Most AI video tools start as generators and try to add production controls afterward. Haus Video is built the other way around. The platform already exists as a motion graphics authoring and video system — AI Studio is the layer added on top of that foundation. That means the storyboard, cast library, timeline, audio tools, and caption authoring were production-grade before AI entered the picture. It now supports three ways of working: Autopilot for one-shot generation, review at stage for inspecting and correcting the pipeline as it runs, and Guided (Map Mode) for building or reshaping a story as a visual scene diagram — all inside the same authoring environment competitors would have to rebuild from scratch.
Autopilot, Review, and Guided Map Mode let operators choose how much control they want. Scenes resolve into editable units with prompts, references, and history attached — so correction targets the wrong scene, not the whole board.
Once the board is shaped, users can generate a soundtrack, produce voice over with models like ElevenLabs, and send that timing directly into captions — with typography controls as a finishing layer, not a separate tool.
AI-generated visuals, audio, and captions are designed to flow back into the platform's motion graphics, layout, and timeline — so reels are finished inside the same authoring environment, not exported into a separate editor.
The useful framing is no longer "generate a storyboard." It is how much control the operator wants at the moment the story takes shape.
Start with a one-shot brief and let the runner seed the board automatically with scenes, prompts, references, and render jobs.
Pause inside the pipeline to inspect generated scenes, regenerate reference frames, compare history, and correct weak outputs before final stitch.
Turn the story into a visual diagram map, review blank seeded scenes first, then add, remove, or redirect scenes as if you were planning a virtual re-shoot.
Under the hood, even the fastest mode is staged. Create the script. Create or reuse characters. Create scene scripts. Create reference frames. Render image and video jobs. Then stitch the finished sequence. That is what makes correction, reuse, guided editing, and debugging possible.
script_gen step turns it into structured JSON with title, logline, characters, and scenes instead of leaving it as one free-form prompt.
cast_gen
The runner can look up an existing cast member first, or create a new one as a cast_gen job when none exists so that character becomes reusable in future boards.
image_gen and video_gen jobs individually, which makes failures and retries visible.
stitch job compiles them into a coherent output ready for review, publish, or another pass.
Script, reference frames, scene steps, compile, and publish are represented directly in the board, which makes the one-shot runner inspectable instead of magical.
When a matching cast member already exists, the storyboard can pull it in. When it does not, cast_gen creates a new recurring asset with source descriptions and variants for later boards.
Scene keyframes are explicit assets feeding the story instead of hidden intermediate outputs, and they can be reused when later shots need continuity support.
The queue shows the actual production trail: script_gen, a cast_gen job when new
recurring cast needs to be created, multiple image_gen and video_gen jobs, then the
final stitch.
Debug access to the raw script_json makes the storyboard inspectable, debuggable, and easier to
evolve than a hidden chain of prompts. The sample story document here drives a two-scene forest-to-crater sequence.
After the prompt is written, Guided Mode can create blank scenes for review before the user commits to final render. From there the board behaves like a flow diagram: generate reference frames, add more context, insert new scenes, delete weak ones, and keep the story structure visible while the prompts evolve.
Guided Mode lays out scenes as connected blocks with transitions between them, so authors can reason about the cut, spot gaps, and insert new beats before render.
A prompt like "kids zooming their bikes past a vintage car" may randomly land on the wrong car. If the real subject should be a 1960s Fiat 500 or a 1976 Monte Carlo instead of a Chevy Bel Air, the operator can upload additional references, pin them to the scene, regenerate, and compare history before rendering video.
When a new scene is inserted after the initial seed pass, the board can auto-write prompts that match the tone of the surrounding sequence: atmosphere, visual style, location logic, and the existing cast language.
The operator is not just asking for another output. They are steering subject accuracy, scene order, tone continuity, and shot intent with visible references and version history attached to each scene.
The current screens show the core claim clearly: this is not a black-box generator. It is a board where users can inspect frames, tune scene prompts, compare outputs, reshape scenes, and reuse stable cast assets.
The board setup exposes the one-shot as an editable production document rather than a single generate button, which is the right foundation for scene-level control.
Scene cards keep image prompts and motion prompts editable separately so camera direction, motion energy, and transition behavior can be corrected in place.
Continuity frames from earlier scenes can be carried forward so the next shot begins from visual memory instead of starting cold, and uploaded subject references can force a re-shoot toward the right prop, vehicle, or location.
