Post-Production AI Automation

The Machine
Behind the
Creativity

Custom AI-augmented software and automation systems that eliminate the manual grind of post-production — so your team can spend every hour on the work that actually matters.

Content Ecosystems Break
on Volume

A shoot that generates 500 clips, 1,200 photos, and 40 hours of footage doesn't need more editors — it needs a system. Manual tagging, renaming, transcoding, organizing, and exporting at scale isn't a workflow problem. It's an engineering problem.

We build the infrastructure that turns raw media chaos into structured, searchable, delivery-ready assets — automatically.

01

Manual Tagging

Hours spent manually describing, labelling, and categorizing thousands of media files per project.

02

Inconsistent Naming

Disorganized file naming conventions that break search, versioning, and downstream delivery.

03

Pipeline Bottlenecks

Sequential handoffs between ingest, review, edit, and delivery that create delays at every stage.

04

No Single Source of Truth

Scattered assets across drives, clouds, and inboxes with no unified metadata layer to query or report on.

MetaVision

AI-Augmented Post-Production Platform

MetaVision is a custom-built desktop application that ingests raw media, applies AI-driven analysis, enforces naming conventions, generates structured metadata, and delivers organized, export-ready assets — in a fraction of the time.

Computer Vision LLM Integration Batch Processing Custom Schemas Multi-format Export
MetaVision video pipeline interface
MetaVision photo pipeline interface
MetaVision settings window
MetaVision activity log window

Built Iteratively.
Deployed in Production.

Three generations of the same idea — refined through real post-production workloads until it became something production teams could depend on.

v1.0

VideoTagger

2024

The origin: a focused tool for AI-assisted video tagging and metadata generation. Built to solve one problem — getting structured metadata out of raw footage without touching every file manually.

AI Tagging Video Metadata Batch Ingest
v2.0

VideoTagger Pro

2024

Expanded to handle complex multi-format pipelines, custom tagging schemas, and configurable export profiles — designed for production teams managing high-volume content libraries.

Custom Schemas Multi-format Export Profiles Team Workflows
v3.0

MetaVision

Current
2025

The full platform: unified photo and video pipelines, LLM-augmented analysis, real-time activity logging, and a settings architecture that adapts to any content ecosystem. This is the current flagship.

LLM Integration Photo + Video Real-time Logs Platform Architecture

What the System Does

AI-Powered Tagging

Computer vision and large language model integration analyses every frame, transcribes audio, identifies subjects, scenes, and moods — and writes structured metadata without human input.

Schema-Enforced Naming

Configurable naming conventions automatically applied at ingest — client codes, project IDs, date formats, resolution tags, and version strings enforced consistently across every asset.

Parallel Batch Processing

Multi-threaded ingest and processing pipelines handle hundreds of files simultaneously — video transcoding, photo resizing, format conversion, and metadata writing in a single pass.

Unified Photo + Video Pipelines

Separate optimized pipelines for photo and video media share a unified metadata layer — enabling cross-format search, reporting, and delivery from a single interface.

Real-Time Activity Logging

Every operation — ingest, tag, rename, export — is recorded with timestamps, checksums, and status codes. Full audit trail for every asset at every stage of the pipeline.

Configurable Export Profiles

Delivery specs for every platform and client — Instagram reels, broadcast masters, web proxies, archival packages — generated automatically from a single source file per project.

0 assets/hr Processing Throughput
0 % Reduction in Manual Tagging
0 generations In Active Development
0 % custom Built for Your Workflow

Ready to Automate
Your Pipeline?

Every content ecosystem is different. Tell us what your post-production workflow looks like and we'll map out what a custom automation system would look like for your team.