Publish OpenID Connect or OAuth server metadata under /.well-known.

    study: 11.8%
  • OAuth Protected ResourceNot met

    Serve protected resource metadata at /.well-known/oauth-protected-resource.

    study: 10.3%
  • auth.mdNot met

    Publish an auth.md describing how agents sign in.

    study: 0.0%
  • MCP Server CardNot met

    Offer an MCP server and publish its server card under /.well-known.

    study: 0.2%
  • A2A Agent CardNot met

    Publish an agent card at /.well-known/agent-card.json.

    study: 0.1%
  • Agent SkillsNot met

    Publish a skills index with the main tasks agents can do.

    study: 0.2%
  • WebMCPNot met

    Register key actions such as search or cart as WebMCP tools.

    study: 33.7%
  • ARD manifestNot met

    Publish an ai-catalog.json listing every agent interface.

    study: 0.1%

Score 24 of 100 · 100% of checks could run · checked on October 4, 2026 · www.pbt-permianbasintrust.com · 2,047 companies scanned

See how every company did, and how this was measured.

The checks behind the AI readiness score

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 checks
Discoverability
  • Passedrobots.txt publishedstudy: 85.0%
  • PassedXML sitemapstudy: 73.2%
  • Not metHTTP Link headers (RFC 8288)study: 29.0%
Content Accessibility
  • Not metMarkdown content negotiationstudy: 8.3%
  • Not metllms.txt publishedstudy: 21.7%
  • PassedToken budget (page weight)study: 95.2%
Bot Access Control
  • Not metExplicit AI bot rulesstudy: 13.0%
API / Auth / MCP
  • 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%
What AI crawlers may read (robots.txt)

This website sets no rules for any AI crawler by name, like 84% of the websites whose rules we could read. Every AI crawler follows its general rules.

Training AI
  • GPTBotallowed
  • ClaudeBotallowed
  • Anthropic-AIallowed
  • Google-Extendedallowed
  • Applebot-Extendedallowed
  • Meta-ExternalAgentallowed
  • FacebookBotallowed
  • Bytespiderallowed
  • CCBotallowed
  • Diffbotallowed
  • Omgilibotallowed
AI search
  • OAI-SearchBotallowed
  • Claude-SearchBotallowed
  • PerplexityBotallowed
  • Applebotallowed
  • Amazonbotallowed
  • YouBotallowed
Answering questions
  • 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.

Websites the AI used as sources

How many times each website was listed as a source in these answers

image-ppubs.uspto.gov
20
mdpi.com
16
pmc.ncbi.nlm.nih.gov
13
jpt.spe.org
11
dupont.com
9
sciencedirect.com
9
nepis.epa.gov
8
researchgate.net
8
env.nm.gov
7
linkedin.com
6

In the study as US-listed.

Every question, and where the company came up (0 of 15)
QuestionOpenAIClaudePerplexityGemini
  • Latest technologies available for treating and reusing produced water in US oilfields?
  • How are polymers used to improve sludge dryness in remote oilfield water facilities?
  • Best practices for dosing dry versus emulsion polymer in produced water treatment applications?
  • Other approaches to managing and reducing sludge volume in produced water operations?
  • How do you calculate the cost per barrel when selecting a polymer for produced water treatment?
  • Are automatic blowdown controls necessary for efficient sludge management in produced water clarifiers?
  • What are the best methods for treating produced water in oilfield operations with high TDS levels?
  • What are effective alternatives to polymer-based treatments for high-salinity produced water?
  • How does polyacrylamide compare to other polymer types for oilfield water clarification?
  • How do you address iron sulfide solid build-up in produced water treatment systems?
  • What factors influence the overall price of dewatering solutions for oilfield sludge management?
  • Most effective approaches for dewatering sludge in US oilfield water treatment?
  • Jar testing versus field trial: Which is more reliable for produced water treatment optimization?
  • What causes persistent turbidity in produced water, and how can it be reduced after long storage?
  • Is investing in chemical dosing systems for brine management in oilfield operations worth it?
named the companyanswered without naming itdid not answer

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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