How to Automate Rough Cuts for Interview Videos: A Professional Editor's Guide

I have edited interview-driven video professionally for over a decade. Corporate brand films, documentaries, testimonial campaigns, event coverage. The creative work is why I got into this career. The rough cut is what makes me question it.

If you edit interviews for a living, you already know the math. A typical corporate project with three to four hours of raw footage takes 12 or more hours of pre-editing before you touch the creative cut. You are scrubbing through footage you already watched on set. You are logging clips you already know the content of. You are building an assembly that follows a story structure you already have in your head.

At standard industry rates, that is over $1,500 in billable time spent on work that requires endurance, not creativity.

I am not the only one frustrated by this. When my co-founder Bradley and I started reaching out to professional editors about what we were building at Threadline Studio, our cold outreach response rate hit 15%. The industry average for B2B cold outreach is under 2%. Editors do not need to be convinced this problem exists. They have lived it.

So what is actually working in 2026 to automate this phase of post-production? I have tested the tools, talked to dozens of working editors, and built one myself. Here is what I have learned.

Why the Rough Cut Is So Hard to Automate

Before getting into specific tools, it helps to understand why this problem has resisted automation for so long.

The rough cut is not a mechanical task. It is an editorial one. When a skilled editor watches raw interview footage, they are not just listening to words. They are tracking vocal delivery: the moment a subject leans into a point, the pause that gives a statement weight, the shift in energy that signals the emotional core of an answer. They are mapping those moments against a narrative structure in their head: where does this story begin, where does it build, where does it resolve?

Most AI tools approach this problem by transcribing the audio and then making cuts based on the text. That is a reasonable starting point, but it misses something fundamental. The difference between a mediocre rough cut and a good one is almost never about which words were selected. It is about timing, delivery, and narrative architecture.

This is why professional editors have been skeptical of AI editing tools. The tools that have launched over the past two years largely treat video like a text document. They find keywords, pull clips, and export. The result feels like what it is: an algorithm that read a transcript, not an editor who watched footage.

The Manual Workflow Most Editors Still Use

Before comparing automated approaches, it is worth documenting the workflow that AI tools are trying to replace. Most professional editors working on interview content follow some version of this process.

First, ingest and organize. Import raw footage, create bins, label files by camera angle, interview subject, and date. For a multi-camera shoot, this alone can take an hour.

Second, review and log. Watch all footage at 1.5 to 2x speed, marking selects and noting timecodes for strong moments. For three hours of raw footage, expect four to six hours here.

Third, build selects. Pull marked clips into a selects sequence, roughly ordered by topic or narrative beat.

Fourth, assemble the rough cut. Arrange selects into a narrative structure with a beginning, middle, and end. Trim for pacing. This is where the editorial judgment lives.

Fifth, export and hand off. Deliver the rough cut to the producer or client for notes, then iterate.

Steps one through three are where 70% or more of the time goes. They are also where AI has the most room to help, because they are primarily organizational and analytical, not creative.

Four Approaches to Automating the Rough Cut

The tools available in 2026 fall into four distinct categories based on how they process footage. Understanding these differences matters because the approach determines the quality and usefulness of the output.

Transcript-Based Editing

This is the most established approach. Tools like Descript pioneered the idea of editing video by editing its transcript. You see the spoken words as text, highlight what you want to keep, delete what you do not, and the video follows.

Descript has evolved significantly with its Underlord AI features, which can now remove filler words, suggest cuts, and generate summaries. It is a powerful tool for certain workflows, especially podcast editing and content repurposing.

For professional rough cuts on interview-driven content, though, transcript-based editing has a ceiling. The cuts happen at word boundaries, not at natural editorial points. The tool has no concept of delivery quality, so it cannot distinguish between a subject saying something important with conviction versus saying it flatly. And because you are working inside Descript's editor rather than your NLE, there is a workflow friction cost: you eventually have to move the project to Premiere, Resolve, or Final Cut for the real edit.

Descript's pricing recently shifted to a credit-based model, which has frustrated some long-time users. Plans start around $24 per month, but heavy users working with long-form content report significantly higher costs.

Prompt-Based Rough Cuts

Eddie AI, built by the team behind Simon Says, takes a different approach. You upload footage, and then interact with the tool through natural language prompts. You can ask Eddie to find specific topics, create a rough cut following a particular structure, or pull the best soundbites on a subject.

Eddie supports multi-camera footage, exports XML to Premiere Pro, DaVinci Resolve, Final Cut Pro, and Avid, and offers "Rough Cut Frameworks" that let you define story structures before the AI assembles the edit. It has earned coverage from No Film School, RedShark News, and CineD, making it one of the most visible tools in this category.

