- Visibility8
- Citations2
- Sentiment50
- Site quality87
- AI readiness14
AI assistants name SoundThinking in 5% of answers, compared with 3.3% for the typical company in its group. Its overall score is 29 out of 100, which ranks 1,316th of 2,047 companies. The website passes 2 of 14 technical checks for readiness for AI tools.
We asked 15 unbranded questions about its market: none named SoundThinking. AI assistants named it in 3 of 60 answers (5.0%). The typical company in its group is named in 3.3% of answers. By how often it is named, SoundThinking ranks 27 of 61 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.
61 companies in this sector. The line on each bar is the typical company.
An answer that names the company
“Best real-time incident detection technologies for law enforcement operations”
Below is a concise, practical guide to the best real‑time incident‑detection technologies for law‑enforcement operations, with strengths, weaknesses, deployment notes, and vendor examples so you can shortlist and plan procurement. I cite recent evaluations and market sources for the most important claims.
1) Real‑time AI video analytics (on‑camera or server/cloud)
What it does: Detects people, weapons, fighting, falls, crowds, unusual motion/loitering, vehicle behavior (wrong‑way, stopped in travel lane), and object removal — and issues real‑time alerts to operators or CAD.
Why law enfor …
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.soundthinking.com · 2,047 companies scanned
2 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%
- Not metToken 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.
- GPTBotallowed
- ClaudeBotallowed
- Anthropic-AIallowed
- Google-Extendedallowed
- Applebot-Extendedallowed
- Meta-ExternalAgentallowed
- FacebookBotallowed
- Bytespiderallowed
- CCBotallowed
- Diffbotallowed
- Omgilibotallowed
- 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 US-listed.
- How to address false positives in gunfire detection technologies for urban safety teams
- What are some other approaches to leveraging data for faster criminal investigations beyond AI-driven platforms?
- Options for public safety agencies seeking non-AI solutions to strengthen critical entry point security
- Best real-time incident detection technologies for law enforcement operations
- Common issues with integrating AI-powered public safety platforms into existing agency workflows
- How do AI-driven gunfire detection systems compare to traditional surveillance methods for city policing?
- Typical costs associated with comprehensive AI-powered public safety ecosystems in the United States
- How can real-time data platforms assist in coordinating emergency response during critical incidents?
- What are the most effective AI-powered solutions for public safety agencies in the US?
- Are AI-based investigative platforms worth the investment for mid-sized police departments?
- Alternatives to gunfire detection sensors for improving urban incident awareness
- Is there a pricing difference between cloud-based and on-premise public safety AI platforms?
- What should security organizations consider before deploying operational AI for situational awareness?
- Ways public safety agencies use AI analytics to generate actionable investigative leads
- Advantages and drawbacks of vehicle intelligence platforms versus manual license plate checks for public safety
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/soundthinking.com"><img src="https://aeo-audit.rezolve.com/us/badges/soundthinking.com.svg" alt="Overall score 29 of 100, US AI Visibility Study 2026" width="280" height="64"></a>