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Yes. A scalable case model starts with the right structure, not just a queue. Our consultants design case record types per support process, configure assignment rules and queues so cases route to the right team automatically, and build escalation rules tied to entitlements and milestones so nothing breaches SLA silently. They set up case teams for collaborative resolution, auto-response rules for acknowledgement, and Email-to-Case and Web-to-Case so inbound contacts become trackable records rather than lost emails. They configure status and reason picklists that feed meaningful reporting, build case lifecycle automation with Flow, and design the agent console layout so reps see the customer context contact, account, history, entitlements on one screen. They plan for volume with selective queries and sharing that does not collapse at scale. The PM scopes your support channels and SLAs so the model fits your operation from day one.

Yes. Omni-Channel is what turns a pile of queues into balanced, skills-based work distribution. Our consultants configure presence statuses and capacity per agent so no one is overloaded, build routing configurations and queues with priority, and set up skills-based routing so a billing case reaches a billing-trained agent rather than the next free body. They enable the Omni-Channel widget in the Lightning console, configure secondary routing and overflow for surge handling, and set up Omni-Channel Supervisor so team leads see live workload and wait times. They route work items across channels cases, chats, messaging, voice through one engine so reporting and capacity planning are unified. They tune the model with real volume data after go-live rather than guessing. The PM confirms your channel mix and staffing so routing matches how your contact centre actually operates.

Yes. Salesforce Knowledge done well cuts case volume and speeds resolution. Our consultants set up the Lightning Knowledge data model with article types and record types, design a category and data-category hierarchy so articles surface in the right context, and build the authoring and publishing workflow with approval so content stays accurate. They embed Knowledge in the agent console so reps attach and reuse articles during case work, surface relevant articles via Einstein search and case-to-article suggestions, and publish to a self-service Experience Cloud site or help centre for customer deflection. They configure article ratings and analytics to find gaps and stale content, and tie deflection metrics to case volume so the value is provable. They also plan content migration from a legacy help system. The PM confirms your content sources so the Knowledge base launches with real, findable articles.

A PM is assigned within 10 minutes of booking and scopes your requirement on the first call. For a clearly defined need an SLA and entitlement setup, an Omni-Channel configuration, a Knowledge launch, an Einstein Bot a vetted consultant typically starts within a day. We offer three engagement models: project-based (a defined deliverable such as a case management redesign or a bot deployment), dedicated (a consultant embedded with your support team across a transformation programme), and on-call advisory (hours drawn down for tuning and incident support). You can scale up or down between phases and cancel after any engagement. The PM coordinates the consultant with your support leadership, agents, and any telephony or messaging vendors so changes to routing, SLAs, and the console land without disrupting live customer support.

Yes. Our consultants build Einstein Bots to deflect and triage before a human is needed. They design conversation dialogs and intents, connect the bot to Knowledge so it answers from published articles, and configure entity extraction so the bot captures order numbers, case reasons, and customer details cleanly. They build escalation to a live agent through Omni-Channel with full context handed over no "please repeat your issue" and deploy the bot across web chat, WhatsApp, and messaging channels. They set up the bot performance dashboard to measure containment and deflection, then iterate the dialogs on real transcripts rather than launching and walking away. They know when a deterministic bot is enough and when generative answers via Einstein for Service or Agentforce add value, and they design guardrails so AI responses stay grounded in your approved content. The PM confirms your channels and content so the bot launches grounded and useful.