A
Drama Script AI Reader
4.50
Derivation Chain
Step 1
Growth in K-drama production
→
Step 2
Script evaluation bottleneck
→
Step 3
Automated first-pass script evaluation tool
Problem
Planning teams (3–5 people) at drama production companies receive 50–100+ script submissions per month and need to identify investment-worthy works, but reviewing a single script takes an average of 3–4 hours. With limited team capacity, over 70% of scripts are rejected without even a first-pass review, creating opportunity costs from potentially missing breakout hits.
Solution
Upload submitted scripts (PDF/HWP) and AI performs structural analysis (three-act structure, character arcs, conflict density), scoring them against genre-specific hit patterns to generate a prioritized review list. Core features: (1) Automated script structure analysis (three-act structure, character arcs, dialogue density), (2) Genre-specific hit pattern matching scores, (3) Similar existing work recommendations with differentiation highlights.
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 (78%)
Data Availability
23.3/25
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 (58/100)
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 [low]
Frontend [low]