Neaton Auto Products Manufacturing (NAPM) Improved Quality Control and Reduced Search Time by 70% with CADDi AI Data Platform

Why It Matters

The initiative demonstrates how AI‑driven data unification can dramatically boost operational efficiency and procurement insight for Tier‑1 manufacturers, setting a benchmark for industry‑wide digital transformation.

Key Takeaways

• NAPM unified 140,000 design assets on CADDi platform.
• Search time for drawings cut by up to 70%.
• QA queries now 50% faster, saving 1m47s per task.
• CADDi active accounts rose 183% to 85 users.
• Procurement will use CADDi Quote to streamline cost analysis.

Pulse Analysis

Manufacturers are racing to move beyond basic digitization toward AI‑enabled decision making, but fragmented data silos often stall progress. Legacy systems such as separate PLM, ERP and shared‑drive repositories create duplicate effort and obscure critical insights. AI platforms that can ingest unstructured files—drawings, handwritten notes, and legacy PDFs—and present them in a searchable knowledge base are becoming essential for unlocking manufacturing intelligence and supporting real‑time analytics.

In the NAPM case, CADDi’s AI data platform ingested more than 140,000 assets, consolidating design specifications, quality records and procurement data into a single searchable hub. Engineers reported a 40‑70% reduction in drawing‑search time, while the quality team cut query duration by roughly 50%, translating into thousands of saved labor hours annually. User adoption surged from 30 to 85 active accounts, a 183% increase, indicating rapid cross‑departmental buy‑in and the platform’s tangible productivity benefits.

Looking ahead, NAPM’s rollout of CADDi Quote aims to link engineering drawings directly with historical cost data, enabling procurement teams to benchmark supplier quotes against past pricing. This capability promises to reduce manual validation, accelerate strategic sourcing, and improve margin protection—critical factors for Tier‑1 suppliers facing tight cost pressures. As more manufacturers adopt similar AI‑driven data unification, the competitive edge will shift toward firms that can turn consolidated design intelligence into faster, data‑backed decisions across the product lifecycle.