Embrasure provides a data‑infrastructure layer that connects AI agents—such as those built on Codex, Claude, or Slack—to existing data platforms like Snowflake, Databricks, and Iceberg. The service intercepts queries, executes them using the end‑user’s permissions, captures silent failures, and caches verified results to avoid redundant processing costs. By integrating with tools teams already use, Embrasure streamlines reliable, cost‑effective data access for AI‑driven workflows.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents embedded in tools like Codex, Claude, and Slack often struggle to access enterprise data reliably, encountering silent query failures and costly redundant computations. Organizations also need to enforce user-level permissions and maintain governance when agents retrieve data from warehouses such as Snowflake, Databricks, or Iceberg.
Solution
Embrasure offers a middleware layer that sits between generative AI agents and enterprise data warehouses. It intercepts each query, executes it using the requesting user’s access rights, and captures any silent failures for review. Verified query results are cached and reused, eliminating unnecessary compute expenses while preserving result accuracy. The platform provides a unified interface for connecting multiple data sources, managing workspaces, and defining approval policies for higher‑risk operations. Administrators can monitor agent activity, enforce compliance, and integrate the service with existing collaboration tools such as Slack. This infrastructure enables teams to ask live, governed questions of petabyte‑scale data without building custom integrations.
Target Audience
Primary customers are product teams and data‑driven organizations that embed AI agents in internal tools, as well as enterprise data engineering groups needing secure, governed access to their data warehouses.
Features
- Middleware that routes agent queries to Snowflake, Databricks, Iceberg, and other warehouses with per‑user permission enforcement
- Automatic detection and logging of silent query failures for audit and debugging
- Result caching layer that reuses previously verified query outputs to reduce compute costs
- Workspace and role‑based access controls for configuring data sources and reviewing agent actions
- Approval policies that require human sign‑off for high‑risk data modifications
- Integrated dashboards and scheduled insights generated from query results
- API and SDK support for embedding the service in Codex, Claude, Slack, and custom agent clients