Problem → Solution → Impact
Problem: every change to a core object carried risk nobody could actually measure, so teams played it safe and deferred. Solution: nCoder replaced guesswork and tribal memory with a complete, evidence-backed view of every dependency. Impact: work that used to take weeks of careful digging became something a reviewer could check and trust in a single sitting.
About the client: a 12-year Salesforce org
The client is a global manufacturing company running a Salesforce org that's been in continuous use for 12 years — around 400 custom objects, a 14-person delivery team spread across three squads, and integrations into ERP, CPQ, and two data warehouses. Releases go out every two weeks.
Three different consultancies have worked on this org over the years. Two of the original architects are still around, but neither builds on it day-to-day anymore. Documentation exists, technically — it's about four years out of date, and everyone on the team quietly knows not to trust it too much.
Over time, even the smallest changes started to feel risky. A picklist update that should take twenty minutes could sit untouched for months, not because anyone doubted it was easy, but because nobody could honestly say what else it might touch.
The challenge: why a twenty-minute change sat for months
The problem wasn't technical difficulty. It was an inability to know, with any confidence, how far a change would reach — and no reliable way to find out.
The approach: intelligence layered onto the existing org
nCoder layered its impact analysis engine directly onto the client's existing Salesforce org — nothing torn out, nothing rebuilt, no disruption to how the team already worked.
Business impact: an honest, defensible "yes"
What nCoder really gave the team wasn't speed. It was something they'd never had before: an honest, defensible yes to the question "have we checked everything?"
Risk became something you could actually approve
A change with three known dependencies is easy to sign off on. Once the risk had a boundary, it stopped being a reason to wait.
Fewer surprises after release
Live-path verification before shipping meant "we didn't find anything" was replaced by an answer someone had actually confirmed.
Old, forgotten dependencies came to light
The traversal turned up a flow and a downstream scheduled report the team had genuinely never known about — the exact kind of blind spot that caused their earlier incident.
The backlog started moving again
Changes that had sat untouched for months could now be reviewed and shipped in one sitting — no week-long assessment required.
The team stopped depending on who was still around
Coverage no longer hinged on which engineer happened to remember a decision from three years ago.
The real win wasn't moving faster for its own sake — it was finally having a repeatable, defensible answer to a question every Salesforce team asks before every release.
Results: the measurement framework
Measurements that would evidence this case in a live deployment. Presented as slots, not figures — nothing here is asserted until a named deployment produces it.
| Metric | Unit |
|---|---|
| Investigation time, manual baseline | Engineer-hours |
| Investigation time, with nCoder | Engineer-hours |
| Recall vs. hand-compiled dependency list | Percent |
| Undocumented dependencies surfaced | Count |
| Backlog items cleared without a dedicated assessment | Count per quarter |
| Incidents from unforeseen dependencies | Per release window |
Recall against a hand-compiled list is the credibility measure. Backlog items cleared is the business measure — it captures the compounding effect, not just the single-task saving. Capture the manual baseline during the first assessment run; it can't be reconstructed afterward.
A representative composite drawn from problems common to large Salesforce orgs. The situation and approach describe real platform capability; the organisation is not a specific customer, and no outcome or figure is asserted.
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