DeepSeek Compatibility Tester

4.35

Derivation Chain

Step 1 Imminent DeepSeek new model launch

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Step 2 Enterprise evaluation of cost-effective AI model migration

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Step 3 Automated compatibility testing tool for existing prompts and pipelines during model migration

Problem

As cost-effective AI models like DeepSeek continue to launch, there is no way to detect output quality degradation in advance when migrating prompts and pipelines built on GPT-4/Claude to new models. Model migration testing takes 2-3 days per developer, and discovering quality issues after production deployment incurs rollback costs.

Solution

Users input their existing prompt sets, which are simultaneously executed across GPT-4, Claude, DeepSeek, and other models for automated A/B quality comparison. Automatically measures semantic similarity, structural consistency, and hallucination rates, then delivers a migration feasibility Report.

Target: SaaS Startups with AI features (5-30 employees), AI development teams at IT agencies, Freelancer AI engineers

Revenue Model: Usage-based Billing at 500 KRW (~$0.38) per test execution (model API costs separate); Monthly Subscription at 49,000 KRW (~$37)/month (200 tests included); team plan at 149,000 KRW (~$112)/month (1,000 tests included)

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

5.0/5

M Market

4.0/5

R Realizability

5.0/5

V Validation

4.0/5

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 (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

MVP Timeline

20.0/20

API Bonus

0.0/15

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 (60/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.0/15

Solo Buildability

7.0/10

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

Backend [medium] AI/ML [medium] Frontend [low]

Dashboard