Data Readiness
Before connecting AI to your enterprise data, discover sensitive information, map PII exposure, and define secure access controls.
What changes for your team
Before a model touches SharePoint, S3, databases, or file shares, teams need to know where sensitive data sits, who can access it, which repositories are over-permissioned, and what should be masked or excluded.
We deploy Pelestra, our data readiness engine, directly into your infrastructure via Docker. It connects to PostgreSQL, S3, Azure Blob, and file shares via read-only APIs so source data can be scanned in place inside the approved environment.
Basic regex scanners create noisy findings. We use a 3-tier approach: pattern matching, NLP-based named entity recognition, and exact data match. It reads column headers and surrounding context to improve classification of SSNs, financial data, and sensitive IP.
The output is a practical readiness map: sensitive repositories, likely exposure paths, lineage notes, access-control recommendations, masking needs, and which workflows are safe to test first.
How it works
Agree on the work, information, business rules, user roles, desired output, and what the AI is allowed to do.
Bring in only the approved documents, records, business data, control libraries, or playbooks needed for the task.
Give your team findings, matrices, notes, data-backed answers, or work queues with sources and clear reasons for exceptions.
Track edits, accuracy, time saved, exception quality, and adoption before applying the system to more work.
FAQ
It is a systematic evaluation of your enterprise data to identify sensitive information, assess current access controls, and determine what data is safe to expose to AI models.
No. We deploy scanning tools within your environment for in-place scanning so source data can remain inside your infrastructure.
Technology partnership
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