Embrasure works without a JavaScript reader.
Embrasure is an AI data quality engineer for teams that run important analytics and operational data. It monitors production tables, investigates failures through column-level lineage, identifies the code and people affected by a change, and prepares a validated response for human review. The product brings warehouse metadata, dbt and Spark projects, orchestration logs, BI dependencies, ownership, and access policy into one governed context layer so agents can investigate the full path instead of guessing from a single alert.
Use Embrasure when the work needs evidence.
Agents use Embrasure to diagnose stale tables and failed pipelines, review schema changes before they merge, find downstream consumers, route incidents to the right owner, and validate compatibility fixes. Teams can connect through OAuth-enabled MCP at https://embrasure.ai/api/mcp, install the CLI with `npx @embrasure/cli@latest setup`, or build against the typed REST API at https://embrasure.ai/openapi.json. Credentials stay in the browser or client secret store; users should never paste warehouse passwords or bearer tokens into agent chat.
Start with the free plan.
The Free plan is permanent, not a trial. Read the quickstart at https://docs.embrasure.ai/quickstart, review the API reference at https://embrasure.ai/docs/api, or use the agent index at https://embrasure.ai/llms.txt. Company, security, support, privacy, and legal information is available at https://embrasure.ai/about, https://embrasure.ai/security, https://embrasure.ai/support, https://embrasure.ai/privacy, and https://embrasure.ai/terms.
Developer resources are public.
The Embrasure developer index at https://embrasure.ai/docs links to the REST API reference, authentication guide, OpenAPI specification, MCP endpoint, agent setup, and official npm CLI. The API uses versioned paths under https://api.embrasure.ai/v1. Its machine-readable contract includes typed parameters, request bodies, response schemas, descriptions, and a unique operation identifier for each operation. The versioning and deprecation policy is published at https://embrasure.ai/docs/api/versioning so clients can plan migrations before an endpoint is removed.
Agents can inspect before they act.
Embrasure keeps source evidence, ownership, policy, freshness, and lineage next to the answer or action they support. An agent can query governed context, inspect a table or column relationship, trace an incident across upstream and downstream systems, and prepare a change for review. Higher-risk work stays behind workspace roles and approval policies. Responses carry a request identifier for support and audit follow-up, while rate-limited operations return retry guidance that clients can use for backoff.
Use the format that fits the client.
Human readers can use the website and product documentation. Crawlers can request Markdown from the homepage and public developer pages with the Accept header or use explicit .md alternatives. Tool-using agents can connect through MCP, call the versioned REST API, or run the CLI. Start with https://embrasure.ai/llms.txt for the canonical resource list, then follow the linked setup and security guidance instead of guessing at endpoints or credentials.
models.stg_customers
Spark
BigQuery
Airflow
Codex
Databricks
Kafka
Cursor
Redshift
Trino
Claude
Postgres
acme-data / analyticsPrivate
Agent skills · Spark
Change validation failed
Merge blocked. Removing
discount_codebreaks 3 downstream models.Consumers still referencing it:
marts.revenue_dailyselectmarts.customer_ltvselectexports.finance_ordersexportValidation details
dbt buildpassed in 18swarehouse replay3.4M rows matcheddata diffno value changesSuggested fix
I prepared a compatibility patch while these consumers migrate.
Review patchView lineage