Learn how Prodly makes Salesforce metadata deployment fast and traceable. This walkthrough shows how to deploy page layout and custom field changes between orgs using a full metadata comparison or diff viewer, or Metadata Quick Select for faster, targeted deployments straight from the source. You'll also see how to link a deployment to its Jira epic for full traceability between your work items and what actually shipped to each environment. If your team is stuck doing manual metadata comparisons or losing track of what deployed where, this is what streamlined Salesforce DevOps looks like.

Learn how Prodly makes Salesforce metadata and data deployment simple and repeatable. This walkthrough shows how to compare a Dev and QA org, reviewing color-coded metadata and data results to see exactly what's new, changed, or different between environments. You'll also see how to save those results as a locked, reusable bundle, combine bundles from multiple developers into a single release, and deploy everything together in one click. If your team is manually tracking Salesforce changes across orgs or struggling to coordinate deployments across developers, this is what streamlined Salesforce DevOps looks like.

See how Prodly makes migrating complex Salesforce data across environments fast and controlled. This walkthrough covers deploying a price rule from a sandbox to an integration org using Prodly's pre-built data sets for CPQ, Billing, and Field Service, filtering deployments down to exact records with queries, validating impact before you deploy, and fine-tuning your selection so only the records you want, and their related conditions and actions, actually move. If your team is manually recreating configuration data between orgs or worried about what a deployment might break, this is what safe, query-driven Salesforce data migration looks like.

Learn how Prodly Test safeguards your Salesforce CPQ pricing logic through automated regression testing. This walkthrough shows how to set up a test template by selecting key fields to monitor on quote and quote line objects, then create a test case against a quote containing a mix of hardware and software products. You’ll also see how scheduled tests catch pricing errors automatically: in this example, a new hardware promo rule with an inactive target condition accidentally applied a discount to high-margin software lines, and the scheduled test flagged the deviation, giving the team a chance to fix it quickly. If your team is manually spot-checking CPQ pricing after every release or deployment, this is what automated regression testing looks like.