Supply Chain Pipelines
From supplier risk to shipment prediction, each pipeline connects to your existing systems.
Ingest supplier performance data (delivery times, quality scores, financial indicators), enrich with AI-generated risk assessments, categorise risk levels (low, medium, high, critical), and route high-risk suppliers to procurement alerts while maintaining a live risk dashboard.
Process incoming quality inspection reports (free-text and structured), extract defect types, severity levels, and root causes with AI, aggregate defect patterns per supplier/product line over time, and flag recurring quality issues for corrective action.
Ingest shipment tracking events, enrich with weather and port congestion data, use a first-pass AI to estimate delay probability, then a second-pass AI (only for likely delays) to generate detailed impact assessments and mitigation recommendations. The two-stage pattern reduces LLM costs by 40% by only running the expensive analysis on flagged shipments.
Stage 1 uses a fast, inexpensive model to classify delay probability. Only shipments flagged as "likely delayed" proceed to Stage 2, which uses a more capable model for detailed impact analysis. This pattern reduces LLM API costs by ~40% while maintaining analysis quality for the shipments that matter.
Validate BOM entries against approved component lists, check for obsolete parts, verify compliance with material regulations (RoHS, REACH), and flag discrepancies with AI-generated correction suggestions. Routes validated BOMs to approval queues and rejected items to engineering review.
See AI-powered supply chain pipelines running live. Book a 30-minute demo with our team.