B
Opera Subtitle Maker
3.00
Derivation Chain
Step 1
Turandot 100th anniversary commemorative performance
→
Step 2
University and regional opera performance growth
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Step 3
Performance subtitle production tool
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Step 4
Multilingual subtitle auto-synchronization
Problem
University and regional opera companies—like Keimyung University's Turandot production—need Korean subtitles for Italian and German performances, but professional subtitle production costs $1,500–$3,000 (200–400만원) per show and takes 2–3 weeks. Small companies either perform without subtitles due to budget constraints or have staff manually create them, consuming over 100 hours. Frequent cue timing mistakes during live performances also break audience immersion.
Solution
Upload an opera libretto and AI automatically generates Korean and English subtitles with cue timing synchronized to the musical score. Provides real-time fine-tuning during rehearsals and one-click cue execution on performance day.
NUMR-V Scores
NUMR-V Scoring System
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
SaaS N=.15 U=.20 M=.15 R=.30 V=.20
Senior N=.25 U=.25 M=.05 R=.30 V=.15
Feasibility (68%)
Data Availability
24.4/25
Feasibility Breakdown
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
Market Validation (52/100)
Validation Breakdown
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |
Technical Requirements
AI/ML [medium]
Backend [medium]
Frontend [medium]