Brevo automated go-to-market workflows with Dust & Supabase: 80% faster email personalization, zero engineering tickets
Brevo's sales reps spent 30+ minutes per prospect manually researching across fragmented systems, while 30%+ of RevOps requests required manual human responses despite being answerable from existing documentation. AI agents had no access to CRM data, and finding relevant customer references for sales calls relied entirely on tribal knowledge.
Existing AI agents had no access to the CRM, and alternative data platforms like BigQuery and Snowflake were designed for analytics rather than real-time agent read/write operations.
Brevo achieved an 80% time reduction on email personalization, automated 30%+ of internal support requests through self-serve AI agents, and executed 2,500+ production actions through Supabase-connected agents without writing engineering tickets.
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Frequently asked questions
What did this team achieve with this AI workflow?
Brevo achieved an 80% time reduction on email personalization, automated 30%+ of internal support requests through self-serve AI agents, and executed 2,500+ production actions through Supabase-connected agents without…
What tools did this team use?
Dust, Supabase, MCP, LinkedIn.
What results were reported?
Email personalization time reduction: 80%; Internal support requests automated: 30%+; Production actions executed: 2,500+; Concept to production workflow time: Days, not months (source-reported, not independently verified).
What failed first in this deployment?
Existing AI agents had no access to the CRM, and alternative data platforms like BigQuery and Snowflake were designed for analytics rather than real-time agent read/write operations.
How is this sales outreach AI workflow structured?
BDR selects contacts in CRM → Read CRM history from Supabase → Enrich with web and LinkedIn data → Route to specialized sub-agent → Prompt-level hallucination guardrail → Generate 3 personalized emails → Write email sequences to Supabase.