AI Services · Service
AI Data Readiness for Salesforce and HubSpot
Duplicates, empty fields and siloed systems stall Agentforce and every other AI project. Get a data readiness score, then fix the records your first agent reads.
Short answer
AI data readiness is the work of getting CRM data ready for Agentforce, Claude, ChatGPT or Breeze: scoring duplicates, completeness, ownership and connections to other systems, then fixing what the first AI use case reads and setting rules so it stays fixed. Vantage Point scores your data, cleans it and migrates it from Redtail, Wealthbox and legacy CRMs into Financial Services Cloud when the old system is the problem.
Most Agentforce projects that stall don't stall on the agent. They stall on the data: three versions of the same household, owners who left last year, key fields left blank, and client data that lives in a custodial feed or core banking system the CRM never sees. An agent reading that data gives confident wrong answers.
We don't propose a full data overhaul before any AI work. We score the data your first use case depends on, fix that, and put rules in place so it stays fixed. When the old CRM is the problem, we move you to Salesforce Financial Services Cloud with a field-by-field migration map: Redtail, Wealthbox, HubSpot and other legacy systems.
Our complimentary Data Foundations Discovery and POC for Salesforce and the Data Foundation Accelerator for HubSpot are low-risk ways to get the score.
The data readiness scorecard
| What we score | Why an agent cares | Typical fix |
|---|---|---|
| Duplicates | The agent answers about the wrong client or misses history | Matching rules, merge plan, duplicate prevention |
| Completeness | Empty fields mean vague or wrong answers | Required fields, picklists and validation per use case |
| Ownership | Handoffs and briefs route to the wrong person | Owner clean-up, assignment rules, leaver process |
| Relationships | Households, hierarchies and contacts come out wrong | Account, household and contact relationship model |
| Activity | Meeting prep misses recent contact | Email and calendar sync, logging standards |
| Siloed systems | The agent can't see custodial, core banking or policy data | Integration through MuleSoft, Data Cloud or a direct connector |
| Access | An agent could read data it shouldn't | Sharing and field-level security review |
Each dimension gets a score for the objects your first use case reads. The total tells you whether to build now, fix first or migrate.
What you get
- A data readiness score for the objects and fields your first AI use cases read
- Duplicate rules and a merge plan
- Ownership and relationship clean-up approach
- Validation, required fields and picklist standards
- An integration plan for siloed systems your agents need
- A data quality dashboard and an owner for each rule
- Migration from Redtail, Wealthbox or a legacy CRM when that is the fix
How it comes together
- Start from the use case. List the fields each AI workflow reads.
- Score. Measure duplicates, completeness, ownership, relationships and connections on those fields.
- Decide. Build now, fix first or migrate, with the reasons written down.
- Fix or migrate. Merge, reassign and fill, or move the data with a field-by-field map.
- Prevent and monitor. Rules, validation and a dashboard reviewed monthly by a named owner.
Is this the right call?
Good fit when
- You are planning Agentforce, Breeze or Claude and haven't checked the data
- Advisors or sellers don't trust CRM reports today
- Key client data lives outside the CRM
- You are moving off Redtail, Wealthbox or a legacy CRM
Think twice when
- Your data is in good shape; go to integration
Mistakes we help you avoid
Cleaning everything
Boil-the-ocean projects stall. Clean what your first use cases read.
No rule to prevent recurrence
Without validation and ownership, data degrades again within months.
In practice
Financial Services Cloud migrations. We have moved wealth firms from Redtail and Wealthbox to Financial Services Cloud. The field maps are public: Redtail and Wealthbox.
Adoption. One mid-market client went from 30% to 85% Salesforce adoption after we rebuilt their data model and processes. Read the case study.
Questions we get
Do we need clean data before using Agentforce?
You need clean data in the fields your first agent reads. You don't need a perfect CRM. We score and fix those fields first so the agent build can start quickly.
Is there a free way to get a data readiness score?
Yes. Our Data Foundations Discovery and POC for Salesforce and the Data Foundation Accelerator for HubSpot are complimentary and end with a score and a roadmap.
When does migration make more sense than clean-up?
When the old CRM can't hold the relationships, activity or compliance records your agents need, such as households and custodial data in a legacy wealth CRM. We compare both paths before you commit.
Who keeps data clean afterwards?
A named owner per rule on your side, with a dashboard. We can support through a block of hours or a retainer.
Free · About 3 minutes
How ready is your firm to put AI to work?
Answer 10 questions and get an instant readiness score across 10 areas, with the moves to make first for your industry, CRM and goals. No email needed to see your results.