RFP-driven capacity models were built line by line in Excel and took 8–10 hours (up to 15 for complex RFPs); Polly Xtract now drafts structured RFP models in 2–2.5 hours with expert review.
Chemical inputs were scattered across CAS, Sigma-Aldrich, and internal sheets, causing inconsistencies; a harmonized chemical registry standardizes properties and maps internal codes to pricing for consistent RFP models.
Scale-up math for RFP estimates was manual and error-prone; automated equivalency, yield, and factor calculations run quick simulations from 100 g to 5 kg to size materials, reactor capacity, and costs for each RFP.
Critical fields and compliance steps for RFPs surfaced late; an AI gap-analysis plus a regulatory-aware layer flags BSE/TSE and Nitrosamine requirements early in the RFP process, improving pricing accuracy and turnaround.
Outputs varied by team, making RFPs inconsistent; standardized templates, logs, and an audit trail produce reproducible, audit-ready RFP models, maintain human-level data completeness, and cut manual work by more than half.
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