Simor Consulting
Category: AI Infrastructure
A mid-market e-commerce retailer with roughly $200M in annual revenue had invested eighteen months building a product recommendation engine. The models were accurate. Offline evaluation showed meaning
Benchmark scores tell you how a model performs on problems that someone else chose. Your enterprise systems present different problems: your proprietary terminology, your specific data distributions,
A language model that only generates text is not enough for most enterprise problems. The real value emerges when an AI system can look up your customer record, check inventory levels across warehouse
A recommendation system team built their tenth model. Each model required feature engineering. Each feature engineering project started by copying code from the previous project, then modifying it for
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 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
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
Data pipelines built for business intelligence often fail when supporting AI workloads. The root cause is usually architectural: BI pipelines assume bounded, relatively static datasets, while AI syste
Existing data infrastructure often cannot support ML workflows. The modern data stack offers a foundation, but it requires adaptation to become AI-ready. This article covers building a data architectu
Traditional relational database management systems were designed for an era of megabyte-scale datasets and batch reporting. AI workloads demand processing terabyte-scale datasets with complex analytic
Vector databases index and query high-dimensional vector embeddings. Unlike traditional databases that excel at exact matches, vector databases enable similarity search: finding items conceptually clo