AI Distillation Detection Watermark

3.15

Derivation Chain

Step 1 AI model distillation controversy

→

Step 2 AI model intellectual property protection tools

→

Step 3 Model output watermark embedding/verification SaaS

Problem

As AI startups and research institutions release their models publicly, cases of competitors like MiniMax and DeepSeek extracting knowledge through unauthorized distillation are increasing. As demonstrated when Anthropic published distillation evidence, proving distillation requires months of analysis and tens of thousands of dollars in expert fees. Small and mid-sized AI companies lack these resources and are forced to leave intellectual property infringement unchecked.

Solution

Embed statistical watermarks in model API responses, then automatically analyze watermark match rates when suspicious model outputs are uploaded, generating a distillation evidence report. Key features: (1) API proxy-based watermark embedding, (2) suspicious model output comparison analysis, (3) automated PDF report generation for legal evidence.

Target: CTOs and legal officers at Korean AI startups with 5–30 employees that serve their own LLMs

Revenue Model: SaaS monthly subscription ~$74/month per model endpoint, evidence report ~$367 per transaction, 15% discount for annual billing

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

3.0/5

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 (74%)

Tech Complexity

29.3/40

Data Availability

25.0/25

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 (51/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

7.5/15

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 [medium] Frontend [low]

Dashboard