For years, the organizations working hardest to locate missing children had access to technology. That was never really the problem. What was happening? Tools didn't talk to each other. Investigators worked across disconnected systems, manually managing thousands of images, cross-referencing spreadsheets, and running searches that could take hours to produce a single potential match. Leads piled up faster than anyone could follow them. The data existed, but not the capacity to act on it.
Sex trafficking investigations face a particular version of this challenge. Children appear in escort ads and across social media platforms, sometimes for a matter of hours before moving on. The window to act is narrow. The volume of material to search is enormous. And every hour spent on manual work is an hour a child isn't being found.
Matt Shelton built Freed People to close that gap. What he needed to accomplish this was technology that actually worked like investigations do.
The mission behind the platform
Freed People is a nonprofit focused on finding missing children in the United States, with a specific focus on victims of sex trafficking. For four years, Shelton and his team have partnered with technology experts to build tools that analyze social media, escort advertisements, and other digital sources — helping locate children and connect them with restorative care teams for safe placement and ongoing support.
The work is time-sensitive in a way that most enterprises never experience. Speed isn't a competitive advantage. It determines whether a child comes home.
When Shelton talks about VISION, the AI-powered case management and image-matching platform Unframe created for Freed People, the shift he describes is so much more than dashboards or efficiency metrics. It's about confidence: going from hoping to knowing.
"When a missing child appears in an ad somewhere across the country," he says, "we don't hope we find them. We already have."
- Matt Shelton, Founder, Freed People
What changed and why it matters
Before VISION, investigators managed the work across disconnected tools and spreadsheets. Each potential image match took hours.
VISION automates the most time-consuming parts of that workflow. Images are ingested and matched automatically. Potential matches are scored, queued, and surfaced for review. Investigators stay in control at every step — accepting, rejecting, or flagging matches with full human-in-the-loop verification — but they spend their time on judgment calls, not manual search.
A key aspect is the Case Network Graph, which surfaces hidden connections across a case: phone numbers, aliases, locations, ads, images, and known contacts, all in one view. Relationships that once took hours of cross-referencing to find are now visible immediately.
The platform runs on a secure, on-premises AWS deployment. No data leaves the environment.
AI that serves the people doing the work
What stands out about the Freed People story isn't any single feature of VISION. It's what becomes possible when technology is built around the actual shape of the work — not retrofitted onto it.
Investigators aren't switching between systems or waiting on back-office turnaround. The platform meets them where they are, with the information they need, at the moment they need it. That's what drives adoption. And in this case, adoption directly translates to outcomes: more cases investigated, more leads followed, more children found.
The team at Unframe took the time to understand the mission first and work together on a tailored solution. The result is a platform that reflects the urgency and precision that Freed People's work demands — and that gives investigators something they didn't have before: the confidence that when a child surfaces somewhere in the country, they'll already be ahead of it.
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