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AI is only as smart as the data it can access. We implement Salesforce Data Cloud to unify your fragmented customer data, creating the real-time context required to power autonomous Agentforce deployments.
You can't point an AI agent at a messy database and expect good results. When customer data is scattered across Salesforce, AWS, Snowflake, and marketing platforms, your AI lacks context.
The same customer exists as a Lead, a Contact, and a subscriber in Marketing Cloud. The AI treats them as three different people.
Without real-time streaming ingestion, your support agent might try to upsell a customer who just filed an angry complaint.
When data isn't harmonized into a standard format, LLMs make incorrect assumptions, eroding trust in your autonomous systems.
We set up zero-copy integrations with Snowflake/AWS and native connectors for external systems, bringing all data into Salesforce without heavy ETL pipelines.
We map disparate data structures to the standard Customer 360 Data Model, so an "Order" looks exactly the same regardless of which system generated it.
We configure identity resolution rules to merge duplicates into a single golden record. This unified profile is what feeds directly into Agentforce and Marketing Cloud.
Salesforce's Einstein Trust Layer requires grounded data to function securely. Data Cloud acts as the grounding mechanism.
When you ask an Agentforce SDR to "research this account," it relies on the unified profile built in Data Cloud to synthesize the account's history, current products, and recent website engagement.
Let's assess your current data architecture and map exactly what it will take to get Data Cloud running in your org.
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