Analyzer
Detects emotional Peaks and Valleys from text and visualizes the emotional rhythm of your story in real time. It serves as the foundation for all emotion-driven editing.
Emotion · Time · Narrative
When AI can generate a 30-minute even an hour film, what will pre-production look like?
Shot Rhythm Engine transforms stories into emotional rhythm data — solving the fundamental limitation of current video AIs.
Google Cloud MVP
Seed/Hash Reproducible
Research-grade Dataset
Our Vision
When video generation AIs like Sora and Veo can create 30-minute stories,
the missing piece will be emotion and time as data.
JsisOn has built the initial grammar to solve this — the
Shot Rhythm Engine, an evolution from IP to Dataset to Model to Engine,
now productized as Shot Rhythm Studio.
Our engine automatically analyzes emotional curves from text and generates
data-driven Cut Boards for pre-production. This enables creators to
visualize emotional peaks and valleys, treating structure like data.
Emotion is Retention. Narrative is Conversion.
This is the new paradigm of storytelling where emotion meets technology.
What Is Shot Rhythm Engine?
Shot Rhythm Engine is the core narrative AI engine that reads text, predicts the emotional rhythm and timeline of a story, and generates shot suggestions tailored for various formats — including webtoons, drama, short-form video, film, and games. It aligns shots to the visual tempo of each scene. This is not a user-facing tool, but the **intelligence at the heart** of the entire system.
The engine runs beneath Shot Rhythm Studio, much like an engine powers a car. Shot Rhythm Studio is the creative platform that drives on top of this engine — allowing creators to visualize emotional rhythm and refine shotboards directly from text.
MVP Live on Azure
Migrating to GCP
Seed/Hash Reproducible
Shot Rhythm Studio — 5 Modules
Shot Rhythm Studio is built on top of the Shot Rhythm Engine. It begins by visualizing the emotional rhythm and timeline inferred from text. Through LLM-based alignment audits and narrative coaching, it helps writers, editors, and content creators enhance scene composition across multiple formats. After this analysis, the Engine generates tailored shot suggestions for each content type — such as webtoons, short-form videos, dramas, films, or games. Creators can then prepare their edits based on this data, significantly reducing both time and production cost. The finalized editing data can flow directly into the video generation pipeline or be exported as structured assets to support independent content production.
Coach
Performs LLM-powered analysis to identify rhythm breaks, pacing issues, and emotional mismatches, delivering narrative coaching and global advice for stronger scene execution.
Editor
Converts emotional rhythm data into visual cutboards and scene timelines, optimized for video, animation, or webtoon formats.
Mapper
Generates multimodal datasets combining text, emotion, and shot data, enabling advanced AI models to understand narrative flow and emotional time. These datasets support both internal workflows and external model training.
MVP: Shot Rhythm Studio Analyzer is currently live on Azure, with the full SaaS toolset now being migrated and developed on a Next.js-based architecture.
Shot Rhythm Studio — Analyzer MVP in Action
The Analyzer module is the first officially released feature of Shot Rhythm Studio, a narrative AI platform that transforms text into emotion-driven shot data. This module analyzes text to detect emotional Peaks and Valleys, visualizes the rhythm of the story in real time, and provides LLM-based alignment auditing, global narrative advice, and coaching — all generated as structured reports. The links below showcase the currently deployed MVP versions:
- Shot Rhythm Studio MVP Demo Video: An overview of the platform and a walkthrough of emotional analysis
- Shot Rhythm Engine (Azure): A gated test app that suggests shot scenes based on emotional rhythm (for API-based testing)
- Shot Rhythm Studio Analyzer (GCP): A service-based MVP implementation of the Analyzer module
Watch MVP Demo
Open Shot Rhythm Engine (tester, gated) (Azure)
Open Shot Rhythm Studio Analyzer MVP (GCP, public)
Open Shot Rhythm Studio Analyzer MVP (Azure, public)
Product Preview: The nine screens below illustrate the Analyzer's product-in-development stage — a visual glimpse of its upcoming production build, evolving from the current MVP foundation powered by Azure.
