warp-dd-ecosystem-analysis

linedot/warp-dd-ecosystem-analysis

warp-dd-ecosystem-analysis is a graph machine learning model from linedot. Use it for the graph machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

Ecosystem Analysis AI is the first of three proprietary AI models powering WARP DD, an AI-powered technology due diligence platform by LINEdot., Inc.

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Updated Jun 18, 2026

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Hugging Face

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At a glance

Task: Graph Machine Learning
Library: transformers
Access: Public
Created: Jun 18, 2026
Updated: Jun 18, 2026
SHA: ec49d992

Model card

From the Hugging Face model README

Ecosystem Analysis AI — WARP DD Model 01

Overview

Ecosystem Analysis AI is the first of three proprietary AI models powering WARP DD, an AI-powered technology due diligence platform by LINEdot., Inc.

This model captures the relational structure of technology ecosystems — mapping how technologies, research, and markets interconnect and evolve.

Architecture

Foundation: Graph Neural Network (GNN)
Approach: Models the tripartite relationships between technology, research, and market as a dynamic graph
Input: Global technology data sources (papers, patents, GitHub, funding data, etc.)
Output: Ecosystem structure analysis, relationship mapping, influence scoring

What It Does

Maps the entire technology ecosystem as a dynamic graph
Identifies hidden connections between technologies, companies, and research domains
Detects emerging clusters and structural shifts in real-time
Provides the foundational "world model" that feeds into Tech Profiling AI and De Facto Trajectory AI

Part of the WARP DD 3-Layer AI Engine

Model	Role
Ecosystem Analysis AI (this model)	Graph-based relational structure modeling
Tech Profiling AI	6-axis quantitative competitiveness evaluation
De Facto Trajectory AI	Future trajectory prediction with causal inference

Validation

89% overall backtest accuracy across 100 AI/ML companies (2-year validation)
~200 companies across 7 technology domains validated with real-world data

Note

Model weights and training code are proprietary and not publicly available. This Model Card is provided to document the architecture and capabilities of the model.