An AI short drama platform MVP should be scoped as a production pipeline with review gates and asset provenance, not as a thin wrapper around a video model. Before you ask a studio to quote, define the episode pipeline, the modules that must exist in release one, and the rights, moderation and disclosure checks that gate every asset. The sections below give you the specification, verification steps and deferred-feature record to bring to that conversation.
Map the episode pipeline before listing AI models
Write down the pipeline your team will actually run, stage by stage, and note what enters and leaves each stage:
- Concept and series bible: premise, characters, locations, visual rules.
- Script and beat sheet: episode length, scene count, dialogue.
- Shot list: framing, camera notes, duration per shot.
- Keyframe or character reference generation.
- Shot generation and selection.
- Voice, music and sound effects.
- Assembly, subtitles and export package.
- Review gate, then publish or schedule.
For each stage, record the input, the output, the person who approves it and where the file is stored. Ask the supplier to redraw your pipeline and mark the human approval point at every stage. Model choice follows the pipeline, not the reverse: ask which model or service serves each stage, and what the system does when an output is rejected. A platform that cannot show a rejected shot being regenerated and re-reviewed is not yet a production tool.
MVP modules to specify for release one
Keep release one to the modules that let one team produce and approve one episode end to end. Everything else is a later phase.
| Release-one module | Minimum behaviour to specify | How to verify |
|---|---|---|
| Project and episode workspace | Series, episode and scene structure with status per stage | Create a test episode and move it through every status |
| Script and shot editor | Structured scenes and shots, version history, comments | Edit a scene, restore a prior version, confirm the comment trail |
| Asset library with provenance | Every generated file stores model, prompt, parameters, date, operator and licence reference | Open a random asset and check the record is complete |
| Generation job queue | Queued, running, failed and rejected states with retry | Force a failure and confirm the job can be retried without losing the brief |
| Review and approval gate | Named approver per stage, blocking publish until approved | Attempt to publish an unapproved episode and confirm it is blocked |
| Admin and roles | Users, roles, quotas, spend log, audit trail | Sign in as each role and confirm permitted actions match the spec |
A backend that carries these records is a separate build from the generation features; see admin panel and backend development for the typical module list to compare against your brief.
Features to defer and how to record them as later phases
Defer anything that does not block a first episode: multi-language dubbing, direct publishing to social platforms, in-app monetisation, community and comments, advanced analytics, model switching, custom fine-tuning and mobile apps for viewers. Record each deferred item in a register with a reason, an entry condition and a dependency. Attach that register to the contract as an appendix so both sides know what release one excludes. Deferred items that touch platform rules, such as automated publishing to a short-video platform, should be re-scoped against current platform requirements when the phase starts; the Douyin mini program brief shows the kind of launch checklist that work implies.
Rights, provenance, moderation and disclosure questions to settle before build
Settle these in writing before development, and confirm current terms with the model providers, the platforms you publish to and the relevant authority in your market, because terms and rules change:
- Commercial use and ownership of generated output under each model provider's terms.
- Consent and documentation for any uploaded face, voice or likeness, including performer agreements.
- Licensing for music, sound effects and any stock material.
- Disclosure and labelling obligations for AI-generated content on each publishing platform.
- Moderation of prompts and outputs, including who reviews and what is blocked.
- Data retention and deletion for prompts, uploads and generated files.
Provenance should be a product feature, not a spreadsheet. Every asset needs a stored record that a reviewer can open later.
Cost drivers and usage controls to put in the supplier quote
Ask for a cost model with stated inputs and exclusions rather than a single figure. Inputs to define: finished minutes per episode, shots per minute, expected retries per accepted shot, output resolution and duration, storage and download volume, human review hours and third-party model pricing. Ask the supplier to quote the provider's current dated price list separately from their own build fee, and to show the calculation.
Controls to specify in the build:
- Per-user and per-project generation quotas with a hard stop.
- A spend log the buyer can read without contacting the supplier.
- Job cancellation and reuse of approved assets instead of regenerating them.
- API keys held in the buyer's own accounts, with rotation documented.
The AI API integration quote checklist lists the limits, logs and key-handling clauses to compare across suppliers, and AI API integration describes the integration scope those clauses attach to.
Acceptance tests, source code handover and operating responsibilities
Write acceptance tests as scenarios, not opinions. A workable set: run one sample episode through every pipeline stage; confirm a rejected shot can be regenerated and re-reviewed; confirm every asset carries a complete provenance record; confirm a quota stops a job at the limit; confirm each role sees only its permitted actions; confirm the export package can be rebuilt from stored assets.
Handover should include the source repository, deployment runbook, environment configuration, API documentation and confirmation that model accounts and billing sit with the buyer. Agree who monitors failed jobs, who approves content, who handles takedown requests and who pays model bills. Manboo Studio scopes these projects as fixed estimates after the module list, deferred register and acceptance tests are agreed.
FAQ
Should the MVP include an editing timeline?
Only if your team will cut shots inside the platform. If editors work in a separate tool, release one needs an export package and a shot list, not a timeline. State the editing workflow explicitly so the supplier does not build both.
How do we handle a model provider changing its terms?
Keep model access behind your own accounts and an internal service layer, and store the provider and version on every asset record. Ask the supplier to document how a provider swap would be made without rebuilding the pipeline.
What should we give a studio for an accurate quote?
Your pipeline diagram, the release-one module table, the deferred register, one sample episode specification and the target publishing platforms. Without these, any quote is a guess.
Ready to compare scope? Share your pipeline stages, the modules you consider must-have, your deferred list and the platforms you intend to publish to via discuss requirements.