Introduction

DataStori is a SaaS platform that makes your business AI-ready. It connects to your business applications, unifies the data, and adds business context so the same layer can feed AI agents, analytics and reporting, and machine learning.

DataStori is hosted in AWS US. Key features of DataStori:

• Data Security: DataStori runs data pipelines in customer's cloud. Customer data never leaves their cloud, either in processing or storage.

• Add New Applications on the fly: DataStori's agents generate data pipelines dynamically from API documentation, emails, SFTP folders and SQL database connections.

• Data Testing: DataStori runs automated tests on your data to ensure data quality.

• Semantic Models: DataStori builds a semantic model from the data you ingest. Tables and columns come from the data catalog, and you define how those tables relate and which business metrics sit on top of them. The model is centralized, so every transformation and downstream consumer uses the same definitions of entities, joins, and measures.

• Context Engineering: Add the business meaning your AI agents need to use the data correctly. Describe tables and columns in the catalog, or generate those descriptions, and combine them with relationships and metric definitions. Agents read that context to choose the right tables, interpret a question, and explain a result.

• Transform Data: Use DataStori's agents to turn a plain-language goal into a transformation. The agent reads the semantic model, identifies the relevant tables and relationships, and produces a script that joins, filters, aggregates, or unifies data across sources. The result becomes a versioned pipeline that runs on a schedule and writes a business-ready dataset to the gold layer.

• Data Governance: Define which datasets each MCP user can access. Rules decide what an agent or user is allowed to see, so access stays limited to the datasets you permit.

Read the next few sections to understand how DataStori works.