shuxiachai/academic-commercialization-agent

What it solves

This project automates the evaluation of whether academic research is ready for commercialization. It replaces the manual, time-consuming process of gathering evidence from academic papers, patent databases, and market intelligence to determine if a research topic has viable commercial potential.

How it works

Built on the CrewAI framework, the system employs six specialized AI agents that operate in a structured pipeline:

1. Evidence Gathering: Three agents run in parallel to collect data from academic sources (OpenAlex, PubMed, arXiv, Semantic Scholar), patent databases (Lens, WIPO, EPO), and market intelligence sources.
2. Drafting: A Report Writer agent synthesizes this evidence into a Markdown report featuring inline citations and a references block.
3. Reviewing: A Report Reviewer agent checks the draft against seven specific rules (such as citation integrity and unsupported numeric claims) and applies corrections.
4. Scoring: A Commercialization Readiness Scorer agent independently analyzes the raw evidence to produce a quantitative scorecard including TRL (Technology Readiness Level) and MRL (Manufacturing Readiness Level).

Who it’s for

It is designed for researchers, technology transfer offices, and investors who need to quickly assess the commercial viability of a specific research direction or academic paper.

Highlights

• Multi-Source Integration: Pulls data from a wide array of professional tools including OpenAlex, PubMed, arXiv, and Lens.
• Verifiable Outputs: Produces reports with traceable inline citations ([A1][P2][M3]) and a structured JSON scorecard.
• Rigorous Validation: Includes a dedicated reviewer agent and guardrails to prevent hallucinated source IDs and unsupported numeric claims.
• Quantitative Assessment: Provides a weighted formula for scoring commercialization readiness across multiple dimensions.