FlatClaw, Private AI Platform
All use case spotlights
Sales & MarketingCustomer EngagementSoftware / TechnologyProfessional Services

Sales team

≈ $120M revenue (approx.) · 35-person sales team · one CRM

CRM-connected agent

Pipeline questions, follow-up drafts and account summaries with the CRM connected through each user's own OAuth credentials, so nobody's agent can reach a deal its user cannot see.

Organization
Sales team on a CRM
Reality
Deal data visible to some, not to all
Runs on
The company's cloud tenancy
Scope
A CRM-connected agent with per-user credentials
The situation

Where they started.

Sales wanted an agent that could answer pipeline questions, draft follow-ups and summarize accounts. The blocker was access: a shared integration would let anyone's assistant see everyone's deals, and outbound messages sent by an agent without review were a non-starter.

What FlatClaw does

What was built.

  • The CRM connected as a first-party MCP service with credentials scoped to tenant, user and service: each person's agent sees exactly what that person can see.
  • Outbound messages composed by the agent and approval-gated before they leave.
  • Pipeline questions, account summaries and follow-up drafts as a conversation with the data.
  • Scheduled work, like a Monday pipeline brief, run per user with that user's own access.
Results

What changed.

  • No one's agent can reach a deal its user cannot.
  • Follow-ups drafted in minutes and sent only after a human says so.
  • A pipeline brief that arrives before the meeting instead of during it.
  • The same connector pattern for the next system sales asks for.
Why private

Pipeline data is the company's forecast. Per-user credentials and private inference keep it exactly as visible as it was before the agent arrived, and no more.

Runs on

The company's cloud tenancy

The stack

Components involved.

CRM MCP connectorPer-user OAuth credentialsApproval engineScheduled tasksFlatClaw PortalPrivate inference
Your workflow

Have one like it?

Every spotlight started as a conversation about a process nobody liked doing, under a data-locality constraint.