- Visibility18
- Citations4
- Sentiment100
- Site quality67
- AI readiness29
AI assistants name Aeva in 13.3% of answers, which is somewhat more often than the typical company in the technology sector at 10%. Its overall score is 40 out of 100, ranking 405th of 2,047 companies. The company's website passes 4 out of 14 technical checks for AI tool readiness.
We asked 15 unbranded questions about the technology market: none named Aeva. AI assistants named it in 8 of 60 answers (13.3%). The typical technology company is named in 10.0% of answers. By how often it is named, Aeva ranks 111 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
“FMCW LiDAR vs traditional time-of-flight LiDAR: which offers better accuracy for self-driving cars?”
Short answer
For per-point radial range and native velocity (Doppler) accuracy, FMCW LiDAR has the technical edge.
For the practical, deployed automotive market today (cost, maturity, robustness across many environments) pulsed Time‑of‑Flight (ToF) LiDAR is still the more common solution and often the better choice overall for OEMs.
Why (brief technical comparison)
Measurement principle and what that implies:
FMCW: measures a frequency (beat) between transmitted and received continuous chirped light; that yields range and radial velocity simultaneously with coherent detection. That …
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.aeva.com · 2,047 companies scanned
4 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%
- PassedHTTP 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.
- What are some alternatives to 4D LiDAR for object detection in autonomous navigation?
- FMCW LiDAR vs traditional time-of-flight LiDAR: which offers better accuracy for self-driving cars?
- What are common issues with integrating 4D LiDAR sensors into automotive platforms?
- What should I consider when choosing perception sensors for autonomous navigation projects?
- What is the price range for long-range automotive-grade LiDAR sensors in the US?
- What options are there besides FMCW-based sensing for long-range autonomous applications?
- How can high-resolution 4D LiDAR improve safety in warehouse automation?
- How does 4D LiDAR compare to 3D LiDAR for advanced industrial automation?
- Why might a perception system using FMCW LiDAR experience false positives or missed detections?
- Do 4D LiDAR systems have higher total cost of ownership than other perception sensors?
- Is investing in 4D LiDAR technology worth it for next-gen robotics applications?
- Best long-range LiDAR sensors for industrial perception systems?
- Practical applications of velocity and range data from 4D LiDAR in smart infrastructure projects?
- What are the top 4D LiDAR solutions for autonomous vehicle navigation?
- Which non-LiDAR perception technologies are suitable for industrial robotics?
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.
A badge of this result that you can show on your own website. Copy the code below.
<a href="https://aeo-audit.rezolve.com/us/aeva.com"><img src="https://aeo-audit.rezolve.com/us/badges/aeva.com.svg" alt="Overall score 40 of 100, US AI Visibility Study 2026" width="280" height="64"></a>