Imagine your entire order-to-delivery process as a relay race where different specialists carry the baton: one checks inventory, another chooses the best supplier, another plans shipping, and another keeps the customer updated. This solution uses a team of AI “agents” on AWS to coordinate that whole relay automatically, so orders move from quote to delivery with minimal human intervention.
Manual, fragmented order-to-delivery workflows across sales, planning, production, logistics, and customer service cause delays, errors, and high operating costs. The application coordinates these steps with AI agents to automate decisions, reduce cycle time, and improve on-time, accurate deliveries for manufacturing and similar industries.
Tight orchestration of multiple domain-specific agents around an enterprise’s proprietary order, inventory, production, and logistics data, embedded in AWS-native infrastructure and integrated with existing ERP/MES/TMS systems creates high switching costs and process-specific know-how that is hard to replicate quickly.
Hybrid
Vector Search
High (Custom Models/Infra)
Coordinating multiple agents across many concurrent orders can drive up inference cost and latency; robust orchestration, caching, and careful scoping of each agent’s responsibilities are required to keep response times and cloud spend under control.
Early Adopters
Focuses specifically on end-to-end order-to-delivery workflows using a multi-agent pattern on AWS, rather than generic single-agent copilots or narrow point solutions (e.g., only demand forecasting or only logistics). The orchestration of several specialized agents around manufacturing order lifecycles is the primary differentiator.