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ALwrity/docs/story writer/STORY_STUDIO_SSOT.md

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Story Studio – Single Source of Truth (SSOT)

1. Vision and Positioning

  • Product name: Story Studio (evolution of Story Writer).
  • What it is: A narrative engine plus lightweight movie studio that turns brand and product context into structured stories, scenes, and multimedia assets.
  • Role in ALwrity: One of several “surfaces” driven by the same contextual brain (onboarding data, personas, SEO, competitive analysis), alongside Blog Writer, YouTube Creator, Podcast Maker, and Video Studio.

Core positioning

  • Campaign Story Engine (primary):
    • Generates product stories, brand manifestos, founder narratives, and customer journey stories.
    • Uses onboarding + persona + SEO context so stories are on-brand and strategically aligned.
  • Story Studio / Fiction mode (secondary):
    • High-quality freeform stories (including anime / fantasy), primarily for GSC traffic and creative users.
    • Lives “one click deeper” in the UI so marketing-first use cases stay front and center.

Story Studio is not just “a novelist playground”; it is the narrative layer that can feed:

  • YouTube Creator (scripts and scenes),
  • Podcast Maker (episodic story arcs),
  • Video Studio (scene-wise video generation),
  • Blog and social content (campaign narratives).

2. Modes and Templates

2.1 Modes

  • Marketing Narratives (default):

    • Entry path for ALwrity users arriving from the main app.
    • Story types:
      • Product Story (feature-driven but narrative-first).
      • Brand Manifesto / “Why we exist”.
      • Founder Story / Mission Arc.
      • Customer Case Story (problem → journey → outcome).
    • All marketing modes must:
      • Pull from onboarding canonical_profile (brand, product, audience, pain points).
      • Respect persona tone and style.
      • Optionally weave in SEO pillars and competitor differentiation language.

  • Pure Story Mode (creative):

    • Entry path for GSC traffic and direct Story Studio users.
    • Story types:
      • Freeform fiction.
      • Anime / manga stories.
      • Experimental narrative formats.
    • Marketing alignment:
      • Gently surfaces the idea: “Turn this into a video / campaign” via export options.

2.2 Template system

For each template, define:

  • Required fields: goal, target audience, setting, length, POV, style, tone.
  • Optional fields: persona to anchor voice, product/context attachments.
  • Output shape:
    • Premise (1–2 sentences).
    • Outline (scenes with structured metadata).
    • Story text (segments).
    • Multimedia plan (per scene: image prompt, audio cue, video emphasis).

Initial templates:

  • Product Story:
    • Uses onboarding product data (features, benefits, differentiators).
    • Structure: origin → tension → solution → proof → future.
  • Brand Manifesto:
    • Uses canonical_profile (mission, values, audience).
    • Structure: problem with status quo → belief → promise → invitation.
  • Founder Story:
    • Uses founder/persona context if available, otherwise guided questionnaire.
    • Structure: “before” life → trigger moment → struggle → insight → creation of the product.
  • Customer Case Story:
    • Uses ICP + persona + pain-point data.
    • Structure: “Customer X had problem Y” → discovery of solution → implementation → quantified outcome.
  • Anime / Storyverse:
    • Style, visuals, and multimedia prompts oriented toward anime / stylized output.

3. Context Inputs (Data and Signals)

Story Studio should treat ALwrity’s context as first-class inputs:

  • Onboarding / canonical_profile:
    • brand_pitch, positioning, audience, products/services, content pillars.
  • Personas:
    • core persona plus platform adaptations (tone, structure, constraints).
  • SEO and competitors:
    • core keywords, sitemap analysis, competitive positioning and gaps.
  • User preferences:
    • story length, style, tone, POV, “safe vs experimental”, multimedia intensity.

Implementation:

  • Extend Story Setup to optionally show:
    • “Use my brand & product data” toggle.
    • Dropdown/select for which persona to write from.
    • Pre-filled prompts using SEO pillars and competitor gaps.
  • State builder (getRequest() in useStoryWriterState) should:
    • Attach canonical_profile / persona IDs or snapshots when marketing templates are used.

4. Text Model Strategy

4.1 Roles

  • Structured outline + scenes:

    • Use llm_text_gen with json_struct for:
      • Scene lists (scene_number, title, description).
      • Per-scene image_prompt, audio_narration, character_descriptions, key_events.
    • Requirements:
      • High reliability on JSON output.
      • Good adherence to scene progression instructions.

  • Narrative generation (story text):

    • Use llm_text_gen in text mode for:
      • Premise, story start, continuation until word-count targets.
    • Requirements:
      • Style controllability (tone, genre, age group).
      • Long-context coherence for medium/long stories.

