Brightcon 2026, hackathon and courses

Highlights / Discussion Points

• TianGong starts from building a Chinese LCA database and extends toward broader LCA infrastructure.
• The infrastructure connects database development with data structuring, review, calculation, interoperability, and reuse.
• AI is used within controlled, auditable workflows to support data curation and quality governance.
• This approach can help LCA databases work with wider tool, data, and collaboration ecosystems.

Abstract / Concise Description
The TianGong Initiative is building a Chinese LCA database while extending database development into a broader AI-enabled LCA infrastructure. The database remains the foundation, but its long-term value depends on the surrounding system that supports data production, structuring, review, calculation, interoperability, and reuse.

This presentation introduces TianGong’s experience in developing LCA data from standardized unit processes toward more connected representations of industrial systems. It discusses how TIDAS-based data structuring, human-machine collaborative review, structured ingestion, calculation support, and platform services can form an end-to-end workflow for scalable database development.

Rather than using AI as a standalone generator of LCA data, TianGong embeds AI into controlled and auditable workflows where extraction, verification, correction, and expert review remain traceable. The presentation uses TianGong as a concrete case to discuss how LCA databases can evolve into infrastructure that connects with wider toolchains, data systems, and international collaboration efforts, while improving transparency, scalability, quality governance, and reuse.