Evaluation access & setup guide

VisionaryAI Suite is a local-first AI system that helps you analyse, tag, describe, transcribe, and organise media files on your own machine. The aim is simple: make large libraries searchable, understandable, and structured—so teams can find what they need without drowning in filenames and folders alone.

The platform can work with:

• Images, video, and audio
• Embedded and companion metadata
• AI-generated descriptions and tags
• Transcriptions where supported
• Semantic search across generated knowledge—not only filenames
• Structured .vtag sidecar metadata you can keep beside originals
• Workflows that stay local-first where your configuration allows

Fusion

Instead of relying on a single model output, Fusion combines multiple AI signals—such as visual cues, captions, tags, OCR text, and optional LLM reasoning—into a richer, more coherent interpretation of each media file. It is designed to make results feel more “complete” than isolated model runs, while still being something you can inspect and validate.

Deep Analysis

Deep Analysis goes further: using the signals already extracted, the system can attempt a more human-readable summary and interpretation of what is in the file. This supports exploration and documentation workflows—but it is not a guarantee of perfection. For archival, editorial, or legal-adjacent use, treat outputs as assisted drafts that benefit from professional review.

Semantic Memory is one of VisionaryAI’s most important ideas. It helps the application treat your archive as more than a pile of paths: it can connect generated metadata, descriptions, tags, and structured .vtag records so you can search by meaning, not only by filename. For how this contrasts with typical desktop search tools, see AI-powered file search.

In plain terms: “Semantic Memory allows VisionaryAI to understand your archive beyond filenames. It can search through generated metadata, descriptions, tags and structured .vtag files, making it possible to find relevant media even when you do not remember the exact filename.”

• Search by meaning, not only by filename
• Build a searchable layer on top of generated metadata
• Reuse existing .vtag files where you already have them
• Prepare archives for future AI-assisted workflows with portable structure

.vtag is VisionaryAI’s structured metadata format—typically stored as a sidecar file next to your original media, not unlike how an .xmp file travels with an image. It is how the suite keeps AI-generated knowledge portable, inspectable, and reusable.

A .vtag record can hold (depending on analysis settings):

• Tags, descriptions, transcriptions
• Object and scene information
• Timeline-oriented cues where applicable
• AI analysis results and semantic fields
• Room to grow for future forensic and intelligence-oriented layers

“.vtag is designed to make AI-generated metadata portable, searchable and reusable across the VisionaryAI ecosystem.” Learn more on the dedicated .vtag page.

The Trial version is limited to 20 analyses per device. It is designed to give you a hands-on preview of the real platform—Fusion, Deep Analysis, Semantic Memory, and .vtag workflows—before you move into a pilot, closed beta (with a longer evaluation key), or a commercial arrangement.

If you outgrow the cap quickly, contact us or explore closed beta or contact us for paths with longer evaluation.

VisionaryAI Trial 1.5.3 is a strong fit if you:

• Work as a media professional, archivist, researcher, or journalist
• Produce or manage video or audio at scale
• Are an AI-curious creator who wants real desktop software, not a slideshow demo
• Maintain large image, video, or audio libraries inside your organisation
• Want to test local-first AI analysis before committing to cloud-centric tools
• Are exploring smarter metadata workflows ahead of catalog, MAM, or archive upgrades

VisionaryAI is built around a local-first philosophy. The goal is to help you analyse and structure sensitive media while keeping as much of the workflow as possible on your own machine—or inside environments you control—especially when you configure local models.

Depending on your choices, some advanced steps may still use external AI services if you enable them. Think of the trial as flexible: you can bias toward offline-capable stacks where supported, or blend in online models when that matches your policy. If you need a strictly air-gapped posture, treat that as a conversation with our team—we can help you map what is realistic for your configuration.

Public Trial 1.5.3 is about deep media understanding into how VisionaryAI Suite processes your media—with stronger multimodal analysis, OCR, semantic memory and richer .vtag metadata.

• Benchmark discipline anchored in real latency and media identity metadata
• Regression awareness when hardware, drivers or models shift
• Whisper tuning through optimization profiles and adaptive recommendations
• Fusion flexibility including Smart Whisper paths and visual-first video analysis
• Operational documentation via TXT, JSON, HTML, CSV and PNG exports
• Still the same Semantic Memory / .vtag story—now with sharper diagnostics
• An honest waypoint for enterprise pilots that need measurable AI pipelines

Some AI features need models to be downloaded or configured before first use. That is intentional: it gives you more control over weights, VRAM use, and whether work stays local. First launches may take longer while caches fill—plan enough disk and bandwidth.

We are expanding onboarding guidance and walkthrough video material so you are never guessing alone. Until then, start with How it works and the Watch & learn clips on the gallery, and read the notes bundled with your download.

Deep hardware detail lives on System requirements; storage expectations are summarised below.

Evaluation builds are distributed to approved applicants only—not as a public download. After approval, you receive a secure link and signed license.json. Need access? Submit an application or contact us.

Application and AI models

The download and install footprint is large and depends on which AI models you select and download. A minimal install is smaller; a full set of high-capability models can reach many gigabytes to tens of gigabytes or more on disk. Plan free space on a fast drive (NVMe or SSD) for both the app and the model cache you enable.

Your media and .vtag output

Local AI analysis of video and high-resolution stills can require a significant amount of additional storage for working data, model caches, and structured metadata written beside your files. A serious archive may need terabytes of free space in total. Scale hardware to your collection—this site does not give a one-number guarantee.