Apex Development
Server-side logic written with governor limits treated as a design constraint rather than as an error discovered under load.
Apex, Lightning and custom logic — for the parts configuration genuinely cannot reach.
Some requirements exceed what declarative Salesforce can express, and building those properly matters more than usual: custom code lives inside a platform that updates three times a year. Nextherrion writes Salesforce code the way it should be written — governor-aware, tested, and confined to where configuration genuinely could not do the job.
Apex, Lightning Web Components, custom objects and logic, packaging and the test coverage the platform requires and quality demands.
Server-side logic written with governor limits treated as a design constraint rather than as an error discovered under load.
Custom interface components where standard ones do not fit, built to behave consistently inside the Lightning experience.
Extending the data model where the standard objects do not represent your business, without duplicating what already exists.
Trigger frameworks that stay predictable as they grow, instead of the tangle of overlapping automations most orgs accumulate.
Bulk and scheduled processing designed for volume, which is where naive implementations meet platform limits.
Tests that assert behaviour rather than merely satisfy the coverage requirement — the difference shows at every platform release.
Source control, packaging and deployment pipelines, so changes move between orgs reproducibly rather than by hand.
Untangling customization that has accumulated past the point of safe change, sequenced so the org keeps working throughout.
Custom Salesforce code is revalidated every time the platform updates. How it was written determines whether that is routine or an event.
When declarative tools genuinely cannot express the requirement, or would do so at a complexity that is harder to maintain than code. We check that first.
Well-written, well-tested code usually survives. Code written to pass a coverage threshold rather than to assert behaviour is where release problems concentrate.
Yes, usually incrementally — consolidating into a predictable framework rather than rewriting everything in one release.
As a design constraint from the start: bulkified logic, asynchronous processing where appropriate, and testing at realistic volume.
Yes, and it tends to work better. Admins know what the business actually does, which is exactly what keeps custom work proportionate.
Through source control and a deployment pipeline. Change sets by hand do not scale and are difficult to audit.
AI and Generative AI, agent frameworks, cloud platforms, data tooling and modern application stacks — chosen per problem rather than per preference.















Start with a fit review — sometimes configuration reaches further than expected.