LoaderSave StorySave this storyCommentLoaderSave StorySave this storyIn 2020, face-recognition firm Clearview AI became infamous for scraping more than 3 billion photos from the internet to turn faces into names for police and security professionals. Now the company is experimenting with trawling the web again, this time using AI to potentially help law enforcement fill in the person behind the name—who they are, who they know, where they live, and what they’ve left behind online.
Clearview has quietly built and tested what it describes as an experimental AI “analyst assistant,” WIRED has learned. Called InquiryIQ, the unreleased tool is designed to take details an investigator unearths from a Clearview search and then automatically fan out across the web—opening webpages, analyzing images, and assembling what it finds into a profile containing the possible employers, aliases, associates, and physical characteristics of a person police are investigating.
InquiryIQ’s interface states that supplying age, gender, and race can help the system make “smarter decisions” as it searches, according to code for the tool analyzed by WIRED. One of the models that the company tested to power those decisions comes from SpaceXAI, the Elon Musk company behind Grok. (SpaceX and xAI merged in February.) Musk has pitched Grok as an alternative to supposedly “woke” AI systems, and the chatbot has repeatedly drawn scrutiny for racist, extremist, and inflammatory outputs. It is unclear how SpaceXAI would use those demographic inputs or how they might shape its output, and Clearview declined to speculate.
Experts say InquiryIQ and tools like it could reshape police investigations by compressing days or weeks of detective work into minutes. That speed could help police solve crimes faster, but it also lowers the cost of fishing expeditions, making it practical to scrutinize people police might otherwise never have investigated. And because generative AI can produce different answers from the same starting point, they say, it may be difficult to reconstruct why the system pursued one lead instead of another.
Clearview tells WIRED that InquiryIQ is a prototype that has never been pitched or shipped to customers and is not currently planned for release in its present form. The company also disputes that the tool amounts to an automated investigator. Instead, it describes InquiryIQ as a limited way to automate the web searches detectives already perform. In an interview, CEO Amos Kyler says the SpaceXAI and other model options visible in the interface were included so Clearview’s engineers could compare how different models performed during testing, not so police could choose which model ran the research. “No law enforcement user has ever used it, period,” Kyler says.
WIRED discovered InquiryIQ in files that Clearview’s login page sends to any visitor’s browser before they sign in. It was the same technique WIRED used last month to uncover OS Investigate, an AI-powered search tool being developed by Flock Safety, and to reconstruct a mock-up of its interface from publicly accessible files.
Clearview’s files similarly contain code and thousands of lines of text for its user interface—instructions, warnings, and feature descriptions that show how the company has designed and described InquiryIQ. They do not reveal exactly what happens inside Clearview’s servers, how well the tool works, or who has used it. According to the text, InquiryIQ is built to “automatically discover and enrich personal data from web sources.”
Clearview says the apparent completeness of InquiryIQ’s interface should not be mistaken for a product nearing release. Modern AI tools have made it much faster to build sophisticated prototypes, the company says, and the directive to Clearview’s engineers is to move prototypes along rapidly because it’s now possible to do so.
In 2020, The New York Times revealed that Clearview had scraped more than 3 billion images from Facebook, YouTube, Venmo, and millions of other websites. Overnight, the report transformed the company into one of the country’s most controversial surveillance firms. Tech companies demanded that it stop harvesting their users’ photos; lawsuits and regulatory investigations followed. But Clearview kept scraping. Its database grew from more than 3 billion images in 2020 to what the company now says is well over 70 billion, and Clearview says its technology is used by more than 2,000 law enforcement agencies nationwide.
Ton-That stepped down as CEO in December 2024 and left the board the following spring. Kyler, who became CEO in October last year, joined Clearview as an engineer in 2019 and presents a more deliberate, technical face of the company. Where Clearview’s early years were defined by boundary-pushing, Kyler talks instead about controls, auditing, and oversight.
“The mission today is the same,” Kyler says. But he describes the company’s recent focus as “refinement” and “ensuring that the product hits the kind of expectation of integrity that our customers expect.”
For years, Clearview said its job stopped at surfacing possible leads for law enforcement using face recognition. In a 2022 post, Ton-That wrote that it was “up to the investigator to follow those links and do more research to find additional information.” InquiryIQ appears designed to take on some of that work.
As Clearview describes it, InquiryIQ would begin after an investigator has already run a face-recognition search and identified details they consider relevant. Those details can be added to a profile alongside information such as age, gender, race, hair color, and eye color, which the interface says can help the AI make “smarter decisions when running its searches.”
Kyler says InquiryIQ was conceived as a way to test pieces of information an investigator had already identified as potentially relevant. “We looked at it in the form of trying to put together a proposition and then invalidating a proposition,” he says. The system would take a “factoid,” run searches around it, and ask, as Kyler puts it, “Is this related or not?”
The code WIRED reviewed describes InquiryIQ as able to run web and image searches, browse web pages, and use face recognition on photographs it encounters. As the system searches from information supplied by the investigator, it is designed to build what Clearview calls a “Candidate Graph” of possible identities and associates, while filling out the subject’s profile with possible addresses, phone numbers, employers, social media accounts, arrest history, and aliases.
