Shakudo
Use Case
Supply Chain Traceability Platform with AI: Real-time Semiconductor Lot Tracking
SEE IN ACTION
Three screens from a live lot: the tracking board, the lot detail, and the disposition queue.
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A wafer lot that fails at final test can take weeks to trace back to its source. The lot moved through fabrication, assembly, and test, and each step left a record in a different system owned by a different supplier. Quality engineers reconstruct the path by hand, across email threads and spreadsheets, while the finished good is already on hold and the customer is already asking questions. Every week of that search is revenue frozen in a quality hold.
A supply chain traceability platform ends the manual search. It tracks every lot in real time, from raw material to shipped good, across the full network of suppliers and manufacturing partners, and the AI predicts potential quality issues before they impact production. Building and deploying this supply chain visibility platform traditionally requires 6 to 8 months of integration work. With Shakudo's managed platform, organizations deploy these tools within days and securely connect their suppliers without complex IT infrastructure changes.
Shakudo deploys an AI supply chain traceability platform built for semiconductor manufacturers. Every lot is tracked in real time across the supplier and partner network, with quality metrics and supplier performance in one view. Manufacturing teams gain immediate visibility into lot status. Quality managers proactively address potential issues using AI predictions, and procurement teams get real-time insights for supplier management and capacity planning. When a defect does appear, the team traces it to the source lot, process, and supplier in minutes, and the hold clears faster.
The platform runs as sovereign AI on your own infrastructure, which is the foundation of the whole system. Manufacturing process data, supplier quality documents, and lot histories are sensitive, and the platform is built so they never leave the manufacturer's environment. Snowflake serves as the central data warehouse for the full lot and supplier history. MinIO provides secure object storage for supplier documentation and quality reports, in the native formats the suppliers send. dbt transforms raw manufacturing data into standardized formats, so a lot from one fab and a lot from a second source line up in the same model. LangChain powers the AI models that predict potential quality issues from the pattern of lot history and supplier signals, before the issues impact production. n8n enables automated workflow triggers based on real-time manufacturing events, so a lot flag becomes a review task with the right owner assigned. Metabase delivers the dashboards that track the key metrics across the supply chain, from lot yield to supplier on-time performance. As semiconductor manufacturing continues to evolve, the flexible architecture lets teams adopt new processes and new suppliers without rebuilding the platform.
Quality, manufacturing, and procurement teams at semiconductor manufacturers and their contract manufacturers that run a complex network of suppliers and partners. The core users are quality engineers and supply chain planners, supported by the data team that maintains the warehouse and the AI models.
It is a platform that tracks every lot and component in real time across the supplier and partner network, and uses AI to predict quality issues before they reach production. The trace runs in both directions: a defect traces back to the source lot and process, and a risky supplier or process flags the lots that may be affected before they ship.
Yes. Shakudo's managed platform connects suppliers through document exchange and structured data feeds. MinIO holds the supplier documentation and quality reports in the formats the suppliers already produce, and n8n automates the intake and routing, so the supplier sends a quality report and it lands in the warehouse without a manual step on either side.
LangChain powers the AI models that read the combined lot history, process parameters, and supplier quality signals, and flag the lots and suppliers that show the pattern of an emerging defect. The prediction lands in Metabase and triggers an n8n workflow, so the quality team reviews the flagged lot while it is still in process, which is when a catch is cheapest.
For semiconductor supply chains, that means the trace takes minutes, and the defect is caught before it ships. Book a demo and see an AI supply chain traceability platform track every lot in real time.
Shakudo enables semiconductor manufacturers to deploy an integrated supply chain visibility platform that connects fragmented supplier data systems and provides real-time lot tracking capabilities. The platform combines AI models for predictive quality control with automated data integration and standardization tools, allowing manufacturers to quickly onboard new suppliers and maintain consistent quality standards across their supply chain.
"What drew me in is simple. When developers ship production-ready code this quickly, how can I have environments spun up fast enough? Shakudo is how we close that gap."
"Shakudo does not just provide the platform. It is a real partnership. They are always there to help and execute our vision faster and the right way. It is like a co-team working together to achieve our goals."