The reviews from working professionals are mixed but informative. CineD described it as useful for creating rough cuts from interview footage. Early Light Media, a production company that tested it on real projects, concluded that it was more of an "interesting toy than trusted assistant" for high-pressure professional work. MASV's reviewer said he would not trust it with rough cuts of interviews yet but found value in the logging and organization features.

Eddie's pricing starts with a free tier. The Plus plan is $25 per month and the Pro plan is $100 per month.

Automated Prep and Assembly

Cutback's Selects tool focuses less on the editorial judgment of the rough cut and more on automating the prep work that precedes it. It syncs multi-camera footage automatically, organizes clips into topic-based chapters, removes silences and filler words, and exports structured project files to Premiere Pro, Final Cut, and DaVinci Resolve.

Selects claims to reduce editing prep time by 60% by handling the sorting, organizing, and reviewing that consume the first hours of every project. Their "Storyline" feature, currently in beta, uses AI to assemble story-driven rough cuts from prepped footage.

The tool is designed specifically for professional workflows, with exports that include color-coded chapters, markers, labels, and transcripts. Pricing starts at $20 per month for a Lite plan, with a Pro plan at $160 per month for heavy users.

For editors who want to keep full creative control but eliminate the organizational drudgery, this prep-focused approach has appeal. The trade-off is that you are still making the editorial decisions yourself; the tool is clearing the path rather than building the assembly.

Narrative Analysis

This is the approach we are building at Threadline Studio, so I will be transparent about my bias here. I believe it is the right approach because I built the underlying methodology manually before we ever wrote a line of code.

Narrative analysis means the AI does not just read what was said. It analyzes how it was said. Intonation shifts that signal emphasis. Pacing changes that indicate a story turn. Breath patterns that create natural edit points. These are the signals professional editors use intuitively when they make cut decisions. We are teaching an AI to recognize them.

The output is not a collection of clips or a transcript with highlights. It is a structured timeline with a narrative arc: an opening, development, emotional turns, and a resolution. The editor receives an edit-ready XML file for Premiere Pro, DaVinci Resolve, or Final Cut Pro with an intelligent first cut already assembled.

We are currently in private beta with eight pilot customers, all working professionals on real client projects. The feedback has confirmed that the approach produces output that feels qualitatively different from transcript-based cuts. One producer described the output as having a cohesion that felt like a human had cut it. An editor said it identified emotional turns in the story and directed him to exactly where he needed to focus.

The honest caveat: we are early. Threadline is not yet available to the general public, and our beta dataset is small compared to more established tools. What we have validated is that prosodic analysis produces meaningfully better editorial decisions than keyword matching alone.

Which Approach Fits Your Workflow?

There is no single best tool for every editor. The right choice depends on your specific workflow, the type of content you edit, and where in the process you need the most help.

If you primarily need transcription and text-based editing with a polished standalone interface, Descript remains the most mature option in that category. Just be aware of the pricing changes and the workflow friction of moving projects to your NLE.

If you want a conversational interface that can handle specific requests like "find the best soundbites about leadership" and you work across multiple NLEs, Eddie AI offers the broadest feature set and the most flexible interaction model.

If your bottleneck is footage organization, syncing multi-camera shoots, and getting clips chapter-organized before you start cutting, Cutback Selects is purpose-built for that prep phase.

If you want an AI that makes actual editorial decisions based on how the speaker delivers their content, not just what they say, and produces a narrative-structured first cut, that is what we are building at Threadline.

What I Tell Editors Who Ask Me About AI

The most common question I get from professional editors is some version of "should I be worried about AI replacing me?" My answer is always the same: no, but you should be interested in AI replacing the part of your job you hate.

No AI tool in 2026, including ours, produces output that a professional would deliver to a client without further editing. The creative judgment, the pacing instincts, the ability to feel whether a cut works: those remain firmly in the editor's domain.

What AI can genuinely do right now is eliminate hours of non-creative work. The scrubbing, the logging, the initial assembly. The 40% of post-production time that every editor dreads but nobody has been able to skip.

The technology is moving fast. A year ago, most of these tools did not exist or were in early alpha. By next year, the landscape will look different again. The editors who will benefit most are the ones who start experimenting now, find the tool that fits their workflow, and reclaim the time they have been spending on work that was never really editing in the first place.

Jacinto Salz is the CEO and Co-Founder of Threadline Studio, the AI assistant editor for professional video production. He is also a director and DP at OPN ROADS Media, where he has produced commercial and documentary content for over a decade. Threadline Studio is currently in private beta at threadlinestudio.io.