Enables users to investigate infrastructure incidents in plain English, correlate observability and deploy data with runbooks, and get evidence-backed root-cause proposals with approval-gated remediation.
AI-driven MCP server that audits, profiles, detects schema drift, and auto-generates documentation for dbt projects, enabling natural language interaction with your dbt project's health.
Data observability for AI agents. Query alerts, monitor freshness, investigate schema drift, and trace lineage across your data warehouse via 53 MCP tools.
Agentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.
An MCP server that exposes a dbt project's run state as tools, enabling AI assistants to answer questions like 'is the warehouse healthy?' or 'what broke and why?' via plain English, using read-only access to dbt artifacts and warehouse data.
Enables AI to automatically perform Root Cause Analysis for app issues (e.g., sessions not recording, heatmap empty, replica drift) by querying logs, MongoDB, Shopify, and rendering rrweb replays.