- Visibility38
- Citations1
- Sentiment65
- Site quality93
- AI readiness21
AI assistants name Affirm in 46.7% of answers, much more often than the typical company in its sector at 3.3%. Affirm's overall score is 43 out of 100, ranking 161st of 2,047 companies. Its website passes 3 of 14 technical checks, indicating how ready the website is for AI tools.
We asked 15 unbranded questions about the finance market: none named Affirm. AI assistants named it in 28 of 60 answers (46.7%). The typical finance company is named in 3.3% of answers. By how often it is named, Affirm ranks 6 of 205 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.
205 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Are buy now, pay later services a good idea for building credit history?”
Usually not by itself. Most buy now, pay later (BNPL) services do not report on-time payments to credit bureaus, so using them normally won’t help build credit history; however, if a provider does report and you pay on time, it can help, while missed payments can hurt your credit.
A few important nuances:
Most short-term BNPL plans don’t build credit. Standard BNPL plans are often invisible to credit bureaus unless there’s a problem like a missed payment or collections.
Some providers do report. Examples cited in the results include Affirm, Se …
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 AuthCould not check
Only relevant if you run your own agents or crawlers: publish a signing key directory.
- study: 0.3%API CatalogCould not check
List public APIs in a linkset at /.well-known/api-catalog.
- study: 11.8%OAuth discoveryCould not check
Publish OpenID Connect or OAuth server metadata under /.well-known.
- study: 10.3%OAuth Protected ResourceCould not check
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 CardCould not check
Offer an MCP server and publish its server card under /.well-known.
- study: 0.1%A2A Agent CardCould not check
Publish an agent card at /.well-known/agent-card.json.
- study: 0.2%Agent SkillsCould not check
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 manifestCould not check
Publish an ai-catalog.json listing every agent interface.
Score 37 of 100 · 65% of checks could run · checked on October 5, 2026 · www.affirm.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 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.
- Are buy now, pay later services a good idea for building credit history?
- How do traditional credit cards compare to split-payment services for online shopping?
- What should I do if I miss a payment on a buy now, pay later plan?
- Do buy now, pay later services in the US usually have hidden fees?
- Is using a no-fee installment plan worth it for large online purchases?
- Are layaway programs still available as an option instead of pay-over-time?
- Are buy now, pay later services accepted at most major online retailers in the US?
- How are the true costs of pay-in-4 installment plans calculated?
- How do pay-over-time payment plans work for US shoppers?
- What are the best buy now, pay later options for online shopping in the US?
- What are some alternatives to buy now, pay later for spreading out online payments?
- Pay-in-4 versus longer-term installment plans—what’s the difference?
- Can you use pay-over-time options for travel or vacation bookings?
- Why was my application for a pay-over-time service declined at checkout?
- How do interest-free buy now, pay later options compare to those with interest charges?
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/affirm.com"><img src="https://aeo-audit.rezolve.com/us/badges/affirm.com.svg" alt="Overall score 43 of 100, US AI Visibility Study 2026" width="280" height="64"></a>