Matching enterprise technology demands with academic resources is critical for technology transfer, yet difficult because enterprise requests are often vague, constraint-sensitive, and expressed differently from scholarly outputs. Existing search and retrieval-augmented generation (RAG) systems may retrieve topically relevant results but often provide unsupported recommendations or insufficient evidence. We present TechMatch, an interactive system for evidence-constrained matching of enterprise demands to papers, scholars, patents, and projects. Its key novelty is a trustworthy workflow that combines demand clarification, structured constraint extraction, graph-augmented retrieval, evidence-aware reranking, and traceable report generation. Each recommendation is grounded in retrieved source evidence, reducing hallucinated rationales and exposing unresolved constraints and feasibility risks. To support efficient interaction, TechMatch uses hierarchical scholar representations, lightweight profile indexing, parallel reranking, and context-budgeted evidence selection. We construct a large-scale academic corpus covering four million research resources. On a public-source benchmark, TechMatch outperforms standard RAG by 9.9% on P@5, 10.1% on NDCG@5, and 9.3% on evidence support, while enabling users to inspect evidence and generate actionable technology-transfer reports.