4.2 Providers and models

Use the existing provider abstraction:

  • OSS/primary: HF-backed models (Llama 3.1 8B, Mistral 7B, Qwen2.5) for cost-effective long-form and experimental modes.

Decisions:

  • Keep model selection internal for now (no UI dropdown).
  • Introduce a simple “Quality vs Speed vs Cost” preset internally mapped to:
    • High quality: Gemini or best OSS model.
    • Balanced: OSS instruct model (e.g., Llama 3.1 8B, Mistral 7B).
    • Experimental: more creative / higher-temperature models.

Story length control remains as defined in story-writer-architecture.mdc:

  • Short (~1k words): one-shot.
  • Medium (<5k): multi-step.
  • Long (~10k): multi-step with IAMDONE marker.

5. Multimedia Model Strategy

Multimedia generation must reuse existing studio infrastructure rather than invent new stacks.

5.1 Images

  • Use StoryImageGenerationService as primary:
    • Provider: stability / other configured HF/Gemini image models.
    • Inputs: per-scene image_prompt derived from outline and template.
    • Templates:
      • Realistic marketing visuals (product-focused).
      • Anime / stylized visuals (for anime mode).

5.2 Audio

  • Use StoryAudioGenerationService:
    • Providers: gTTS, pyttsx3, or any configured TTS provider.
    • Inputs: audio_narration per scene (or compressed scene text).
    • For campaign stories:
      • Voice selection aligned with brand persona (calm, energetic, authoritative).

5.3 Video

  • Use StoryVideoGenerationService for stitching scenes:
    • Inputs:
      • image_urls or video_urls per scene.
      • audio_urls per scene.
      • fps, transition_duration.
    • For advanced/hero scenes, optionally integrate Video Studio models:
      • text-to-video models (Hunyuan, LTX-2) for key moments.
      • image-to-video (WAN 2.5, Kandinsky 5 Pro) for stylized sequences.

5.4 Modes

  • Standard story video:
    • Use existing story video pipeline.
    • Emphasis on timeline, readability, and voiceover.
  • Anime / stylized video:
    • Switch scene prompts + model selection to anime-friendly setups.
    • Use more dynamic transitions for “story trailer” feel.

6. Phase Flow (Reframed for Story Studio)

Core phases remain as in story-writer-architecture.mdc but with clarified responsibilities:

  1. Setup:

    • Choose mode: Marketing Narrative vs Pure Story.
    • Select template (Product Story, Brand Manifesto, etc.).
    • Toggle/use brand context (onboarding, personas, SEO).
    • Configure image/audio/video settings.

  2. Outline:

    • Generate structured outline with scenes via json_struct.
    • For marketing templates:
      • Ensure each scene maps to a stage in the campaign story arc.
    • For anime/fiction:
      • Emphasize “world, characters, conflicts” and visual richness.

  3. Writing:

    • Generate story text based on length settings.
    • Enforce length controls and completion detection.
    • Show word count and target in UI.

  4. Multimedia / Export:

    • Generate scene images, audio, and video using configured settings.
    • Allow:
      • Export as story video (using StoryVideoGenerationService).
      • Export scenes and script to:
        • YouTube Creator (for adaptation to YouTube-specific script + scenes).
        • Video Studio (for advanced editing or alternative visual styles).

  5. Reset:

    • Keep existing reset semantics (clears state, localStorage, phases).

7. Implementation Plan

Phase 1 – Positioning and UX tweaks

  • Rename surface in UI copy to “Story Studio” where appropriate (keep routes/IDs stable to preserve GSC).
  • Add mode selector in Setup:
    • Marketing Narratives (default).
    • Pure Story.
  • Add template selector for marketing mode:
    • Product Story, Brand Manifesto, Founder Story, Customer Story.
  • Adjust landing screen messaging to emphasize:
    • “Turn your brand and product into narrative campaigns.”

Phase 2 – Context integration

  • Extend Story Setup state to store:
    • useBrandContext flag.
    • selectedPersonaId (optional).
    • selectedProductId or product context stub.
  • Update getRequest() to include:
    • References or snapshots from canonical_profile (where available).
  • Update backend story_service to:
    • Accept and pass context into prompts (premise, outline, story start, continuation).

Phase 3 – Template-specific prompts

  • Design prompt templates per marketing template:
    • Product Story, Brand Manifesto, Founder Story, Customer Story.
  • Encode:
    • Clear arcs (setup → conflict → resolution).
    • Use of brand and persona parameters.
    • Explicit constraints on tone, length, POV.
  • For pure story mode:
    • Provide genre/style toggles, including anime.

