Simor Consulting
Category: Tooling
Data quality failures are expensive and silent. A broken pipeline does not crash — it produces wrong data that flows into dashboards, models, and decisions. The error is discovered weeks later when a
You have signed off on an AI initiative. Your team has a real workflow in mind — say, triaging inbound operations tickets, drafting first-pass vendor reviews, or reconciling exception cases across thr
The demo looked great. The model summarized the document cleanly, answered the test question correctly, and produced prose that read well enough to ship. Two weeks later it is in production, and the c
Real data is expensive, restricted, and often unusable. Privacy regulations block access to customer records. Data sharing agreements prevent using production data in development environments. Class i
Graph databases went from niche to essential as AI applications discovered that relationships matter. RAG applications that only search by vector similarity miss the connections between entities. Reco
A production AI application calls multiple LLM providers. The primary model is GPT-4o for complex reasoning, but simple classification tasks use Claude Haiku for cost savings, and the fallback for rat
Machine learning teams face a version control problem that Git does not solve. Git tracks code changes, but ML experiments change more than code — they change hyperparameters, datasets, model architec
Serving a language model in production is an infrastructure problem, not a model problem. The model weights are the same regardless of how you serve them. What differs is throughput (how many requests
A data catalog solves a trust problem. When an analyst cannot find the right table, does not know what a column means, or cannot tell whether data is fresh, they either guess or ask someone. Both outc
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