Clearview is not the first company to automate the process of trawling publicly available information. Other intelligence platforms sold to law enforcement, including ShadowDragon’s SocialNet, Penlink’s Tangles, and Fivecast, help investigators uncover aliases and associates and map a person’s digital footprint.
Andrew Guthrie Ferguson, a George Washington University law professor who studies AI and policing, describes this kind of automated investigation as “digital rummaging.” “They're basically going to create a profile of you based on all of the random digital clues you left on the internet,” he explains. In practice, that means gathering scattered bits of information that once lived in separate places but were difficult to connect.
Woodrow Hartzog, a Boston University privacy scholar, argues that the labor of investigative work once served as a practical check on surveillance. By vastly reducing the labor required to investigate a person, tools like InquiryIQ also eliminate those checks. “The privacy protections we have in place right now were mainly built in a world that assumed a certain amount of friction in the ability of governments to collect information about people,” he says. “There are a lot of rules we never had just because we never needed them—because there were these practical barriers to following everyone around.”
When an InquiryIQ search is finished, the interface is designed to present the officer with the identities, connections, and other details the system has surfaced, which the officer can accept or reject before they are added to the profile. Clearview warns that automatically generated demographic, social media, and arrest data “may or may not be accurate.” Before accepting any finding, the officer must attest that they independently verified it.
Kyler says that human review is central to Clearview’s design. The system is meant to surface possible leads, not determine what is true, he says. “That’s the job of an analyst—to evaluate what’s true, what’s not; what’s noise, what’s reality.”
“A human in the loop is a little bit of a cold comfort,” Hartzog says. Over time, investigators can defer to automated systems until the person checking the machine becomes “a sort of rubber stamp.”
For example, in United States v. Sant, a Minnesota case involving undercover Homeland Security Investigations agents and surveillance of political activists, defense lawyers obtained a Clearview report drawing matches from roughly 15 years of protest photography. Every result was stamped "Accepted by Guy Gino," including one Clearview itself labeled "A Less Likely Result"—which, the report's footnote notes, can only be exported if a user accepts it. Defense attorneys allege the report swept in photos of an entirely different man, his pregnant wife, and young daughter. (There is no evidence InquiryIQ was used in the case.)
In the prototype WIRED reviewed, what InquiryIQ returned could vary depending on which model was selected. Its interface includes a control for choosing the model that runs the research and lists xAI and Amazon Bedrock, a platform for accessing models built by other companies.
In 2025, what xAI said was an unauthorized change to Grok’s system prompt caused the chatbot to inject claims about a supposed “white genocide” in South Africa into unrelated conversations. Less than a month later, after another change to its instructions, Grok produced antisemitic posts and praise for Adolf Hitler. SpaceXAI did not respond to a request for comment.
“A hallucination-prone chatbot would not be trusted as an informant under any other regular circumstances,” says Michael Price, litigation director of the National Association of Criminal Defense Lawyers’ Fourth Amendment Center, referring specifically to information police rely on to establish probable cause in court. “This person just told me to go eat rocks and drink bleach. And they’re going to be the basis for probable cause?”
Price, who helped litigate Chatrie v. United States, the landmark Supreme Court case over geofence warrants, argues that concerns around the reliability of generative AI tools extends beyond Grok. AI models are trained on material pulled from the internet, including, he says, “uninformed posts, conspiracy theories, all of the prejudices that sadly seem to go along with the internet.”
Kyler says the apparent model selector is for internal testing and not so police could choose which model to use for their research. He insists that InquiryIQ’s output, like everything Clearview surfaces, would be treated as information for investigators to evaluate, with human analysts responsible for deciding what constitutes a real lead. The company’s interest is to “help generate a lead that leads to ground truth,” he says. “That leads to reality.”
Clearview says it is continuously evaluating a wide variety of available large language models and has not assessed the suitability of any of them for InquiryIQ.
In a statement, Amazon says that AWS is not involved in the development of InquiryIQ and that customers are responsible for ensuring they comply with the company’s terms and policies, including our acceptable use policy and responsible AI policy, as well as any third-party provider's terms. The company also stated that it does not have service-specific terms that prevent law enforcement from using Amazon Bedrock.
In spite of the risks of tools like InquiryIQ, Ferguson sees potential upside to AI-assisted investigations. They could, for example, leave a clearer record of how police arrived at a suspect than traditional detective work does. Officers routinely search databases, discard leads, and follow hunches without documenting every step, he explains. An AI system that preserved its prompts, searches, model choices, and investigative paths could make that process easier to audit.
“It’s a silver lining in an otherwise potentially fraught and disruptive change,” Ferguson says.
Ferguson’s optimism is tempered, however, by 16 years of watching police technology get deployed before agencies work out how it should be governed. “I’m a cynic because I’ve seen this happen every single time,” Ferguson says. “It’s another new technology, the same playbook.”
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