data-fabric-fit-assessor

Assessing data fabric fit requires classifying your data infrastructure across Microsoft Graph, Google Workspace, Salesforce, or custom graphs to map it to the best-fitting AI agent fabric. This process identifies the required integration steps, migration costs, and compliance flags for safer enterprise governance.

Mapping a custom data graph to a native agent fabric requires evaluating vendor native access and cross-fabric integration constraints. A structured assessment identifies whether direct access is viable or if migration is needed to achieve faster ROI and safer data governance.

Yes, the data fabric mapping approach supports Microsoft Graph and Google Workspace. It classifies primary data infrastructure across these platforms, along with Salesforce 360 and custom data graphs, to determine the most appropriate AI agent fabric and integration approach for your environment.

The best way to evaluate data residency constraints for AI agent integration is through a phase-driven intake process. This identifies primary infrastructure and compliance constraints upfront, enabling a structured recommendation that includes specific compliance flags and migration cost estimates.

You should migrate data graphs instead of using vendor native access when cross-fabric integration requirements or data residency constraints prevent direct access. A structured assessment compares native fabric compatibility against migration costs to determine the safest and most cost-effective approach.

To generate a data fabric fit assessment, provide the client's data landscape to the assistant and request an evaluation. It will perform a phase-based intake, map the infrastructure to one of four fabrics, and output a structured recommendation with required integration steps.