Pipeline and stage design, forecasting you can defend, lead routing that fires in seconds and CPQ-readiness. Built by Salesforce implementation specialists who also wire AI copilots into the CRM.
Sales Cloud is easy to switch on and hard to make useful. The difference is whether the pipeline, the forecast and the routing rules were designed or improvised.
We design Sales Cloud around how deals actually move, then take away every click that does not help a rep close.
Stages with entry and exit criteria, probabilities calibrated from your win history, and validation that only fires when a stage changes. Record types per sales motion, not per team preference.
Collaborative Forecasts set up by role hierarchy or territory, with forecast categories mapped to how your board already reads the numbers. Quota loading and adjustment rules agreed before the first month-end.
Assignment rules, Flow or Apex depending on complexity, with round-robin, capacity and business-hours logic where needed. Every rule has a fallback owner and a report showing where leads landed.
Products, price books, bundles and discount approval rules modelled so quoting tools slot in later. Quote templates and order hand-off designed even if you start with standard quotes.
Pipeline inspection, stage velocity, conversion by source and rep activity, built for the meetings you actually hold. Managers get a Monday view that needs no export.
Lead scoring, call-note capture and next-step suggestions, using Einstein, Agentforce or an external model through our MCP integration layer. The data model is designed so these features have something to read.
A stage is a claim about the deal. If two reps can disagree about whether a deal belongs in it, the stage is badly defined and the forecast inherits the argument.
We run two workshops. The first maps how deals really progress, including the loops back. The second agrees one observable exit criterion per stage and who confirms it. The example below is illustrative; your stages and probabilities come from your own win history.
| Stage (example) | Exit criterion | Probability | Confirmed by |
|---|---|---|---|
| Qualify | Pain, budget owner and timing confirmed in a conversation | 10–20% | Rep |
| Propose | Written proposal sent to the decision maker | 30–50% | Rep and manager |
| Negotiate | Commercials agreed; in legal or procurement | 60–80% | Manager |
| Closed won | Signed order received and handed to onboarding | 100% | System, from the order record |
Routing, stages and forecast categories are one system. The diagram shows where each rule sits and where an AI copilot feeds in without a rep typing.
Routing fails quietly. A lead lands with the wrong person, nobody notices for a day, and the prospect has already spoken to a competitor. We pick the simplest mechanism that covers your rules, then report on it.
| Method | Best for | Watch out for |
|---|---|---|
| Lead Assignment Rules | Simple territory or product splits with a handful of conditions | Rule order; rule 14 that nobody remembers writing |
| Flow-based routing | Round-robin, capacity caps, business hours, enrichment lookups | Keep the logic in one Flow with a fallback owner and tests |
| Omni-Channel for leads | Inbound queues where speed-to-lead is the whole game | Capacity settings and reps forgetting to set themselves online |
| Apex routing | Complex scoring, external data, very high volumes | Needs unit tests, a release pipeline and a named owner |
Where Apex is the right answer, our Apex and LWC practice builds it with test coverage and a documented deployment path.
Sales motions, current pipeline data and the forecast your board already reads.
Stages, routing rules, forecast categories, product model and security, signed off before build.
Configuration in a sandbox with weekly demos to the sales leads who will own it.
Accounts, contacts and open opportunities cleansed, deduplicated and trial-loaded.
Reps run a real week of selling in the sandbox and the forecast is reconciled against last month.
Cutover at a quiet point in the sales cycle, with the old system set to read-only.
Two to four weeks of daily triage, dashboard tuning and adoption nudges.
A trusted pipeline is the foundation for AI in sales and for connecting the CRM to the rest of the business. Browse all Salesforce services.
Meeting prep, call notes written to the CRM and drafted follow-ups, grounded in your own deal history.
ReadWhere AI pays off in CRM first: lead scoring, next-best-action and email drafting, prioritised over twelve months.
ReadERP for orders and invoices, telephony for call logging, marketing for lead hand-off, warehouse for reporting.
ReadLead scoring and nurture journeys that hand qualified leads into Sales Cloud with consent intact.
ReadCases, omnichannel and knowledge on the same account record, so sales sees service history.
ReadHow regulated sales teams use CRM data for onboarding, risk and compliant outreach.
ReadPipeline design, forecasting, lead routing, reporting and one or two integrations usually take 8–12 weeks, plus 2–4 weeks of hypercare. Quoting, multiple business units or territory management push that towards 16 weeks. We follow the same seven phases as every Salesforce implementation.
Usually not. We make the org CPQ-ready: clean products, price books, quote templates and approval rules. Salesforce CPQ or a third-party quoting tool can then be added later without rework. Most teams add quoting once the pipeline data is trusted.
Fewer required fields, exit criteria enforced only when a stage changes, and mobile layouts that take seconds. A copilot writes call notes into fields for them. Managers then inspect from dashboards instead of chasing. See the sales copilot solution.
Yes. Leads can hand over through a native connector or a documented integration, with the fields and consent flags agreed up front. Our marketing automation and integrations pages cover the options.
Lead scoring from your own history, call and email summaries written to the opportunity, suggested next steps and drafted follow-ups. Whether that runs on Einstein, Agentforce or an external model is a design choice we cover on the Agentforce and Einstein page.
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.