A document enters the pipeline.
W-2 · loan file · check image · clinical note
REGULATED UNSTRUCTURED DATA FOR AI ON DATABRICKS
Your unstructured data is the goldmine your AI teams can’t safely use.
Loan files, claims, clinical notes, identity documents, images and call recordings contain the context AI teams need—and regulated information they cannot expose. GritRedact de-identifies that content at the source, before it enters Databricks.
SENSITIVE SOURCE
↓ POLICY
CONTROLLED OUTPUT
THE DIVISION OF RESPONSIBILITY
Databricks governs access.
GritRedact removes exposure.WHY GRITWORKS IS NEEDED
Masking extracted text does not de-identify the source artifact.
The text stream may be protected while the original page or image remains unchanged—stored, linked, retrieved, rendered or exported with its identifiers intact.
The extracted text can be masked.
Useful for text retrieval, but masking does not automatically alter the page or image linked to that text.
The original artifact can persist.
It may remain in storage, stay linked to retrieved chunks, appear in answers, or move into exports and training sets.
COMMON QUESTION
“Isn’t that what Unity Catalog is for?”
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. Governance controls who can use the data; source-side de-identification reduces what can be exposed when the artifact is indexed, retrieved, rendered, shared or exported.
IN AND BEYOND DATABRICKS
One insertion point makes regulated artifacts reusable across the AI stack.
GritRedact runs inside your environment and creates a de-identified derivative before ingestion. Databricks and the rest of your stack receive the safe output—not the exposed source.
Enterprise content
File shares · ECM · SharePoint
De-identify at source
On-prem · zero egress
Move the safe output
Lakeflow Connect or existing pipelines
Land the clean asset
Unity Catalog Volumes · Delta metadata
Search or vector store
Databricks-native or external
RAG · agents · training
Your chosen AI stack
Raw data remains inside your environmentDe-identified derivative from GritRedact onward →
WHERE IT FITS BEST
Use the same de-identified artifact across internal and external workflows.
The protection travels with the data because the artifact itself was de-identified before it moved.
Copilots your staff use
Ground copilots and RAG on de-identified artifacts rather than raw files or raw embeddings.
- Loan modification and servicing assistants
- Claims-adjuster copilots
- Underwriting and intake review
- Case and document research agents
Anything that leaves the building
Share, export, train, evaluate or disclose without propagating the original identifiers.
- Vendor and partner data sharing
- Model training and fine-tuning sets
- Customer-facing AI products
- Regulatory and legal disclosure
COVERAGE
One de-identification layer across every modality sensitive data hides in.
De-identify the artifact itself before the downstream architecture is chosen.
Typed and handwritten
W-2s, loan files, claim packets, underwriting documents, clinical notes and legal filings.
The page that gets reused
Check images, scans and photographed documents—de-identified before indexing or retrieval.
The recording itself
Contact-center, servicing and dispute recordings with sensitive spoken content.
START WITH DE-IDENTIFICATION
Make real data safe. Create synthetic data when real data is unavailable.
The products solve different constraints and can be used independently or as one controlled development workflow.
Make real artifacts AI-ready.
De-identify documents, images and audio so they can enter Databricks, feed retrieval and support agents without carrying original identifiers downstream.
Explore GritRedact →Build before production access.
Generate structurally realistic synthetic documents, images and audio for development, testing and evaluation when live regulated data is unavailable.
Explore GritRender →ENTERPRISE EVIDENCE, WITH STAGE DISCLOSED
Different products. Different engagements. No inflated proof.
U.S. housing-finance organization
De-identified sensitive mortgage artifacts inside the controlled environment.
U.S. health insurance and services organization
Prepared handwritten clinical content for controlled retrieval and agent evaluation.
U.S. diagnostic services provider
Generated synthetic requisitions with known truth—without production PHI.
PRIVATE DE-IDENTIFICATION TEST
You can’t send the data out. So we come in.
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.
Run a private de-identification test ↗