*The core Shot Rhythm Engine is currently deployed on Azure, operating under gated access for internal use — this engine powers the scene-generation and rhythm-analysis backbone.* *The Analyzer (MVP), built on top of this engine, has been publicly released on both Azure and Google Cloud (GCP) for open access.* *We are now migrating the Analyzer into a full product form within the Shot Rhythm Studio suite — with Mapper and Editor modules under development and scheduled for launch soon.*
Founder
Jisook Park
Founder & CEO, JsisOn OÜ (Estonian e-Residency Company)
Creator of Batal Stone, a 9-volume SF Hero Saga published on Amazon.
Developer of the Shot Rhythm Engine and MS-Arc Narrative AI model.
With 20+ years of experience in film art, design, and storytelling, Jisook has evolved from a film art director to a world-architect building a full narrative data ecosystem where emotion meets structure.
- 📍 Location: Daejeon, Republic of Korea
- 📧 Email: icbooks@batalstone.com
- 🌐 Websites: batalstone.com / js-is-on.com
Career Highlights
- Film & Design (2002–2017): Led art & prop design for major films — Blue, Deus Machina, The Legend of the Holy Lineage, Hanbando, X-Men: Apocalypse, and Logan.
- Writer & Creator (2018–2025): Authored and visualized the Batal Stone Universe — a symbolic SF-fantasy epic spanning 9 volumes, serialized and later published worldwide on Amazon.
- AI & Publishing (2024–Present): Founded !CBooks for creative AI publishing and global distribution.
- Media & Education: Directed news videos and cultural archives (JBS / Nonsan Cultural Center) and lectured on AI art and short-form video production through Hoseo University and Chungnam Provincial University CCEPA.
- AI Narrative Technology: Developed MS-Arc v1.0, the first AI model to infer narrative rhythm (Peak/Valley structure) from text. Built Shot Rhythm Engine MVP on Azure and currently migrating to Google Cloud (GCP).
Education
- BFA in Sculpture — Chung-Ang University (2002)
Recognitions & Programs
- AWS Summit Seoul 2024, 2025 & AWS Public Sector Seminar (Daejeon)
- Peachscore Accelerator Cohort 26 & Dealum Estonia Startup Track
- Azure Startup Program — MVP selected and deployed
Jisook Park has spent over a decade exploring structured storytelling — where emotional rhythm, temporal curves, and symbolic architecture converge into data-driven narrative. Her narrative experiments culminated in the 9-volume epic Batal Stone, and are now being transformed into an AI narrative engine.
Product
Script-to-shot tooling and emotion-driven narrative datasets for research and production.
Shot Rhythm Engine — API
Transform scripts into shot sequences with tags, transitions, and timing. Control via presets, reproduce via hash.
PV Radar — Analyzer MVP (gated)
Narrative engine-based shot rhythm tool. Private MVP, session logging active. Analyzer export is disabled in public build because datastore is not fixed yet.
Deployed on multiple runtimes. See runtime matrix →
Benchmark — One-Pager
Golden Set summary, coverage metrics, and scoring methodology.
Datasets
Scene-level emotion VAD curves with symbolic tags, manifests, and complete documentation.
SKU: EMOTION-V1
Emotion Dataset — Core100 + Episode Delta (v1.0)
Scene-level V/A/D curves with labeled centroids, distances, and margins. Includes 3-scene samples, data dictionary, and changelog.
