`.vtag`: the metadata foundation of VisionaryAI Suite

.vtag is the metadata foundation of VisionaryAI Suite. It stores AI generated knowledge next to the original media file, making analysis portable, structured and ready for future workflows. The same structure feeds semantic file search and catalog-style indexing — so you can search your archives by what is in them, not just filenames.

What is `.vtag`?

A .vtag file is a sidecar document stored next to a media file (same basename, different extension). It holds structured results of AI and parsing steps—typically JSON—so any tool that can read a text file can participate: backup, Git, find/grep, custom scripts, and catalog software with indexing rules.

Why sidecar metadata matters

Portability: copy a folder, and the intelligence travels with the media.
Provenance: you can record which model produced which field, for audit and reproducibility as models change.
Interoperability: XMP, EXIF, and database-only solutions each have a role; a sidecar can remain the neutral interchange for rich AI output that outgrows a few embedded fields.

Example (illustrative JSON)

The real schema is defined by the product and may evolve. Treat this as a readability example, not a normative spec:

{
  "vtag_version": "1.0",
  "media": "interview_0445.mp4",
  "summary": "Outdoor interview, traffic noise, two speakers",
  "tags": ["interview", "outdoor", "car_passes"],
  "objects": [
    { "label": "person", "t": 12.4, "confidence": 0.89 }
  ],
  "caption": { "text": "Reporter with microphone, building facade behind", "confidence": 0.78 },
  "transcript": { "text": "…we filed the request under the public record law…" },
  "speakers": [ { "id": "spk1", "label": "Reporter" } ],
  "timeline": [ { "t0": 0, "t1": 3.1, "event": "siren" } ],
  "confidence": { "overall": 0.81 },
  "engine": { "name": "VisionaryAI Suite", "build": "0.0.0" }
}

What can be stored in `.vtag`

Version 1.5.3 enriches what can flow into each sidecar. A .vtag is designed to be portable, structured and future ready — holding AI-generated knowledge beside the original file:

AI-generated descriptions

Natural-language summaries and scene narratives for quick triage and search cards.

OCR text

Visible text extracted from documents, screenshots, posters and on-screen content — searchable alongside visual analysis.

Transcriptions

Speech-to-text from audio and video for keyword and phrase access across long recordings.

Timeline data

Points or ranges for what happens when — grounding search in the temporal structure of the file.

Detected objects

People, objects and regions that ground search in the visual structure of the asset.

Speaker information

Roles or diarised identities when the workflow supports it.

Visual findings

Frame-grounded observations and scene evidence from multimodal analysis.

Semantic summaries

Higher-level interpretations that connect multiple signals into coherent context.

Deeper AI interpretation

Fused multimodal understanding — vision, speech, OCR and metadata combined on the timeline.

Confidence scores

So humans and automations can down-rank low-trust inferences without deleting them.

Engine metadata

Model names, versions, and pipeline identifiers for your technical records.

Open metadata instead of a locked-in database

A database inside a single product can be excellent for that product and still be the wrong long-term archive contract if the vendor changes terms or the project ends. A sidecar you own is boring technology—in the best way. You can re-index it tomorrow in another system without a migration project that re-runs all inference on day one (unless you choose to, for a new model generation).

How `.vtag` can work with XMP and catalogs

Many workflows keep XMP in images and database rows in a DAM, while video and audio often carry less in embedded metadata. A .vtag can bridge that gap: one rich JSON for modern AI output, while you still selectively map into XMP or database columns where your tool chain expects them. The same sidecar can feed NeoFinder indexing in documented setups—without replacing how each tool stores its own cache.

Future of `.vtag`

As an open AI metadata pattern, the format can grow with the ecosystem: new fields, shared schemas between tools, and community tooling around validation and display. The direction is a portable, inspectable, versionable record of what machines inferred about your media—without pretending that a single file replaces legal records or human cataloguing policy.

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.vtag: the metadata foundation of VisionaryAI Suite

What is .vtag?