The operating layer
your post-sales team has been missing

Quivly connects directly to Snowflake, BigQuery, and Redshift via a read-only connector. You define which tables and fields to sync, and Quivly incorporates them into its unified customer model alongside data from your CRM, support tools, and product analytics. Custom metrics and attributes are first-class citizens — you can use them in health score models, playbook triggers, and dashboards.

Many teams do use Quivly as a full replacement for legacy CS platforms, particularly when they want tighter automation and AI capabilities. Others run Quivly alongside existing tools, using it as the intelligence and automation layer while keeping their current platform for certain workflows. We’re happy to walk through the comparison for your specific stack.

Quivly’s playbooks run automatically — the trigger fires, the action executes, the task is created or the email is sent. CSMs don’t have to remember to follow the playbook because Quivly runs it for them. For plays that require CSM action, Quivly creates a task in their queue and tracks whether it was completed. You get a full audit trail and completion rates per CSM.

Quivly includes a live reporting suite covering: portfolio health over time, renewal forecast vs. actuals, churn and expansion by cohort, playbook performance (saves, expansions, open rates), and CSM activity metrics. Reports are shareable and can be scheduled for automatic delivery to leadership. Custom dashboards using your own metrics are also supported.

Core integrations (CRM, product analytics, support) are typically live within the first week. Full data unification including custom warehouse connections takes two to three weeks depending on complexity. Quivly’s implementation team handles all of the integration work — your engineering team doesn’t need to be involved unless you’re connecting a custom data warehouse.