- • Files:
emotion_curve.csv,scenes.jsonl,manifest.json - • Format: CSV + JSONL with complete manifest
- • Language: EN (KR mirrored structure coming)
Sample Visualization
Dataset Specification (manifest.json)
{
"tables": {
"emotion_curve": {
"fields": [
{"name": "scene_idx", "type": "integer"},
{"name": "valence", "type": "number"},
{"name": "arousal", "type": "number"},
{"name": "dominance", "type": "number"},
{"name": "label", "type": "string"},
{"name": "label_distance", "type": "number"},
{"name": "margin", "type": "number"}
]
}
},
"version": "1.0",
"updated": "2025-08-17"
}
Sample JSONL (2 lines)
{"scene_id":"S-0001","valence":0.12,"arousal":0.34,"dominance":0.56,"label":"neutral"}
{"scene_id":"S-0002","valence":0.75,"arousal":0.60,"dominance":0.90,"label":"pride"}
From IP → Dataset → Engine → SaaS
Built on the Batalstone narrative IP, we derived emotion/symbol datasets, trained MS-ARC v1.0 to infer narrative curves, engineered the Narrative Engine and Shot Rhythm Engine, and are productizing it as Shot Rhythm Studio (Analyzer shipped first).
Tech is licensed to and operated by JsisOn OÜ (5-year exclusive, auto-renewing).
Stack
From foundational data to production tooling — every layer tracked with manifests and hash reproducibility.
Dataset
Emotion VAD curves, symbolic tags, scene-level annotations
↓
Model
PV features, shot-tagging, transition rules
↓
Narrative Engine
Shot rhythm planner, presets, seeds
↓
PV Radar (Tool)
Shot sequence UI & API
Watermark & SHA manifest — planned/rolling out
App (Azure MVP)
Private access. Session logging active. Watermarking & SHA manifest — planned/rolling out. No redistribution permitted.
Deployed on multiple runtimes. See runtime matrix →
Benchmark — One-Pager Preview
Updated: 2025-10-05
Licensing
| Tier | Rights | Model Training | Seats | Price |
|---|---|---|---|---|
| Evaluation | Internal tests, private demos (30 days) | ✕ | 1 | $149 / 30d |
| Research | Non-commercial research | △ (pre-approval) | 3 | $1,490 / year |
| Commercial | In-product usage (limited) | ✓ | 10 | $9,900 / year |
| Enterprise | Expanded rights / sublicensing | ✓ | Custom | Custom |
IP Provenance
Owner (original work): Park, Ji-suk
Exclusive licensee (commercialization): JsisOn OÜ
Term: 5 years from initial execution, auto-renewal unless terminated under the license terms.
Public-facing wording: "Tech is licensed to and operated by JsisOn OÜ."
This states that creation and technology are separated: the creator retains authorship and IP title; the company operates, hosts, and sells.
* Model training on Research tier requires pre-approval. No redistribution of raw data or model weights.
Restrictions: no resale or open redistribution; maintain copyright notice; no re-identification attempts; no redistribution of model weights trained on the data.
Read full license preview
DATA LICENSE (Preview) — JsisOn OÜ Scope: Scene-level emotion dataset (EMOTION-V1) and Shot Rhythm Engine API. Grant of rights per purchased Tier. You may: • Download, process, and use internally within Tier scope • Train models as specified in Tier permissions • Access API endpoints with valid key You may not: • Re-sell or re-publish raw data • Re-distribute model weights trained on data • Attempt re-identification of source material • Sublicense without Enterprise tier Attribution: © JsisOn OÜ. Include LICENSE.txt with internal archives. Jurisdiction: Estonia. Refunds per policy at /legal. This preview is informative; executed license PDF governs.
API Key access is the primary licensing path. Direct dataset sales are currently on hold. Full results available under NDA upon request.
About
Our operational structure
Domain Strategy
JsisOn OÜ operates a dual-domain model:
- js-is-on.com — Product documentation, API, datasets, and tooling
- batalstone.com — Brand presence, company story, and investor relations
This separation ensures focused product experience while maintaining transparent organizational accountability.
IP & Licensing Model
Original narrative IP is created and owned by the founder. JsisOn OÜ holds an
exclusive, commercialization-first, auto-renewing license to operate these
technologies and datasets on js-is-on.com and linked brand channels.
Public phrasing: "Tech is licensed to and operated by JsisOn OÜ."
Contact
For licensing, collaboration, API access, or demo inquiries:
contact@js-is-on.com
Please include your organization name, intended use case, and region.