- Visibility43
- Citations3
- Sentiment69
- Site quality87
- AI readiness43
AI assistants name GitLab in 43.3% of answers, which is much more often than the typical company in the Technology sector at 10%. GitLab's overall score is 47 out of 100, ranking 51st of 2,047 companies. Its website passes 6 of 14 technical checks for readiness with AI tools.
We asked 15 unbranded questions about the technology market: none named GitLab. AI assistants named it in 26 of 60 answers (43.3%). The typical technology company is named in 10.0% of answers. By how often it is named, GitLab ranks 26 of 273 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.
273 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“How can integrated DevSecOps platforms help teams collaborate more effectively?”
Integrated DevSecOps platforms bring development, security, and operations together into a shared toolchain and workflow. That alignment reduces friction and enables teams to collaborate more effectively in several concrete ways:
Main collaboration benefits
Shared visibility and single source of truth: centralized dashboards, ticket links, and traceability let devs, ops, and security see the same build status, vulnerabilities, and deployment history — reducing duplicated work and miscommunication.
Shift-left security + faster feedback loops: automated SAST, dependency scanning, and IaC ch …
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 negotiationPassed
Requests sent with Accept: text/markdown receive a markdown version of the page.
- study: 25.1%llms.txtPassed
An llms.txt that tells language models what the site offers.
- 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 54 of 100 · 97% of checks could run · checked on October 5, 2026 · about.gitlab.com · 2,047 companies scanned
6 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%
- 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%
- PassedOAuth Authorization Server discoverystudy: 10.1%
- PassedOAuth 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 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.
- GPTBotpartly
- ClaudeBotpartly
- Anthropic-AIpartly
- Google-Extendedpartly
- Applebot-Extendedpartly
- Meta-ExternalAgentpartly
- FacebookBotpartly
- Bytespiderpartly
- CCBotpartly
- Diffbotpartly
- Omgilibotpartly
- OAI-SearchBotpartly
- Claude-SearchBotpartly
- PerplexityBotpartly
- Applebotpartly
- Amazonbotpartly
- YouBotpartly
- ChatGPT-Userpartly
- Claude-Userpartly
- Perplexity-Userpartly
- DuckAssistBotpartly
- MistralAI-Userpartly
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 US-listed.
- How can integrated DevSecOps platforms help teams collaborate more effectively?
- Are there affordable intelligent software delivery solutions for startups or small businesses?
- How does a DevSecOps orchestration platform compare to using separate CI/CD and security tools?
- What are some lightweight alternatives to enterprise DevSecOps platforms for small teams?
- What are the best DevSecOps orchestration tools for enterprise-scale software delivery?
- Is it worth investing in an end-to-end DevSecOps orchestration platform for a mid-sized tech company?
- Typical pricing structures for enterprise DevSecOps orchestration platforms in the United States
- Common challenges when implementing DevSecOps automation in large development teams
- Ways to leverage AI-driven code suggestions in secure software delivery pipelines
- Integrated DevSecOps solutions vs modular pipeline setups—pros and cons
- Best open-source options for orchestrating secure software pipelines
- How can you fix slow deployment speeds in your software orchestration pipeline?
- What should enterprises consider before choosing a unified software delivery and security platform?
- Alternatives to all-in-one DevSecOps orchestration platforms for security automation
- Top features to look for in an intelligent DevSecOps platform for automation and security
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/gitlab.com"><img src="https://aeo-audit.rezolve.com/us/badges/gitlab.com.svg" alt="Overall score 47 of 100, US AI Visibility Study 2026" width="280" height="64"></a>