❯ AI · Creator · 2026
Narra-AI
An AI narrative-production platform that turns story premises into segmented multimedia stories, preserving character, plot, and tone across segments.
The problem
Producing a narrated story is hand-assembly: write the script, record the voice, find or generate the images, keep character and tone consistent across every segment. Generative tools make each piece easy and the whole impossible. Continuity drifts the moment one scene is edited.
The approach
Narra-AI is built around two layers: a persistence and continuity memory layer that carries character, plot, and tone across segments, and a media orchestration layer that aligns word-level TTS timestamps with beat markers and scene cues. The narrative engine is a working prototype in Python. Strict JSON Schema-aligned prompts make the LLM emit story plans (story, characters, segments with beats, sceneCues, targetDurationMs, and an aggregate storyState) that the rest of the pipeline can consume without guessing.
Architecture
- Story Premise
- Narrative Engine (LLM)
- Story Segments
- Continuity Layer
- Media Orchestration
- Veo 3.1 Generation
FLOWS
- Story Premisestrict schema promptNarrative Engine (LLM)
- Narrative Engine (LLM)beats / sceneCuesStory Segments
- Story Segmentsaggregate storyStateContinuity Layer
- Story Segmentstimestamped cuesMedia Orchestration
- Media Orchestration8-sec clipsVeo 3.1 Generation
A story premise enters the engine and comes out as a structured plan. Each segment carries the cues the media layer needs: where beats land in time, which scenes the stills should depict, how long the audio should run. TTS timestamps are validated against the beat markers with DTW-style temporal alignment. When a segment misses, the system regenerates only the failing modality instead of the whole clip, a cost lever that matters at scale. Video generation is prototyped with Google Veo 3.1 Fast, composing image-reference assets with timestamped scene-cue prompts into 8-second clips.
Key decisions
Schema-aligned prompts
The model's output is a typed plan, not prose; everything downstream depends on that contract.
Regenerate the modality, not the clip
Fixing one bad audio track should not cost a new video render.
Continuity is state, not prompting
A rolling storyState beats hoping the model remembers.
Prototype before platform
The engine, the alignment, and the clip format were proven before committing to a product surface.
Documentation alongside the code
The 24-doc suite forced clarity on architecture, data, legal/IP, and localization early.
What I'd do differently
The cost model assumed linear spend per segment; caching, resolution tiers, and provider fallback arrived late in the design. I would define the unit economics before writing the orchestration layer. The Veo prototype proved the 8-second clip format, but image-reference composition deserved earlier testing, since that is where visual continuity is won or lost.