Resources / Glossary
The concepts behind trusted AI analytics.
Short, plain-English explanations of the ideas every serious AI analytics buyer ends up researching — semantic layers, context layers, RAG, text-to-SQL, hallucinations, and more.
Glossary
What is a Semantic Layer? (And Why It Matters for AI Analytics)
A semantic layer is the governed translation between raw warehouse tables and the business questions people actually ask. In the AI era, it is the layer that decides whether an LLM hallucinates or answers correctly.
Glossary
What is a Context Layer? (The Missing Piece in AI Analytics)
A context layer captures the business logic, exceptions, and analyst knowledge an LLM needs to produce trusted answers. It is the piece most generic AI analytics tools skip.
Glossary
Why Text-to-SQL Fails on Enterprise Data (and How to Fix It)
Text-to-SQL demos look magical on toy schemas and fall apart on real enterprise warehouses. Here is why — and what actually works.
Glossary
AI Hallucinations in Analytics: Why They Happen and How to Stop Them
Hallucinations in analytics are not about the model inventing facts. They are about the model confidently using the wrong definition. Here is the root cause and the fix.
Glossary
Analyst-in-the-Loop: The Operating Model for Trusted AI Analytics
Human-in-the-loop is not a workflow feature. It is the operating model that lets AI analytics scale without losing trust.
Glossary
What is Trusted AI Analytics?
Trusted AI analytics means every answer is grounded, explainable, reviewable, and owned. Here is the working definition and what it takes to deliver it.
Glossary
Retrieval-Augmented Generation (RAG) for Analytics
RAG works for documents. For analytics, you need something stronger — retrieval over governed business context, not unstructured text.
Glossary
What is Warehouse-Native AI Analytics?
Warehouse-native AI analytics means compute stays in Snowflake, Databricks, or BigQuery. Here is why that matters for security, governance, and cost.