Platform Overview The PolyPhaze Knowledge Fabric, the trusted foundation beneath every solution.

Solutions

Optimize Trusted data for faster, defensible decisions Migrate De-risk every system migration and cutover M&A Diligence to integration on one trusted view Data Commerce Package and sell your data as a product Asset Twin One trusted record for every physical asset AI-Decisions Govern AI by the decisions it makes
See it live See it on your stack. Tell us your systems and goals — we'll show how the Knowledge Fabric fits beneath what you already run. Talk to our team

By industry

Insurance Underwriting & claims intelligence Energy Asset & grid data, unified Manufacturing Supply chain & quality signals Mergers & Acquisitions Diligence at deal speed Retail Demand & inventory signals Federal / DoD Mission readiness & sustainment
Not listed? The Fabric adapts to any data domain. If your industry isn't here yet, it's still a fit — the Knowledge Fabric is model- and domain-agnostic. Talk to an expert

Company

About Us Our mission, team & story Careers Join the team building it Resources Blog, ebooks & insights Contact Talk to our team Integrations Connect your existing stack
Request a demo See it on your own data. Book a 30-minute walkthrough with a knowledge expert and find the value hiding in your existing systems. Request a demo
Outcome story · Electronics

$12M in excess inventory found, and an unseen AI buyer governed

$12M in excess inventory surfaced. An undisclosed automated procurement system running for 11 months was identified and governed. AI planning became a daily operational workflow.

Knowledge Fabric™OptimizeAI Decision TracePolyPhaze Trust Score™
$12M
Excess component inventory surfaced with disposition
11 months
Undisclosed AI procurement activity surfaced and reviewed
4 systems
ERP, MRP, supplier portals, demand planning
Routine
AI planning adoption by supply chain team
The outcome

AI Decision Trace surfaced 11 months of undisclosed procurement AI activity — purchase orders running at 22 times the human baseline — before it generated a compliance or financial event. The organization reviewed all commitments, confirmed no material adverse positions, and closed the governance gap on its own initiative.

  • Optimize surfaced $12 million in excess component inventory with clear disposition recommendations.
  • Demand planning AI adoption shifted from contested to routine once every recommendation carried a Trust Score and traceable lineage.
  • Procurement savings from supplier consolidation became visible in connected spend data for the first time.
  • Demand forecast accuracy improved as AI models ran against Trust-Scored signals rather than fragmented inputs across six part-number identifier variants.
Reviewing component inventory bins

The situation

An electronics contract manufacturer producing assemblies for industrial and communications equipment customers was managing significant supply chain volatility. Component lead times had extended. Demand signals were shifting faster than the planning cycle could absorb. The company had deployed an AI demand planning model, a dynamic inventory optimization system, and a third-party procurement intelligence tool that could automatically trigger purchase commitments when its algorithms identified favorable market windows. That tool had been deployed by the procurement team without full IT or compliance review.

The same component part number appeared under six different identifiers across the ERP, MRP, supplier portals, and demand planning system. A reconciled view of actual versus planned inventory positions was the critical prerequisite for the AI models to produce outputs the team could act on.

The solution

The Knowledge Fabric™ connected the ERP, MRP, supplier portals, and demand planning system. Entity resolution reconciled component part numbers, supplier identifiers, and customer product codes into canonical Trust-Scored records across all four systems. Every component position carries a Trust Score and lineage chain.

The Optimize capability surfaced component excess with specific disposition recommendations, made procurement savings from supplier consolidation visible in connected spend data, and improved demand forecast accuracy by running models against Trust-Scored inputs rather than fragmented ones.

AI Decision Trace established a governed baseline for demand planning, inventory, and procurement. The Coverage Discovery capability identified the third-party procurement tool: purchase order creation was occurring at 22 times the human historical baseline, with uniformity signatures confirming algorithmic generation. Eleven months of AI-generated commitments were surfaced for review. The governance function confirmed the commitments were within dollar thresholds but outside the procurement policy approval framework, and a new review protocol was established.

The same trusted data foundation is available for your operation. Talk to the PolyPhaze team about a walkthrough on your systems.

Request a demo