Loan files, claims, clinical notes, identity documents, images, and call recordings contain the context your AI teams need — and the regulated information they cannot expose.
GritRedact de-identifies regulated content within each artifact at the source — before it enters Databricks, is indexed or embedded, or is used by RAG and agents.
Databricks governs access. GritRedact removes exposure.
Many pipelines mask PII in extracted text before embedding. The source page can still remain unchanged — stored, linked, retrieved, rendered, or exported with the identifiers intact.
The text may be masked. The source artifact can still remain exposed.
It does not automatically change the page or image linked to that text.
There may be no extracted-text layer where masking can intervene. De-identifying the source image before indexing prevents the raw artifact from entering the workflow unchanged.
GritRedact de-identifies the source artifact before either path begins.
Unity Catalog governs access, lineage, and auditability across the lakehouse. GritRedact performs a different job: it de-identifies sensitive content inside the artifact before ingestion. The two layers work together — governance controls who can use the data, while source-side de-identification reduces what can be exposed when the artifact is indexed, retrieved, rendered, shared, or exported.
GritRedact runs inside your environment and creates a de-identified derivative before the artifact is ingested. Lakeflow Connect or your existing data pipelines can then move the safe output into Databricks, where it can flow into the search, vector, agent, or model stack you choose.
Use Lakeflow Connect or your existing pipelines for ingestion, then Databricks AI Search, an external vector database, or your existing retrieval layer downstream. GritRedact makes the artifact safe before those architectural choices are made.
Detect and de-identify regulated content in text, handwriting, images, faces, signatures, and spoken words before the artifact moves.
Land de-identified artifacts in Unity Catalog Volumes, with associated metadata and audit records available in Delta.
Index, retrieve, train, evaluate, or share through Databricks-native services or external AI systems without propagating the original identifiers.
Ground copilots and RAG on de-identified artifacts rather than raw files or raw embeddings.
Share, export, train, evaluate, or disclose without propagating the original identifiers.
The protection travels with the data because the artifact itself was de-identified before it moved.
W-2s, loan files, claim packets, underwriting docs, clinical notes, legal filings — fields and free-hand alike.
Check images, scans, and photographed documents. We de-identify the pixels before the image is indexed, retrieved, or reused.
Contact-center, servicing, and dispute recordings. We detect and de-identify sensitive spoken content before downstream use.
De-identify documents, images, and audio so they can enter Databricks, feed retrieval, and support agents without carrying the original identifiers downstream.
Need usable data before live regulated data is available? Generate structurally realistic synthetic documents, images, and audio for development, testing, and evaluation.
When the artifact is a regulated record, the bar is high.
Choose one representative artifact. We run GritRedact inside your environment and show the de-identified output, metadata, and audit trail. Raw data stays under your control.