The problem this solves
AI tools are only as good as the context they can reach. An AI agent drafting outreach from a CRM with empty notes fields produces generic filler; a scoring model trained on records where half the properties are blank produces confident nonsense. Companies discover this after the rollout, when the pilot underwhelms and the tool gets blamed. The actual failure happened earlier: nobody checked whether the data could support the use case.
How we work
We assess your data the way an AI workload would consume it. That means measuring completeness on the properties that matter per use case, checking whether activity history, emails, and notes actually live in the CRM or in inboxes and silos, and testing whether records connect: contacts to companies to deals to outcomes. Structure matters as much as volume; a million records that cannot be joined answer nothing.
We run this against your actual portal and connected systems, not a questionnaire. Where we find gaps, we distinguish the fixable, like missing associations or importable history, from the structural, like context that was never captured anywhere.
The output is a readiness report scored per AI use case category, with a concrete remediation path: what to clean, what to migrate, what to start capturing, and in what order. Since 2021 we have moved enough messy CRMs to know which gaps are a weekend of work and which are a project.
Deliverables
- Data completeness measurement on AI-relevant properties across core objects
- Context availability audit covering notes, emails, and activity history
- Record connectivity check across contacts, companies, deals, and outcomes
- Readiness score per AI use case category
- Remediation roadmap ordered by effort and unlocked value
What buyers ask before scoping.
Our data is a known mess. Is the assessment pointless until we clean it?
The opposite: assessing before cleaning is what makes cleaning efficient. Blanket data cleanup projects stall because everything looks equally important. This assessment tells you which specific gaps block the AI use cases you care about, so remediation gets a scope and a finish line instead of an open-ended mandate.
Does this cover data outside HubSpot?
Yes, to the extent your AI use cases depend on it. If the context an agent needs lives in a ticketing tool, a billing system, or a shared drive, the assessment covers whether it exists, whether it can be connected, and what that takes. Unreachable context is functionally missing context.
How is this different from the Data Quality & Integrity Assessment?
The data quality assessment measures general CRM hygiene: duplicates, completeness, and decay for everyday operational use. This one is purpose-built around AI consumption, which has different demands: context depth, record connectivity, and history. Clean-looking CRMs regularly fail the AI readiness bar, because tidy fields are not the same as rich context.
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