Simor
Data Infrastructure for Production AI
Practical writing on AI data engineering, feature stores, and the infrastructure choices that determine whether AI systems work in production.
A manufacturing company runs their operations on an ERP system installed in 2004. The vendor still supports it. The team knows how to maintain it. The integrations are stable. It works. The problem i
A speed camera does not stop the car. It captures an image at a specific moment, records the license plate and timestamp, and sends the data to a system where a human makes the judgment. The camera ob
A healthcare system deployed an AI triage assistant. It worked well in testing. In production, it started routing patients with chest pain to low-priority queues. The error was subtle and infrequent.
The buffet is unlimited in theory. You can make as many trips as you want. But the plate you carry is finite. Stack it wrong and you have room for eight crab legs but no space for the mashed potatoes
You are looking for a place to swim in warm weather. You do not know the address. Instead, you walk into a city where the street layout encodes meaning. You ask a local: "Where can I swim somewhere wa
A software debugging agent receives a bug report. It needs to search code, understand the error, propose a fix, write tests, and summarize for the developer. None of these steps are independent. Each
You have a treasure map where X marks the spot. Not for gold, but for meaning. The map places every concept at a coordinate. Related concepts sit near each other. "Dog" and "puppy" are neighbors. "Cat
You stand at a hotel concierge desk. You want a table at the restaurant downstairs, a reservation at the spa, theater tickets, and a car to the airport. You do not want the concierge to do these thing
Most teams implement retrieval-augmented generation and call it a knowledge layer. Give the model access to a vector database, stuff in some documents, and ship. This approach works for demos. It fall