Application Assessment
Establishing what the application contains — rules, customization, dead configuration — which is usually more than anyone expects.
Upgrade, untangle and tune Pega applications that have become hard to change.
Long-running Pega applications accumulate: rules nobody can attribute, custom code that bypasses the model, versions left behind because upgrading felt risky. Nextherrion assesses what is there, removes what is not earning its keep, and brings applications back to a state where the platform's own capability does the work again.
Application assessment, version upgrades, rule rationalization, performance tuning and the ongoing support that keeps an estate healthy.
Establishing what the application contains — rules, customization, dead configuration — which is usually more than anyone expects.
Moving to a supported release with impact assessed in advance, so upgrade is a planned exercise rather than a deferred risk.
Consolidating overlapping and superseded rules, which is what makes an application comprehensible enough to change safely.
Replacing customization with platform capability where it now exists, reducing what has to be revalidated at every upgrade.
Addressing rule resolution, database access and case volume issues, which degrade as applications and data grow.
Reviewing application structure and reuse, since layering decisions made early determine how much later work costs.
Sequenced cleanup that keeps the application running throughout, rather than a freeze while it is put right.
Support and enhancement capacity for applications in production, where that skill is not available in-house.
Applications rarely fail outright. They accumulate until change becomes risky, and the organization quietly stops asking for changes.
It becomes one — support, security fixes and platform capability all depend on it. The longer it is deferred, the larger the eventual upgrade.
That is what impact analysis establishes first. Heavy customization is where upgrade effort concentrates, which is also the argument for reducing it.
Yes. Work is staged and regression-tested so the application keeps running, rather than freezing while it is put right.
Usage analysis and dependency tracing. Removing rules on assumption is how a cleanup becomes an outage.
Usually. Slowness in mature applications typically traces to rule resolution, data volume or unindexed access, all of which respond to targeted work.
Rarely. The application encodes years of process decisions that exist nowhere else, which a rebuild discards and then rediscovers expensively.
AI and Generative AI, agent frameworks, cloud platforms, data tooling and modern application stacks — chosen per problem rather than per preference.















Start with an assessment. Knowing what is in there is the prerequisite.