GPT-Powered BOM Data and MCP Are Next for EPDs
Bill of materials spreadsheets still suck hours out of every EPD project. Imagine an AI that hoovers up plant data, maps it to a PCR template, and lets your team move on before lunch. The building blocks already exist, just not in one place yet.
The bottleneck nobody loves
Most environmental product declarations stall at the same step: pulling net-accurate bills of materials from multiple ERP instances and vendor lists. One recent survey found that 57 percent of manufacturers still rely on manual exports and email chains to assemble their BOM files (Spherical Insights, 2024). The data arrives late, messy, and never in the mass balance you need for an LCA.
Large language models can read parts lists
Generative AI already turns blurry photos into shopping lists. Feeding it a structured CSV is trivial. Early lab tests show a GPT model classifying 10,000 line items into UNEP CPC codes in thirty-four seconds with 97 percent precision (VentureBeat, 2024). That kind of lift removes the soul-crushing tagging step that junior engineers dread.
Enter the Model Context Protocol
The Model Context Protocol (MCP) gives language models a standardized socket to pull data and push functions (IBM, 2025). Think USB for AI. A future MCP server dedicated to EPD work could expose:
- A read-only BOM endpoint that streams data from your PLM
- A PCR library so the model always fetches the current ruleset
- A validation tool that flags mass imbalances before humans ever look
How a GPT BOM server might run
Picture two agents: one ingests raw material ledgers, the other cross-walks them to impact categories in your LCA software. The agents talk through MCP, share context, then log every assumption for your third-party verifier. Nothing breaks the audit trail.
Proof of concept exists, if tiny
Custom GPTs like the open "EPD Analyzer" already parse published declarations at. They scrape metadata, benchmark against peers, and spit out plain-language summaries. It is rudimentary but shows the appetite.
What changes for manufacturers
Faster BOM mapping slashes the path to published EPDs. Reduced human hours mean senior engineers spend more time on design tweaks that actually cut carbon rather than on copy-paste marathons. With regulations tightening in the EU and a fresh wave of state buy-clean rules in the US, time will be the scarce commodity, not consultant day rates.
Stay in the loop
The tools outlined above are not commercial products yet, just a blueprint we are tinkering with. Follow the Parq LinkedIn page for progress updates and pilot invites.
Frequently Asked Questions
Will AI-generated BOM mappings satisfy third-party verifiers?
Yes, provided every transformation is logged in a machine-readable audit file and reviewed by a qualified LCA practitioner as required by ISO 14025.
Could data security be a deal breaker?
MCP allows on-prem servers that keep sensitive recipes behind the firewall while still letting the AI reason on embeddings, so IP leakage is mitigated.
Will AI-generated EPDs meet ISO 14025 and EN 15804 requirements?
The short answer is not yet. Human LCA experts need to verify datasets and assumptions. However, automated audit trails and standardized prompts are in development, and program operators are warming to digital submission formats (IBU, 2025).
About the Author
John brings a clear, research-driven perspective on the EPD, LCA and HPD space and keeps a close pulse on environmental trends and standards in North America, helping clients understand where the industry is heading and how to get there.
