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Bruin
The AI data agent that collaborates with your team
45 followers
The AI data agent that collaborates with your team
45 followers
The AI Data Agent that lives, breathes, and collaborates with your team. A data analyst agent that integrates into your Slack, Teams, etc. and built on top a strong context & semantic layer using Bruin's very own open-source tools or from your existing dbt, LookML, etc. Make your agent live inside every conversation, collaborate across teams, deep dive into the data, find insights, and turn them into actionable tasks.
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"Collaborates with your team" is the part most data agents undersell. Curious whether your collab surface is synchronous (live chat in-context) or async (PR-style review). Different products entirely.
Bruin
@pengspirit666 Both actually. The chat interface (web, slack, teams, etc.) is the primary way for collaborating with the agent but if the agent is given access to your repo, it can go ahead and create a PR. This can be updating the metadata, context, and semantics layer or fixing data issues (e.g. fix the join in a SQL asset).
Also worth mentioning that the agent can also take actions as well - for example, it can rerun a pipeline to backfill missing data, or trigger a reverse-ETL asset for a specific client ID to update their CRM profile.
@arsalan_bruin The PR-creation surface is the part I keep coming back to as a
contract-design problem. If the agent can fix a SQL join via PR, the
review surface is doing two jobs at once: catching the agent's bad PR,
and catching scope drift the reviewer would not have noticed without
automation in the loop.
Curious whether you saw reviewer decision-time go up or down once agents
started producing PRs. My intuition is that good PRs from a competent
agent compress review time, but borderline PRs eat more reviewer
attention than equivalent human PRs because the failure mode is
unfamiliar.
The reverse-ETL trigger by client ID is a clean primitive — small enough
scope that an audit-log entry plus a notification covers the read-back
loop. The harder version is the multi-asset cascade where reverse-ETL
into the CRM kicks off a downstream segment recompute. Where do you
draw the line on what the agent can chain without confirmation?
Can Bruin ask follow up questions when a request is not clear?
Bruin
@karimbenkeroum yep. it allows you to explain the business context and then attributes data to the business context. then if something is vague / irrelevant it asks you back instead of making up numbers/concepts or assuming.
Embedding a data agent directly into team conversations is interesting. how are you handling query validation and permission boundaries across shared data environments?
Interesting approach combining semantic layers with conversational workflows instead of building another standalone AI dashboard. The Slack/Teams integration feels practical for real analyst collaboration. Curious how you handle metric consistency and hallucination risks when multiple teams query the same data differently.