Enterprise System Integration
Connecting Pega to the core systems a process touches, with ownership of each piece of data settled explicitly.
Connect Pega to the systems your processes depend on, including the old ones.
A Pega application is usually orchestrating work that lives elsewhere — the customer record in one system, the ledger in another, a mainframe behind both. Nextherrion builds those connections with the awkward realities handled: systems that are slow, occasionally unavailable, or only reachable through interfaces designed decades ago.
Enterprise and legacy connectivity, data virtualization, resilient error handling and the monitoring that keeps a process honest about its dependencies.
Connecting Pega to the core systems a process touches, with ownership of each piece of data settled explicitly.
Consuming and exposing services, with contracts, versioning and timeouts treated as design concerns rather than defaults.
Reaching mainframe and older systems through adapters and intermediate services, without requiring them to change first.
Presenting data from several systems as one view to the process, so case handling does not depend on where each field lives.
Queue and event-based connections where systems should react to change rather than be polled for it.
Retries, timeouts and defined degraded behaviour, so an unavailable dependency pauses a case rather than failing it.
Visibility into connection health and latency, so a slow dependency is identified as the cause rather than blamed on the platform.
Keeping connections inventoried and owned, since integrations accumulate and each is a dependency somebody should know about.
A process is only as reliable as the systems it calls. Most of the engineering is in how it behaves when one of them is slow or absent.
Usually, through adapters, messaging or an intermediate service. It is more work than a modern API, and that difference is assessed rather than assumed.
Defined behaviour rather than failure: pause the case, retry, or proceed on degraded information. Which one is a business decision made during design.
Depends on volatility and volume. Fetching keeps one source of truth; caching helps performance and introduces staleness. Both are deliberate choices.
Inventory and ownership. Each integration is a dependency, and estates accumulate them faster than anyone tracks.
Monitoring per connection answers that. Without it, latency in a dependency is routinely attributed to the platform calling it.
Yes, and it is usually better. One connection at a time, monitored, is easier to verify than a large integration release.
AI and Generative AI, agent frameworks, cloud platforms, data tooling and modern application stacks — chosen per problem rather than per preference.















Start with a dependency review.