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What Oceanir is, and what it isn't

Oceanir verifies where an image was taken.

It is a tool for visual evidence: places, properties, vehicles, and location claims. It returns ranked context for a person to review, with the reasoning shown in full. It is not a way to identify, find, or watch people. This page is the line we hold, in plain terms.

What it is

A visual evidence tool for location, built to be checked.

You give Oceanir a photo or a video frame. It works out where it was most likely taken, shows you why, ranks the alternatives, and flags what argues against the top answer. A careful reviewer can take that and reach a conclusion they can stand behind.

Find where an image was taken

Give Oceanir a photo or a video frame and it works out the most likely location from the visible scene: signage, architecture, road markings, vegetation, and the other cues a careful analyst would read.

Show the evidence, not just a pin

Every result comes with the visual reasons behind it, a ranked set of alternative candidates, and the points that contradict the top answer. The judgment stays with the person reviewing it.

Work from the picture alone

Oceanir reads the content of the image. It does not rely on EXIF GPS or any metadata shortcut, so it works even when a file has been stripped, screenshotted, or re-saved.

Handle video

Upload a clip and Oceanir samples frames across it, so a moving source can be reviewed the same way as a still.

Scale from a quick look to a defensible record

Three depth levels run from a fast surface read to a full forensic bundle. Teams can export an evidence record and keep an audit trail of who reviewed what.

Keep the image private

Oceanir does not store the images you upload. Analysis happens on the content, and the content is not retained.

What it isn't

The things Oceanir will not do, by design.

These are not features we have not built yet. They are choices. Oceanir places images, not people, and it is meant to be verified rather than obeyed.

Not face recognition or biometrics

Oceanir reads scenes, not people. It does not identify, match, or measure faces or other biometric features.

Not identity resolution or people-search

It answers where a photo was taken. It will not tell you who is in it, who took it, or who owns anything in the frame.

Not a way to locate or track a person

Oceanir places an image, not an individual. There is no capability to follow, monitor, or find a private person.

Not a surveillance system

There is no live monitoring and no persistent watch on any subject. Each analysis is a one-time review of material you provide.

Not an oracle

A result is ranked context with a confidence score for a human to verify, never a verdict delivered as certainty. When the scene does not support a confident call, Oceanir says so and ranks alternatives instead of guessing.

How it is meant to be used

01

Human in the loop

Oceanir informs a decision. It does not make one. The product is built to be checked, not trusted blindly.

02

Defensible by default

Results carry their evidence so they hold up when someone pushes back. A claim you cannot show is a claim you cannot use.

03

Honest about uncertainty

A confident wrong answer is worse than an uncertain one. Oceanir is tuned to know when it does not know.

04

Privacy first

No stored images, no metadata harvesting, no dependence on a consumer surveillance stack. The work is done from the picture and nothing else.

Who it is built for

  • Journalists and investigators verifying footage and sources
  • Legal and compliance teams documenting evidence
  • Insurance reviewers checking claimed loss locations
  • Security and risk teams assessing where an image was captured
  • Researchers and analysts working from open-source media

Some access is limited on purpose. Product access, exports, API use, and any vehicle-related review workflow may be gated by plan, workspace type, business verification, and safety review. If a use case sits near the boundaries above, it gets a closer look before it is enabled.

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Notes from the verification desk. What we're learning about reading places from pixels. Occasional, no noise.

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