Kodiact Launches AI-Native Simulation Platform to Overhaul Direct Materials Procurement

The manufacturing sector spends up to 60% of its revenue on direct raw materials, yet the majority of procurement teams still rely on fragmented ERPs and manual Excel models to manage this exposure. This structural blind spot means that when commodity prices fluctuate, leadership teams often cannot calculate the product-level margin impact until the costs are already embedded in their P&L. To address this lag, Chicago-based startup Kodiact has launched an AI-native platform designed to bring intelligent simulation to direct materials procurement.

Unlike traditional category management tools that merely visualize historical spend, Kodiact functions as a predictive decision engine. The platform connects every material to its underlying price drivers and unifies purchasing decisions onto a single ledger. Its architecture includes real-time margin-at-risk calculations and agentic workflows, where AI agents can shortlist suppliers, draft offers, and negotiate within predefined guardrails.

According to co-founder and CTO Jonathan Lee, the platform allows manufacturers to simulate the full impact of a purchasing decision across thousands of forward paths before committing capital. The system strictly isolates customer data from shared public models to protect proprietary supply chain strategies.

The introduction of agentic workflows into procurement marks a significant shift from reactive data entry to proactive strategy. By capturing the context behind every buying decision, the software ensures that institutional knowledge compounds over time, reducing the risk of expertise walking out the door when senior buyers leave. As supply chain volatility becomes the norm, platforms like Kodiact are moving beyond basic spend analytics to offer executable insights.