A leading provider of cloud HR software solutions for mid-market enterprise customers.


Our client has a comprehensive suite of HR solutions targeted toward mid-sized companies based in North America. Like all modern enterprise SaaS companies, their focus is to provide their customers with exceptional software that can be fully integrated into best-of-breed app ecosystems. To our client, this means having a comprehensive set of canonical integrations with related 3rd party HR software and services.

To accelerate the development of these integrations and to ensure they are scalable and secure, our client leverages Workato’s embedded integration solution and partners with Dispatch to rapidly design, develop, and deploy a suite of integrations. This has proved to be a very successful strategy that has improved sales velocity and opened up new customer segments.

The success of this approach has resulted in significant adoption of Workato-built integrations across their customer base. A consequence of this rapid growth has been the need to re-design components of the integration architectures to improve task usage and scalability. By improving task consumption across their integration portfolio while maintaining performance, our client can be confident integrations will remain economically efficient and be able to onboard much larger customers than they historically had been supporting.


Refactoring code is a standard development process for improving performance and scalability without altering underlying performance. This process is often required for integration workflows as well, especially as a consequence of growth. In this particular case, the integrations functioned well and met performance requirements, but the client recognized that as they grew, they would need to improve task usage for certain dataflows. By refactoring the integrations to optimize task usage, they would be able to accelerate growth and take on larger and more sophisticated customers in a cost-effective manner.

We assessed the portfolio of integrations to determine which integrations and customers were driving the most tasks and in what processes. From this analysis, we were able to create revised candidate architectures and estimate the task reductions for each potential change. We also assessed the potential impact on performance and customer experience to ensure that the changes would be invisible or positive for customers.

A common way to reduce tasks is to move away from individual processing of actions and move towards batching of transactions. Whether to batch and how much to batch will depend on the individual workflows and the criticality of near-real-time performance for each transaction.

In this case, we were able to use the recently released Workato Collections functionality to batch transactions without impacting the customer experience. Collections are a great way of processing larger datasets in a single task. The size and structure of batches to use are determined by balancing task optimization with performance and in-memory usage.


In terms of optimization, we were able to exceed our projected task savings – providing solutions capable of reducing task usage for the client’s highest volume customers by over 99% without compromising experience or performance. The no-code nature of Workato meant these changes could be implemented rapidly with zero disruption to end customers. Additionally, we were able to reuse a canonical Collections approach in each of the client’s recipes, saving time and costs throughout the project.

Overall, this optimization initiative has enabled our client to gain confidence in their integration approach, as they now know that continued adoption of integration solutions by larger and larger customers can be done economically.

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