Cast is broader than characters. It can include people, animals, vehicles, places, and objects, each with reusable descriptions and variants. In this example, the cat inspector already carries multiple variants, including astronaut and astronaut with helmet off.
The provided forest-and-crater example is useful because the second shot needs a clean directional reset. The bad version uses the last frame of the previous video as a reference, which makes the kids appear to bicycle backward. The corrected version uses a clear prompt that understands they are approaching the crater and makes a hard cut to a shot from behind. The important point is that the system lets the operator compare, re-cut, and retry.
This is the failure mode the workflow needs to expose: using the last frame as the reference flips the implied direction, so the kids appear to bicycle backward instead of approaching the crater.
A stronger pass uses a clear prompt about approach direction and hard-cuts to a view from behind the riders, so the crater reveal reads as forward motion instead of reversal.
Once the storyboard, cast, references, and scene-level corrections are in place, audio becomes part of the same production flow. Users can create a soundtrack for a scene, generate background music, or produce voice over with different providers and voice models, including services like ElevenLabs. Captions can then sync to that voice over, or work as a standalone authoring surface.
The audio surface can support user-created soundtrack ideas, AI-generated music beds, AI voice over, reusable metadata, mood tags, preview playback, and explicit send-to-project actions. The important product point is that audio becomes a managed asset, not a hidden side effect of the video render.
The caption tool can ingest text directly or receive voice over text and timing from the audio workflow. From there the operator chooses block, phrase, line, or word display, motion graphics text animations — karaoke, typewriter, fades, and more — plus aspect ratio, legibility settings, typography, and placement. Captioning can be synced to voice over or used as a standalone authoring tool.
The end result is a finished video asset inside the platform — motion graphics, AI-generated visuals, synchronized audio, and styled captions delivered as a single output. Everything authored in the caption tool is baked into the final render, ready for distribution or reuse across campaigns.
The product goal is to help users automate and create reels at scale. An API-driven workflow can turn a spreadsheet row, CMS record, Airtable entry, or app event into a finished short-form video package: motion, AI-generated visuals, audio, and motion graphics from the haus video platform.
A single source row can define the brief, then trigger an automated storyboard run that produces many Instagram Reels without manually rebuilding each board in the editor.
Connectors to services like Google, OneDrive, Airtable, and other systems can pass a prompt into the pipeline, while stock-agency integrations can pull supporting art from places like Pexels to seed the run alongside generated visuals.
Pulled stock art does not have to stay separate from the storyboard. It can be injected as
reference-image input so the generated scenes inherit a target mood, composition, or subject cue.
The automated path is still inspectable: prompt sources become storyboards, references guide AI visuals, video jobs add motion, audio assets complete the track, platform motion graphics finish the cut, and the results land in the reels editor for review.
The user writes a single prompt, picks a visual style, and switches on auto captions, voice over, and music. Everything after that — script generation, scene creation, reference frames, render jobs, audio, captions, and final stitch — runs automatically. The user returns to a finished video.
An informational 30-second guide on how to dress old money. Cover the staples: herringbone tweed jackets, crisp tucked-in fitted shirts, and straight-cut trousers — contrasted against flashy, baggy modern fashion. Understated, elegant, timeless wealth.
The user selects a 1940s fashion catalog as the visual reference style, then enables auto captions, auto voice over, and auto music. That is the entire configuration. There are no further decisions to make.
While the automation runs, the Guided Map view surfaces the live state of every job: which scenes are seeded, which reference frames are complete, which render jobs are in progress. It is observable, not opaque — but the user never had to intervene to get here.
Every scene is resolved: 1940s catalog aesthetic applied, voiceover attached, captions synced, music laid in. The board is complete. The stitch job runs and produces the final output.
Motion graphics, AI-generated visuals in the 1940s catalog style, voice over narration, synced captions, and a generated music bed — delivered as a single finished asset. The user wrote one prompt. The platform did the rest.
This walkthrough ties the page together in product terms: Autopilot for the first pass, review at stage for inspection and correction, and Guided / Map Mode when the story needs to be reshaped before render.
The video walkthrough makes the handoff between modes concrete: prompt to seeded scenes, reference-frame generation, guided scene editing, subject correction with uploaded references, and final render decisions once the board is stable.
The platform is a turnkey package for enterprises and content teams who need to produce at scale — not one video at a time. A spreadsheet row, CMS record, or Airtable entry becomes a finished video package automatically: script, scenes, cast, motion graphics, audio, and captions, all running in the background. Content people stay in the tools they already know. The pipeline does the rest.