Read how a private investigator uses AI vision models, geolocation, crypto tracing, and OSINT techniques to solve a high-stakes tech disappearance case.
The rain was drumming a steady, relentless rhythm against the tall glass windows of my office when Sarah Chen walked in. She didn’t look like a typical client. Dressed in a sharp blazer, carrying a tablet instead of a manila folder, she sat down and skipped the pleasantries.
”My brother, Marcus, went missing four days ago,” she said, sliding the tablet across the desk. “The police say he’s an adult who probably just needed a break. But Marcus is a lead developer at a high-frequency trading firm. He doesn’t just disappear.”
I picked up the tablet. On screen was a photo of Marcus: late 30s, athletic build, wearing a faded marathon t-shirt.
”Where do we start?” she asked.
”Where the digital trail begins,” I replied, booting up my system and initializing my local AI analysis workspace. “Traditional surveillance takes time. Combining OSINT and AI in private investigation takes seconds.”
1. Geolocation and AI Image Extraction
The last known trace of Marcus was a casual photo posted to his public social media account 96 hours ago—a coffee cup sitting on a wooden table, overlooking a bustling city street. No location tag. No text caption.
I fed the high-resolution image into an AI multi-modal vision model trained on geospatial feature extraction and landmark identification.
[ANALYSIS RUNNING…]
Architectural style: Mid-century modern brickwork with distinctive terracotta window ledges.
Background element: Partial sign reading “…& SONS”.
Environmental marker: Unique hexagonal cobblestone pattern on pavement.
Shadow angle / Time analysis: Sun angle (~42 degrees), shadows pointing NW -> Timestamp ~08:45 AM local time.
Cross-referencing visual database…
MATCH FOUND: 41.8819° N, 87.6278° W (West Loop, Chicago, IL).
“He was at Kavanagh & Sons Coffee on Fulton Market,” I said, pointing to the screen. “And look at the reflection in his sunglasses.”
I zoomed in on the polarized lenses in the photo, ran an image-enhancement algorithm, and fed the reflection into the model. It isolated the license plate of a dark sedan idling across the street: 7YZ-882.
2. Unmasking the Vehicle via Digital Forensics
Using open-source vehicle registry databases and automated web scrapers, I ran the plate.
”Registered to a short-term rental fleet,” Sarah noted, leaning in.
I instructed an LLM agent to crawl local traffic feed archives, public social media posts tagged in the West Loop area around 8:45 AM four days ago, and public security feeds. The AI processed thousands of text snippets and image metadata points in seconds, filtering out the noise.
”Got a hit,” I said. An automated scraper flagged a post from a local bike commuter who had complained on X (formerly Twitter) about a dark sedan blocking the bike lane outside Kavanagh & Sons at 8:50 AM. Attached was a dashcam snippet.
We watched the clip. Marcus walked out of the coffee shop, approached the dark sedan, spoke briefly through the window, and voluntarily got into the back seat. The car drove off toward I-90 West.
”He wasn’t taken by force,” Sarah observed, her voice tight. “He knew whoever was in that car.”
3. Following the Crypto and Source Code Trail
”Marcus left something behind on his public GitHub repository the night before he vanished,” Sarah remembered suddenly. “A commit to a private-turned-public repo. I thought it was just work.”
I pulled up Marcus’s GitHub activity. He had committed a sequence of encrypted code blocks at 2:00 AM. I passed the repository data to an AI code analyzer to search for hidden steganographic data or embedded metadata.
“It’s a dead man’s switch,” I said.
Using blockchain explorers, we mapped the transaction history of the Ethereum wallet. The AI graph engine traced the flow of funds. Half an hour after Marcus got into the sedan, the wallet transferred 5 ETH to a smart contract linked to a decentralized domain name: Oakhaven-Logistics.eth.
4. Physical Convergence via Satellite Intelligence
I fed Oakhaven-Logistics.eth into an OSINT domain-reconstruction tool. Corporate entity registration records buried in state databases revealed a physical link: an abandoned industrial park near O’Hare Airport.
I cross-referenced satellite imagery from the past 48 hours using open SAR (Synthetic Aperture Radar) data to detect vehicle presence at the site.
The AI flagged high radar reflectance—indicating metal structures and parked vehicles—at Warehouse 4 within the complex, specifically showing a vehicle matching the dimensions of the dark sedan parked inside an open bay.
”He’s there,” Sarah whispered.
Case Closed: The Power of AI in Cyber Investigations
We didn’t rush in. I dispatched the coordinates and the AI-compiled dossier—complete with vehicle plate, time-stamped dashcam evidence, and blockchain transaction trail—directly to the local precinct’s detective unit.
Two hours later, my phone rang.
Marcus had been found inside the warehouse alongside his company’s proprietary trading server hardware. A rogue former executive had lured him there to force him to hand over encryption keys to their trading algorithms. Marcus had stalled for four days, knowing his digital breadcrumbs would lead an investigator to him.
Sarah looked at the monitor, where the web of data nodes, satellite maps, and code scripts still glowed.
”You solved in four hours what would have taken weeks,” she said.
”I just asked the right questions,” I replied, closing the terminal. “The data was already telling the story.”
Key Takeaways from this OSINT Case
- Multi-Modal AI Vision: Used to extract geographic location coordinates from shadow angles, reflection analysis, and architectural details.
- Automated Scraping: Leveraged to correlate social media complaints with dashcam footage in real time.
- Blockchain Forensics: Traced smart contract transfers to uncover hidden domain registrations and corporate entities.
- Satellite Radar (SAR) Analysis: Verified physical targets without triggering suspect alerts on the ground.
1. Final Narrative Wrap-Up & Author Byline
End the story with a memorable takeaway, then immediately transition to a professional author block to establish E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) for search engines.
“I just asked the right questions,” I replied, closing the terminal. “The data was already telling the story.”
About the Author
Kyles Investigation provides specialized skip tracing, background research, and digital OSINT solutions. Operated by a licensed Private Investigator, we combine advanced digital forensics, open-source intelligence, and modern analytics to uncover critical facts nationwide.
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