Simor Consulting
Category: AI Infrastructure
A media company with a library of twelve million articles, transcripts, and research documents had built a semantic search system on a managed vector database. The system was designed to let journalis
Data pipelines for AI are not the same as data pipelines for traditional software systems. The outputs are different. The failure modes are different. The tolerance for data quality issues is differen
AWS announced Bedrock Studio. Google shipped Vertex AI Platform as a unified surface. Azure consolidated its AI offerings under a single "AI Foundry" brand. Databricks, Snowflake, and even Cloudflare
Every AI infrastructure team eventually faces the same argument. One faction wants to build a custom solution because the commercial options do not handle their specific requirements. The other factio
Three releases in the last six weeks have redrawn the open-source LLM map. Meta shipped Llama 4 with a mixture-of-experts architecture that narrows the gap with proprietary frontier models. Mistral re
Traditional software monitoring tracks CPU utilization, memory consumption, request rates, and error counts. These metrics tell you whether your service is running and whether it is handling load. The
Most vector database selection failures come down to one mistake: picking the technology before mapping the workload. Teams benchmark embedding search speed on a curated dataset, pick the fastest opti
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