You know the clip exists. You shot it, or someone did. It’s somewhere in the archive, behind metadata that was never fully logged, on a project whose lead has since moved on. You’re going to spend the next hour looking for it, or possibly the next day.
This is not a storage problem. Media teams have storage. It’s a discoverability problem, and it has a measurable cost.
Assistant editors, and asset managers spend an average of 55% of their working time on tasks that have nothing to do with creative work: searching, logging, syncing, and preparing material before it reaches the edit. For a team working a normal production year, that translates to roughly three to four weeks spent looking for things that already exist.
FLOW AI is EditShare’s response to that problem. It is an analytical AI module built directly into FLOW, EditShare’s media asset management platform, and it is designed to make the gap between finding and delivering footage short enough to matter.
Not All AI in Media Is the Same
When people talk about AI in media and entertainment, they are not always talking about the same thing. Generative AI, the category that draws the most headlines, creates video, audio, and images from scratch. Editorial assist tools, integrated into applications like Adobe Premiere Pro and DaVinci Resolve, handle cleanup tasks: background removal, audio enhancement, object removal. Rough-cut tools attempt to identify story beats in unscripted material and assemble editable sequences for the editor to work from.
FLOW AI sits in a fourth category: analytical AI. It does not generate content. It does not make editorial decisions. It understands the footage you already have, indexes it thoroughly on ingest, and makes it searchable in ways that manual logging rarely achieves in practice.
This is the least visually dramatic of the four categories. It is also the one with the most direct path to a justifiable business case.
Why Integration Is the Design Choice That Matters
EditShare has attempted AI-assisted media analysis before, and the earlier version did not find traction. The approach at the time required bundling media files and sending them to an external service for processing, a workflow that introduced delays of one to two times the running length of each file, raised security concerns around unreleased content, and delivered results into a separate interface disconnected from the environment where editors, producers, and asset managers actually worked.
The lesson was straightforward: AI only works in a media workflow when it fits the way media teams work.
FLOW AI is built on three principles that came directly from that experience.
In-place Processing
Analysis happens on premise. Media files do not leave the facility. For a 60-minute asset, the current processing time is approximately 10 minutes, a significant improvement over the two hours required when files had to travel off-site. The original media stays where it is; only the index is built and stored.
Integrated workflow
Search results, detected moments, and analysis data surface inside FLOW, the same interface where teams already manage their media, create subclips, build sequences, and route material for review. If AI results live in a separate application, teams have another system to check and another place where work gets disconnected. FLOW AI is designed so that finding a clip and doing something with it are steps in the same session, not steps in two different tools.
Predictable Pricing
FLOW AI is licensed on an annual basis. Once the AI server is in place and the module is licensed, teams can run analysis across as much material as the hardware supports without accumulating per-query costs. The ability to control AI spend, rather than discovering at the end of the quarter what the meter has accumulated, was one of the clearest pieces of feedback EditShare gathered while developing the product.
What FLOW AI Detects
On ingest, FLOW AI analyses footage and builds an index of what it finds: objects and scenes, faces, on-screen text, speech and audio events, and logos. That index is what makes subsequent searches fast; the hard work happens once, at ingest, so that retrieval is near-instant.
Search is semantic rather than literal. If an editor searches for “sparks,” the system returns footage containing relevant material even if the original logging used a different language or no language at all. The analysis of the actual frames provides the metadata when manual tagging never did.
Face recognition works across all appearances in the footage and distinguishes between a person’s face on camera and their name appearing as on-screen text in a credit sequence, two different detection types that can be toggled independently during review.
Logo recognition is trained from a single reference image. The system identifies every frame where that logo appears as a graphic, and separately recognises the associated vehicle or object in context (its make, model, and the conditions it appears in) without relying on the logo graphic being visible. Search terms like “SUV” or “Cherokee” will surface the same content because the scene description captures what the system understood, not just what was tagged.
Adding a new recognition target (a logo, a face, a brand name) does not require reprocessing archived material. The initial index has already flagged unknown logos and faces. Assigning an identity applies across all previously indexed content immediately.
Transcription is included. The current release supports 12 languages, with audio detections surfaced as media highlights alongside visual results in the same timeline view.
From Search to Delivery
The practical value shows up in the gap between finding and delivering.
In a post-production scenario: a facility is cutting a trailer from a feature film and needs two things, a sparks sequence and footage of the lead actor. Both searches return results in seconds. Detected moments appear as media highlights linked directly to the relevant frames in the source material. Those highlights drag into a bin as subclips, with no manual in and out points required. A rough assembly sequence is ready to hand off to the editorial department, or send to MediaSilo for client review, in the time it would previously have taken just to locate the first clip.
In a brand partnership scenario: a search for a sponsor’s name returns trademark detections (frames where the logo appears as a graphic) alongside scene detections describing the vehicle in context. The completed sequence can be assigned directly to a colleague inside EditShare One, with review notes appearing as timeline markers on specific clips. The full loop from detection to internal review runs inside FLOW, without an email chain or a shared spreadsheet tracking who has seen what.
Cut-downs that previously required a day of manual logging and timecode notes can now be assembled in minutes. At the scale of a production schedule, that compression changes what is economically worth making, and opens the door to treating AI-assisted editorial prep as a billable service.
Available Now
FLOW AI was released on 1 July 2026 as part of FLOW 26.2. It is an additional module requiring a FLOW license, a separate FLOW AI license, and a dedicated AI server with GPU. An introductory promotion on the AI server is currently available.
FLOW 26.2 also includes drag-and-drop workflow improvements, enhanced metadata display, and zoom controls. A FLOW panel for Adobe Premiere Pro and DaVinci Resolve, bringing the same AI search and media highlights into the NLE environment, is in development and expected later in 2026.
If you want to explore what FLOW AI could do for your facility or team, speak to your EditShare regional sales manager or book a demonstration.
