Optimize Technician Assignment Logic

Our Service Cloud org is live with case management, automated work-order creation, LWC dashboards, and AI-driven prioritisation, yet the technician assignment piece inside the work-order lifecycle is still too brittle. The existing Apex and Flow logic works, but it has become overly complex, hard to maintain, and occasionally sends jobs to the wrong skill set when multiple constraints overlap. Here is the current state at a glance: • Apex classes decide which queue or individual picks up the work order. • Flows update entitlements, turn SLAs into milestones, and trigger child work-orders. • Agentforce recommendations surface, but they are not always respected when the custom logic runs. What I need now is a streamlined, auditable assignment engine that: 1. Evaluates skills, geography, capacity, and priority without hard-coding every rule. 2. Leverages standard Service Cloud features (Omni-Channel, Dynamic Forms, Assignment Rules) wherever possible, falling back on Apex only when unavoidable. 3. Plays nicely with our AI predictions so the “next best technician” suggestion is either accepted automatically or surfaced clearly to dispatchers. 4. Includes clean, well-commented code/metadata, 90 % test coverage, and a short README so my in-house admin can tweak thresholds later. Deliverables • Refactored Apex/Flow solution (managed package not required) • Configuration steps or an unlocked package for deployment between sandboxes → production • Unit tests and sample data scripts • Brief hand-off walkthrough (recording or live) Acceptance will be based on: • No regression on existing SLA or child-work-order functionality • Technician match accuracy ≥ 95 % on our historic dataset • End-to-end assignment time under 3 seconds for a new work order If you have proven experience blending declarative Service Cloud tools with Apex for complex routing scenarios, this should be straightforward. I’m happy to grant access to a full sandbox so you can inspect the current implementation before we lock in scope and milestones.

Project ID: 40417522