DNAi Sales & RevOps Systems Architect Certification

Design the enterprise-wide reference architecture for AI across the revenue engine: a layered platform that serves many sales motions and tools from shared, governed components rather than point bots bolted onto each workflow.

• From Point Bots to a Layered Revenue AI Platform
• The Shared Context and Identity Layer
• Build, Buy, and Orchestrate: Drawing Durable Boundaries

Engineer the security, privacy, and data-residency controls that let revenue AI touch customer and prospect data across an enterprise without leaking it, over-sharing it, or violating consent and regional law.

• Least Privilege and Data Boundaries for Revenue Agents
• Consent, Residency, and PII as Architectural Constraints
• Defending Against Prompt Injection and Exfiltration

Stand up the governance, auditability, and model-risk controls that let revenue AI make scoring, forecasting, and outreach decisions an enterprise can defend to auditors, regulators, customers, and its own board.

• An Auditable Evidence Trail for Revenue Decisions
• The Model Lifecycle: Validation, Monitoring, Recalibration
• Fairness, Disclosure, and Contractual Constraints on Outreach

Integrate revenue AI into a real enterprise estate of legacy CRMs, multiple instances, middleware, and partner systems, where you cannot start clean and every system has an owner, a contract, and load-bearing quirks.

• Integrating AI Into Legacy and Multi-Instance Revenue Estates
• The Integration and Event Layer as Orchestration Backbone
• Migration Sequencing and Source-of-Truth Reconciliation

Operate revenue AI as production infrastructure the business depends on at quarter-end: with SLOs, graceful degradation, cost controls, and capacity planning so the system stays trustworthy, affordable, and fast under real revenue load.

• SLOs and Graceful Degradation for Revenue-Critical AI
• Engineering for Quarter-End Load and Provider Limits
• Unit Economics, Observability, and Scaling Affordably