bluefabric maps fragmented data from ERP, WMS, TMS, spreadsheets, APIs, and data lakes into a queryable supply chain graph. Built from 1,000+ consulting engagements and 200,000+ standardized data connections, so AI agents start with supply chain knowledge already built in.
Supply chain decisions require relationships. An order doesn't exist in isolation. It connects to a supplier, a shipment, a warehouse, a carrier, and a customer commitment. When those relationships are missing from the data layer, agents guess. And guessing is expensive.
bluefabric identifies entities, matches records, resolves conflicts, and creates structured relationships between data that was previously disconnected. The result is a queryable graph that reflects how supply chains actually operate, not how IT systems happened to be built.
bluefabric does not start from a blank schema. It starts with a supply chain operating model already built in, shaped by 1,000+ implementations, edge cases, and operational decisions made at real companies.
1,000+ supply chain consulting engagements across retail, manufacturing, 3PL, CPG, and industrial distribution
ERP, WMS, TMS, and OMS implementations across SAP, Oracle, Manhattan, Blue Yonder, and dozens of mid-market platforms
Slotting, labor, dock, and fulfillment flow projects that exposed how warehouse data actually moves through systems
Transportation sourcing, carrier management, and last-mile delivery programs that shaped how freight data relates to orders
Inventory optimization and fulfillment improvement programs that defined what "available" actually means across systems
Supplier onboarding, product data governance, and master data management initiatives that revealed how records conflict and resolve
Most companies don't just have fragmented data. They have incomplete data. bluefabric enriches operational records before they reach your agents, so AI works from complete context instead of partial facts.
bluefabric resolves the same physical entity across systems with different keys, codes, and naming conventions. SKU_100928 in ERP, Item 100928-A in WMS, and PROD-100928 from a supplier all resolve to one unified product entity with a single identity across your data layer.
When one system has product dimensions and another has the HS code and a third has seasonality flags, bluefabric merges them into a single enriched record. Agents work with complete attributes, not whatever a single system happened to store.
bluefabric infers relationships that no system explicitly records. An inbound ASN from a supplier is matched to the open PO, linked to the WMS receiving event, and connected to the outbound order it was meant to fulfill, even if no system tracks that chain end-to-end.
// Five raw records, same physical product ERP: SKU_100928 WMS: Item 100928-A Supplier: PROD-100928 TMS: 100928 Case Pack Excel: Product 100928 // → bluefabric unified product entity entity Product { sku: "SKU_100928" name: "Widget Assembly 100928" hsCode: "8471.30.0100" sizeCategory: "MEDIUM" // computed from pack dimensions packTypes: [ { level: "each", weight: 14.2, dimensions: { l: 24, w: 18, h: 12, unit: "in" } }, { level: "case", unitsPerPack: 12, tiHi: { ti: 6, hi: 4 } } ] velocityClass: "A" // from ProductKPI seasonality: { source: "calculated", pattern: "Q4 peak", strength: 0.82 } suppliers: [{ name: "Acme Supply Co.", sku: "PROD-100928" }] sourceIds: { erp: "SKU_100928", wms: "Item 100928-A", tms: "100928 Case Pack" } matchConfidence: 0.97 }
Beyond cleaning and matching, bluefabric adds inferred attributes your source systems never captured. Lead times your ERP never tracked. Supplier risk scores no single team maintains. Landed cost estimates that span freight, duty, and handling. Service level baselines built from your own historical performance — not industry averages.
These attributes are sourced from the bluefabric knowledge base, built across 1,000+ supply chain engagements and continuously updated as agents interact with your data.
Without a supply chain graph, AI agents return generic answers or hallucinate relationships that don't exist. With bluefabric, every query resolves against real entities, real relationships, and real operational context.
AI responses can be evaluated with and without bluefabric context across inventory, shipments, exceptions, suppliers, and demand signals. With bluefabric, agents retrieve structured operational context instead of guessing from fragmented records or raw exports.
Measured across 200 standardized supply chain queries covering inventory, shipments, exceptions, and demand signals. Accuracy scored on factual correctness and specificity against known ground truth.
Without bluefabric, AI agents must interpret raw system exports, infer relationships from unstructured data, and guess at entity identities. With bluefabric, every query resolves against a clean, connected, semantically rich graph.
bluefabric turns your existing ERP, WMS, TMS, spreadsheets, APIs, and data lakes into a queryable supply chain model your AI agents can use.