Interview footage rough-cut production workbench

For

Video teams and creative agencies turning interview or long footage into rough cuts

Solves

Long interview and event footage takes hours of manual logging and assembly before a usable rough cut exists.

Delivers

Editor-approved rough cuts and clip sets linked to timeline exports

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual assembly time while keeping editorial control of the cut.

1. Ingest raw footage and transcripts.
2. Generate a first rough cut from raw footage.
3. Accept editing instructions in natural language.
4. Build multi-step workflows on a node canvas with branches and parallel variants.
5. Save and trigger reusable workflow templates.
6. Identify key topics and soundbites.
7. Use visual and audio cues for cuts, captions and b-roll suggestions.
8. Export timelines to Premiere, Resolve and Final Cut Pro.
9. Produce social and client-review output formats.
10. Show real-time analysis of footage and edit state.
11. Connect third-party applications used in the pipeline.
12. Monitor jobs on a management dashboard.
13. Generate motion graphics and dubbing with lip sync.
14. Repurpose long content into short clips and reels.
15. Iterate quickly on reviewer feedback.
16. Compare the reviewed result with the recorded baseline and value assumptions.
17. Capture corrections and named-owner approval before consequential use.
18. Export a versioned editor-approved rough cut with source references and unresolved questions.

Everything these tools do, in one app

• Rough cut generation Automatically creates an initial edited version from raw footage to save manual editing time.Found in Eddie AI, Mosaic
• Conversational editing interface Lets users give editing instructions in natural language as if talking to a person.Found in Eddie AI
• Node-based workflow canvas Provides a visual canvas for building multi-step editing workflows with branching and parallel variants.Found in Mosaic
• Customizable automation workflows Allows users to set up flexible automated processes to fit different editing needs.Found in Eddie AI 2.0, Mosaic
• Key topic identification Automatically finds and organizes important topics and soundbites from video footage.Found in Eddie AI
• Multimodal visual intelligence Uses visual and audio cues like saliency, object detection, and action detection to inform cuts, captions, and b-roll suggestions.Found in Mosaic
• Export to editing software Exports projects or timeline data to popular video editors such as Adobe Premiere, DaVinci Resolve, and Final Cut Pro.Found in Eddie AI, Mosaic
• Multiple output formats Offers various export formats suitable for social media sharing and client feedback.Found in Eddie AI
• Reusable templates and triggers Enables saving workflows as templates and running them programmatically via API or event triggers.Found in Mosaic
• Real-time data analysis Provides immediate insights and analysis to support informed decision-making.Found in Eddie AI 2.0
• Third-party application integration Connects with popular third-party applications to streamline workflows.Found in Eddie AI 2.0
• User-friendly dashboard Offers an intuitive interface for monitoring and managing AI activities.Found in Eddie AI 2.0
• Generative motion graphics and dubbing Creates motion graphics and performs dubbing or voice cloning with lip sync.Found in Mosaic
• Automated repurposing into clips Automatically turns long content into short clips and reels for social media.Found in Mosaic
• Quick iteration and feedback Allows fast refinement of edits based on user feedback.Found in Eddie AI
• Productivity improvement Effectively reduces repetitive tasks and improves overall productivity.Found in Eddie AI 2.0
• Responsive customer support Provides helpful documentation and responsive support to assist users.Found in Eddie AI 2.0

What goes in, what comes out

The workflow

1. In
   Start with

   Licensed footage, transcripts, brand rules and edit constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect licensed footage

4. 3

   Transcripts

5. 4

   Brand rules and edit constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Editor-approved rough cuts and clip sets linked to timeline exports

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed delivery format and licensed music and footage set; final editorial and compliance checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Footage intake and brief, Editable production preview, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for references, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome editor-approved rough cuts and clip sets linked to timeline exports visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Client-owned footage, authorized interviews and permitted music and stock sources. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: ingest raw footage and transcripts; generate a first rough cut from raw footage. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will video teams and creative agencies turning interview or long footage into rough cuts use it to solve "long interview and event footage takes hours of manual logging and assembly before a usable rough cut exists"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted rough cuts per editor hour and corrections after review.
4. Measure, then decide. Track accepted rough cuts per editor hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed delivery format and licensed music and footage set; final editorial and compliance checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: ingest raw footage and transcripts; generate a first rough cut from raw footage. Support the remaining modules with operator review: identify key topics and soundbites; apply visual and audio cues; export timelines. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around editor-approved rough cuts and clip sets linked to timeline exports. Retain the explicit scope boundary: One fixed delivery format and licensed music and footage set; final editorial and compliance checks remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed delivery format and licensed music and footage set; final editorial and compliance checks remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined asset package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded editor-approved rough cut set. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual assembly time while keeping editorial control of the cut. Demonstrate a concrete editor-approved rough cut set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Video teams and creative agencies professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample editor-approved rough cut set from a small authorized input set, with a transparent calculation of accepted rough cuts per editor hour and corrections after review and no promised savings.

The first 30 days

1. Week 1: interview five video teams and creative agencies turning interview or long footage into rough cuts and inspect a recent example of long interview and event footage taking hours of manual logging and assembly before a usable rough cut exists.
2. Week 2: prepare a consented or synthetic demonstration of the stated task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted rough cuts per editor hour and corrections after review, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted rough cuts per editor hour and corrections after review. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted rough cuts per editor hour and corrections after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs editor-approved rough cuts and clip sets linked to timeline exports. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved styles, production constraints and review examples, together with reliable delivery for a narrow creative niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for video teams and creative agencies turning interview or long footage into rough cuts. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Eddie AI 2.0, Eddie AI, Mosaic, freelancers, creative agencies and generic generation tools. Compare this product with the buyer's present method on accepted rough cuts per editor hour and corrections after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of editor-approved rough cuts and clip sets linked to timeline exports. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Editors approve substantive changes and publication scope. One fixed delivery format and licensed music and footage set; final editorial and compliance checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.