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Salesforce Data Cloud
Data Cloud is the most consequential thing Salesforce has shipped in a decade, and the easiest to buy without a plan. Implemented well, it becomes the identity layer that makes every other cloud smarter, and the grounding that makes AI agents safe to deploy. Implemented as a science project, it becomes shelfware with a consumption bill.
Fixed fee. A contractual floor of finished work every month, with a refund behind it. Unified profiles in weeks, not quarters.
Sound familiar?
CRM, ERP, e-commerce, support, and marketing each hold a fragment, and they disagree.
Someone pulls CSVs, dedupes in a spreadsheet, and uploads. Every campaign, again.
The customer did something an hour ago; your systems find out Thursday.
Consumption is billing; value isn't landing, because nobody scoped the use cases before the ingestion.
Every agent pilot stalls at the same wall: the model has nothing trustworthy to ground on.
The build
Data Cloud scoped backward from the business outcome: which decisions, which activations, which agents need unified data. This ordering is the difference between an identity layer and a data swamp, and it also controls your consumption bill.
Batch and streaming ingestion from Salesforce orgs, marketing platforms, e-commerce, data warehouses, and files. Connector configuration, refresh strategy, and the web and mobile SDK for behavioral event capture.
Zero-copy access to Snowflake, Databricks, BigQuery, and Redshift, so warehouse data participates in profiles and segments without duplicating storage or blowing up consumption.
Source data mapped to the standard data model: DLO-to-DMO mapping, transformation, and the modeling decisions that determine whether downstream segmentation is easy or archaeological.
Match rulesets designed for your data reality: deterministic and fuzzy matching, party identification strategy, and reconciliation rules. Tuned and tested against real records, because over-matching is worse than under-matching and both are silent.
Lifetime value, engagement scores, recency/frequency metrics, and the derived attributes your segments and agents actually need, computed in the profile instead of in seventeen spreadsheets.
Segment design and publication to activation targets: Marketing Cloud, advertising platforms, and back into CRM. Attribute selection, refresh cadence, and consent honored end to end.
Streaming events, real-time identity, and profile APIs, so the website, the contact center, and the agent all see what the customer did minutes ago, not last batch.
Data Cloud as the retrieval and grounding layer for AI agents: search indexes, retrievers, and profile context, so agents answer from governed data instead of vibes. This is the highest-ROI Data Cloud use case in most orgs. Agentforce →
Consent data model, data spaces for brand or regional separation, access policies, and the governance documentation, generated automatically, that lets legal and security sign off.
Credit modeling before you build, monitoring after you ship, and architecture choices (zero-copy, refresh cadence, retention) made with the meter in mind. Nobody should discover their Data Cloud bill in arrears.
Month one
Use cases scoped and ranked with your leadership. DAISA maps sources, models, and data lineage. First connections live.
Priority sources ingested or federated, mapped to the model, identity rulesets drafted against real records.
Identity resolution tuned, first calculated insights running, first segment built and validated.
A segment published to a real target, or an agent grounded on governed data, plus the 90-day roadmap and consumption model. Value in month one, not phase three.
The guarantee
You tell us the results you need: match rate, first activation live, agent grounding shipped, consumption under control. We value each one together and write a monthly floor of finished results into the contract. A floor, not an estimate. Cash back if we miss. No change orders, ever.
In the contract before we start.
Same percentage of fees back that we missed by. Cash, not credits.
New priority moves to the front. Nothing becomes an invoice.
FAQ
Maybe you don't need it, and we'll say so. Data Cloud's case is native activation into Salesforce apps and AI: profiles that show up in the console, ground the agents, and drive the journeys without integration glue. With zero-copy, it complements the warehouse rather than competing with it.
By architecting with the meter in mind: use-case-first scoping, zero-copy where it fits, sensible refresh cadences, and a credit model built before ingestion starts. We treat the consumption bill as a design constraint, not a surprise.
First unified profiles and a first activation inside month one on a focused scope. A broader rollout across many sources is sequenced over a 6 to 12 week Launch, each stage shipping something usable.
Not strictly, but agents grounded on governed, unified data are the difference between a demo and a deployment. If AI is the goal, Data Cloud is usually the first mile.
Launch Implementations are fixed-price scoped projects. Managed PODs run from $5k/month to enterprise coverage. See the calculator →
Bring us your systems map and your top three use cases. We'll show you what a Data Cloud POD ships in month one and put a delivery floor on paper.
