Document Processing · Supply Chain Optimization
Sluicebox needed to turn fragmented bills of materials, supplier records, PDFs, CAD files, and emissions inventories into timely product carbon assessments, but manual life-cycle-analysis work was slow, expensive, and often arrived too late to influence design decisions.
Reported impact
Sluicebox raised supplier-data extraction accuracy to 87.7 percent, improved component classification and emission-factor matching accuracy, cut inference costs by 51-80 percent, and made product carbon analysis fast enough to guide supplier and design decisions before products reach market.
Sluicebox rebuilt the most labor-intensive parts of product carbon accounting around NVIDIA Nemotron 3 Ultra so its AI agents could generate dynamic life-cycle assessments instead of retrospective reports. The company's workflows ingest multimodal records and convert them into a structured carbon graph linking components, materials, suppliers, manufacturing processes, and transport information. Three AI workflows sit at the center: component classification across a roughly 100-node taxonomy, supplier part-data extraction from emails and documents, and emission-factor matching against an inventory with more than 10,000 entries. Lucy, Sluicebox's supplier AI agent, uses those results to collect and validate primary data in conversation. By swapping its prior model for NVIDIA Nemotron 3 Ultra, Sluicebox kept or improved accuracy while lowering inference cost and response time enough to support more products, suppliers, and pre-production decision scenarios.
Bills of materials, emails, PDFs, CAD files, and factory records are ingested into Sluicebox's carbon graph, Nemotron 3 Ultra classifies components and extracts supplier data, emission-factor matching maps the results to inventory records, and Lucy uses the structured output to collect or validate missing primary data with suppliers.
Companies can get primary data coverage of 90%+ of their suppliers in weeks instead of years.
Lucy guides suppliers through information requests, identifies missing fields, and checks whether values fall outside expected ranges.
Nemotron 3 Ultra processed the context needed to classify parts, structure supplier records, and select emission factors while delivering 51-80% lower inference costs, improved accuracy, and response times equal to or faster than the company's prior production model.
Sluicebox built its AI agents to create a structured model of a product and its supply chain. The agent connects components, materials, suppliers, manufacturing processes, and transportation data in a carbon graph.
Sluicebox evaluated NVIDIA Nemotron 3 Ultra for these workflows.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Sluicebox, Inc., 11801 Domain Blvd, Level 3, Austin TX 78758, United States of America
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