Phase 4 – Multimedia presets

  • Define multimedia presets for:
    • Marketing Story (clean product visuals, muted transitions).
    • Anime Story (bold colors, stylized prompts, dynamic transitions).
    • Trailer Mode (shorter, punchier video composition).
  • Map presets to:
    • Image generation prompt scaffolds.
    • Video settings (fps, transition_duration).
    • Potential future mapping into Video Studio model choices.

Phase 5 – Cross-surface exports

  • Implement export from Story Studio to:
    • YouTube Creator:
      • Export scenes as a JSON payload that can seed plan/scenes.
    • Video Studio:
      • Export scenes with associated image/audio references to pre-populate a project.
  • Add UI affordances:
    • “Send to YouTube Creator”.
    • “Open in Video Studio”.

Phase 6 – Hardening and metrics

  • Ensure:
    • Subscription / usage checks per story generation and multimedia call.
    • Caching behavior for repeated outline/story generations when context unchanged.
    • Task management and polling remain consistent with existing story_writer architecture.
  • Add metrics:
    • How many Story Studio sessions start in marketing vs pure modes.
    • How many flows export to YouTube/Video Studio.

9. Current Implementation Status (Feb 2026)

  • Modes and templates:
    • Marketing vs Pure Story modes are wired through Story Setup state and backend prompts.
    • Template identifiers (e.g. product_story, brand_manifesto, founder_story, customer_story, short_fiction, long_fiction, anime_fiction, experimental_fiction) are resolved in service components for label-aware prompts.
  • Context integration:
    • Story context endpoint exposes canonical_profile-derived brand_context and brand_assets for Story Studio.
    • Story Setup modal can toggle use of onboarding brand persona when in marketing mode, and surfaces avatar and voice preview where available.
  • AI Setup modal:
    • Dedicated AI Setup modal drives an idea → setup flow.
    • “Enhance Story Idea” uses a dedicated backend endpoint to enhance only the freeform idea text, preserving intent and avoiding premature setup field generation.
    • “Continue to Story Setup” generates exactly three structured setup options and, on selection, pre-fills Story Setup fields (persona, setting, characters, plot, style, tone, POV, audience, rating, ending, length, premise) plus image/video/audio defaults.
    • UI includes rotating, mode-aware idea placeholders and an AI-style, animated glow frame around the story idea textarea to emphasize the primary input surface while keeping the overall theme light and readable.
  • Multimedia defaults:
    • Story Setup state carries image/video/audio settings and can accept per-option overrides from generated setups.
    • Story Outline and StoryImageGenerationModal integrate with the shared image generation stack, including provider/model selection and cost-awareness.
  • Video integrations:
    • HD video configuration section exposes provider/model dropdowns with story-based defaults, aligned with the broader Video Studio architecture.

10. Next Steps for Story Studio

  • Deepen template-specific prompts:
    • Tighten prompt templates per marketing template so setup options encode clearer arcs and brand positioning, especially for product_story and customer_story.
    • Expand fiction/anime prompt variants to better control pacing and recurring characters across scenes.
  • Refine idea-to-setup bridge:
    • Add light-touch guidance in the AI Setup modal to help users iterate between idea enhancement and setup generation (e.g. suggested follow-up edits based on the last enhancement).
    • Capture telemetry on how often users enhance the idea before generating setups to tune prompts and thresholds.
  • Multimedia and cross-surface flows:
    • Wire Story Studio’s outline and scene structures into “Send to YouTube Creator” and “Open in Video Studio” actions, including carrying over multimedia presets.
    • Introduce preset bundles for “Standard Story”, “Anime Story”, and “Trailer Mode” that automatically adjust image/video defaults and, where available, Video Studio model choices.
  • Reliability and guardrails:
    • Add targeted monitoring around the AI Setup endpoints (idea enhancement and setup generation) for latency, error rates, and JSON validity.
    • Continue to enforce subscription and usage checks consistently across Story Studio, Blog Writer, and Video Studio so billing and limits remain unified.

8. Guardrails and Non-Goals

  • Do not:
    • Turn Story Studio into a separate codebase; keep it within the existing Story Writer architecture and services.
    • Fragment context handling; continue to use canonical_profile and existing persona services as SSOTs.
  • Guardrails:
    • Maintain strict story length controls.
    • Keep subscription handling centralized via triggerSubscriptionError.
    • Continue to use structured JSON for outlines and scene metadata wherever possible.

This document, together with story-writer-architecture.mdc and the existing Story Writer implementation docs in docs/story writer/, serves as the SSOT for evolving Story Writer into Story Studio.

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