Salesforce projects: consulting and end-to-end implementation

An enterprise Salesforce project does not start with code — it starts with the design of the data model and the customer journey. CCX runs Salesforce projects across five linked phases: discovery (mapping processes, integrations and KPIs), architecture (object model, clouds in scope, integration strategy), build (sprint-based implementation with demos every two weeks), go-live (controlled cutover with a rollback plan) and hypercare (assisted stabilization through the first weeks). We are a Salesforce Commerce Partner with more than 200 enterprise projects delivered across Brazil and LATAM.

Working with a specialized Salesforce consultancy changes three things in practice. First, predictability: senior squads that onboard quickly avoid the rework that blows up schedules. Second, correct architecture from the first release — data model, permission governance, integration strategy (MuleSoft vs. direct API vs. events) — precisely the points where generic implementations stumble. Third, total cost of operation: architecture decisions made with scale in mind avoid technical debt that is expensive to unwind two years later.

CCX's Salesforce portfolio covers Commerce Cloud (B2C storefront on SFRA/PWA Kit and B2B Commerce), Marketing Cloud (Journey Builder, Data Extensions, attribution), Sales Cloud and Service Cloud, Data Cloud (CDP) with cross-channel activation, Agentforce (AI agents) and MuleSoft API-led integrations. We work on greenfield builds as well as migrations from legacy platforms to Salesforce.

Investment in a Salesforce project varies with scope, the clouds involved and the volume of integrations — there is no honest price list without a diagnostic first. As an enterprise market reference: Salesforce Commerce Cloud implementations typically start at R$ 250,000 and can exceed R$ 2 million in operations with multiple clouds, B2B and complex integrations. Dedicated squads for support and continuous evolution start at around R$ 35,000/month. Salesforce license cost (per org, per cloud, per user or per order) is billed separately, directly with Salesforce.

Three factors push the cost up: the number of clouds (each additional cloud is a subproject with its own data model), the depth of the integrations (SAP, Protheus, legacy ERPs, loyalty systems) and data requirements (Data Cloud, profile unification, LGPD). CCX runs a free 60-minute diagnostic and delivers, within 7 days, a comparative TCO with a phase-by-phase schedule — so you can weigh proposals against real numbers instead of ballpark guesses.

Agentforce is Salesforce's AI agent layer, built on top of Data Cloud and Einstein, which makes it possible to automate service, sales and commerce operations with governed autonomous agents. In practice, an Agentforce agent resolves order questions, recommends products, opens and triages tickets and executes actions in integrated systems — always within guardrails the company defines. It is the fastest-growing keyword in the Salesforce ecosystem, and the difference between a pilot and an agent in production lies in the data foundation and in governance.

Agentforce makes sense when a unified data foundation already exists in Data Cloud (or is built alongside the project): without trustworthy data, the agent hallucinates or answers generically. CCX implements Agentforce on three fronts: modeling topics and actions (what the agent is allowed to do), integration with the systems of execution (commerce, service, ERP via MuleSoft) and observability (containment metrics, resolution rate, human escalation and decision auditing). We deliver the agent with security guardrails and a gradual, channel-by-channel rollout plan.

Most Salesforce projects that overrun their deadlines fail for the same repeated reasons: a data model designed too late, Apex customization where declarative configuration would have done the job, integration treated as a last-minute detail, and no strategy for test data and sandboxes. Each of these mistakes costs weeks of rework and, worse, creates technical debt that makes every future evolution of the platform more expensive.

CCX prevents these mistakes with mature engineering practices: data model and integration strategy locked down during the architecture phase, a preference for declarative configuration (Flows, not Apex, wherever possible), integrations built with idempotency, retry and a Dead Letter Queue, and a CI/CD pipeline with per-environment sandboxes. The result is a project that goes live on the agreed date and evolves predictably — without the rework cycle that consumes 6 to 12 months a year in poorly architected operations.