Simor Consulting
Category: Tooling
Retrieval-augmented generation is simple in theory: retrieve relevant documents, stuff them into a prompt, get a grounded answer. In practice, the retrieval step is where most RAG applications fail. T
Observability is not one problem — it is three. Infrastructure observability watches your servers, containers, and network. Application observability watches your code, APIs, and user-facing behavior.
Kafka has dominated event streaming for a decade. It processes trillions of messages daily across thousands of companies. Its dominance created an ecosystem so large that "streaming" became synonymous
Feature stores solve a specific problem: the features you use to train a model must be the same features you use to serve it. When the training pipeline computes features differently than the serving
Building an LLM application is the easy part. Knowing whether it works — whether it still works after you change a prompt, swap a model, or add a tool — is the hard part. LLM evaluation platforms exis
The orchestration market has a clear incumbent and two serious challengers. Apache Airflow has been the default choice since 2015. Prefect and Dagster both emerged to address Airflow's pain points, bu
Every team building retrieval-augmented generation or semantic search eventually needs a vector database. The market has consolidated around four serious options: Pinecone, Weaviate, Qdrant, and Milvu
Every analytics team eventually faces the same choice: how do you transform raw data into something analysts can actually use? For years, dbt was the only serious answer. SQLMesh arrived with a differ
Organizations often treat data quality as secondary—something to address after building pipelines and training models. This perspective misunderstands modern data systems. In a world where ML models m