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