AI Self-Correction Quality Certifier

2.85

Derivation Chain

Step 1 Spread of AI that builds AI and self-corrects errors

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Step 2 Development tools for AI self-correction systems

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Step 3 Self-correction quality certification SaaS for AI

Problem

As AI systems that automatically self-correct errors become widespread, there is no independent means to verify whether self-corrected outputs are actually correct. Companies in finance, medical, and legal sectors wanting to adopt AI self-correction face 6+ month delays because they cannot prove correction quality to regulators. A 'correction drift' risk exists where self-correction loops actually amplify errors.

Solution

Connect to an AI self-correction system to automatically capture pre- and post-correction states, then compute a correction quality score via an independent verification chain (cross-model validation + rule-based verification) and generate an auditable certification Report. (1) Automatic pre/post-correction diff capture, (2) cross-model validation + rule-based anomaly detection, (3) auto-generated audit Report for regulatory compliance.

Target: QA teams at companies adopting AI solutions, CTOs at finance and medical AI Startups

Revenue Model: API Billing at 500 KRW Per Transaction (~$0.38), monthly plan at 99,000 KRW/month (~$74) including 2,000 transactions. Enterprise annual contracts priced separately.

Ecosystem Role: Regulation

MVP Estimate: 1_month

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

2.0/5

V Validation

3.0/5

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 (57%)

Tech Complexity

24.7/40

Data Availability

20.0/25

MVP Timeline

12.0/20

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 (53/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

10.5/15

Solo Buildability

3.0/10

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 [high] Frontend [low]

Dashboard