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Why We Built Oceanir

Oceanir Team·Oct 15, 2025
XGitHub
Why We Built Oceanir

This started with a simple problem. People had photos and videos, but no reliable way to tell where they were taken without doing hours of manual work.

The core problem

In real investigations, where was this taken? is usually the first question and the slowest one to answer. Teams still compare skylines, road markings, and map tiles by hand. Sometimes it works. Sometimes it burns hours and goes nowhere.

"Upload one image. Get a ranked location answer you can review, not just guess at."

Visual reasoning

Oceanir runs on Orca, our city-scale geo-estimation model. It looks at scene clues like architecture, road furniture, signage, vegetation, and lighting, then returns ranked location candidates with confidence.

System capabilities

Area
Description
Ingestion
Upload high-resolution images from social posts, archives, camera exports, or field submissions.
Analysis
Orca analyzes architecture, signage, and environmental cues to estimate likely capture locations.
Confidence
Every result includes a confidence score so teams can decide what to trust and what to review further.
Privacy
Image data is encrypted in transit and processing logs can be purged based on your retention settings.

Who we serve

We built this for investigators, journalists, and analysts who need location answers fast and still need to defend the result.

The goal is simple: shorter workflows, clearer evidence, and fewer dead ends.

Try it on a real image

Run one photo through the platform and review the ranked candidates and confidence output.

Launch Grid→

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