A quality manager needs to trace a recalled component through every assembly, finished product, shipment, and customer. Today, that is a three-week investigation across ERP, MES, and quality systems. LangOptima unifies them into a single knowledge graph — so the blast radius assembles itself in hours.
Every recall starts with the same frantic sequence: pull ERP extracts, cross-reference MES batch records, line up quality system flags, and hope no customer or shipment gets missed.
ERP knows the PO, MES knows the assembly line, quality knows the deviation log. None of them knows the full chain from component batch to end customer.
Your tier-1 suppliers are visible. Their suppliers are a spreadsheet that gets updated once a year, if at all. Two “independent” suppliers sharing a single sub-tier dependency is a discovery you make during the recall.
By the time the blast radius is calculated, the recall notice has already gone out to everyone “just in case” — damaging relationships and accuracy in equal measure.
LangOptima models your components, batches, assemblies, finished products, shipments, customers, and suppliers as connected entities in a single knowledge graph. Your ERP, MES, and quality systems remain the systems of record. The graph is the layer that lets you traverse the chain from any node to any other — in both directions.
ERP, MES, quality, and supplier systems unified in a semantic layer. Product structure, BOMs, and shipment data all become queryable from a single entry point.
Surface sub-tier supplier clusters. See which “independent” suppliers share a common vulnerability. Spot patterns in quality deviations that cross product lines.
Recall blast radius, warranty claim analysis, and supplier diversification decisions all move at the speed of a query. Hours, not weeks.
Your quality, supply chain, and customer service teams stop chasing data and start acting on it. Every investigator becomes a force multiplier.
Representative scenarios for manufacturers with your profile — ask us what applies to yours.
A manufacturer reduced recall investigation time from 21 days to 4 hours by modeling component batches, assemblies, shipments, and customers as a connected knowledge graph. The same graph surfaced sub-tier supplier dependencies that had never been visible.
Designed and deployed a sentiment analysis solution helping a major auto parts manufacturer understand customer feedback at scale. Real-time insights now drive product development and quality improvement decisions.
However complex the data landscape underneath, the engagement itself stays simple.
We ingest the documents and data you already hold, straight from the systems you already run. Nothing is replaced — your teams keep working where they work today.
A scoped 8–12 week pilot structures your first decision context. Your domain experts contribute the knowledge; we do the engineering.
You start asking the questions — and every answer carries a citation back to the source, so you can check it yourself.
Start with a 30-minute conversation. Tell us about the component, the product line, or the supplier question that is hardest to answer today — and we’ll show you how it would be answered on a knowledge graph.