The data your AI and QA teams need is the data your compliance team can’t release.
Every enterprise building AI Models, Agentic AI systems, or modern document-processing applications faces the same problem: the unstructured data those systems need — PDFs, scanned forms, images — is exactly the data loaded with PII. Manual redaction is slow and inconsistent. Rules-based tools miss non-standard fields. The result: AI and QA teams are starved of the realistic, diverse data they need.
UDA-Redact breaks this bottleneck with a three-stage workflow — deep learning auto-detection, human-in-the-loop operator review, and pixel-level permanent redaction — all inside a single Docker container with zero data egress.
Three stages. One Docker container. Zero data egress.
Detect
The deep learning model scans the document and identifies PII regions with per-field confidence scores. No rules. No regex. The model understands document structure semantically.
Review & Redact
The operator confirms, rejects, or adds manual redactions for anything the model missed. Every decision is logged. Nothing is applied without explicit approval.
Export & Train
A pixel-level redacted PDF is saved — clean, structurally intact, safe for AI Model and Agentic AI training. SHA-256 audit log and governance CSV come standard.
Built for QA leaders, AI teams, and the CISOs who sign off.
ML Auto-Detection & Continuous Learning
Deep learning trained on enterprise document formats identifies SSNs, EINs, names, addresses, account numbers, dates — and improves with every operator correction.
Human-in-the-Loop Review
Every detection is reviewed and approved by an operator before permanent change is applied. Operators can add manual redactions for anything the model missed.
Pixel-Level Permanent Redaction
Sensitive content is irreversibly removed from the underlying pixels — not overlaid with masks that can be stripped. What’s removed stays removed.
Immutable Audit Log & Governance CSV
SHA-256 chain-of-custody hashing on every redaction. Export a structured CSV with entity type, source, confidence score, operator, timestamp, and file hashes.
Batch Processing
Drop multiple PDFs or images at once for concurrent processing. Download redacted outputs as a single ZIP. Built for high-volume enterprise workflows.
100% Offline. Zero Data Egress.
Runs entirely inside a single Docker container. No external APIs, no LLMs, no network egress. Deploy on-premise, private cloud, or fully air-gapped.
Built for industries where a single PII leak is a regulatory event.
Financial Services
Automates PII removal from W-2s, pay stubs, loan applications, mortgage packets, and trade confirmations — enabling safe prep for AI training and audit submission under GDPR, CCPA, GLBA, and SOX.
Mortgage Processing Automation
A regional bank automated PII redaction across tens of thousands of mortgage applications in hours rather than weeks — producing a clean, auditable corpus for training a document classification model.
Healthcare
Removes PHI and PII from patient intake forms, insurance claims, EOBs, and lab reports with pixel-level permanence — enabling clinical informatics, EHR vendors, and healthcare AI developers to work with realistic samples without HIPAA exposure.
AI Training for Claims Adjudication
A large healthcare payer processed thousands of historical claims through UDA-Redact, removing all PHI. The corpus enabled full AI Model training coverage — and became the foundation for their Agentic AI claims-routing pipeline.
Banking & Insurance
Handles mixed-format documents — policy applications, claims packets, underwriting files, identity documents — identifying PII across structured fields and free-form text regions with high accuracy.
Insurance Claims Pipeline
A national carrier used UDA-Redact to automate PII removal from thousands of historical claims, producing a diverse test corpus for their OCR and ML classification pipeline — with a full compliance audit record.
The problems QA leaders, AI teams, and compliance officers actually hit.
| The Challenge | How UDA-Redact Solves It |
|---|---|
| Production unstructured data contains PII that blocks TDM, AI Model training, Agentic AI development, and cross-team sharing. | ✓Permanently removes PII at the pixel level — producing clean, structurally intact data safe for every downstream pipeline. |
| Rules-based tools miss PII in non-standard fields and produce no auditable record. | ✓Deep learning detects PII semantically, not just by pattern — and logs every action with SHA-256 chain-of-custody proof. |
| Compliance teams cannot prove what was redacted, when, and by whom. | ✓Governance CSV export captures entity type, source, confidence score, operator, timestamp, and file hashes per region. |
| Cloud-based redaction services create unacceptable data sovereignty risk. | ✓100% offline, Docker-native. No external APIs, no LLMs, no network egress. Runs entirely inside your infrastructure. |
What changes when unstructured data is no longer a blocker.
Compliance & Privacy
Replace PII-dense production documents with permanently redacted, auditable outputs.
Automation at Scale
Documents redacted in seconds. Thousands of files via batch mode.
Audit-Ready
Cryptographic proof for every redaction — satisfying GDPR, HIPAA, CCPA, SOX.
AI, Agentic & TDM Ready
Feed redacted output into AI pipelines — or into UDA for unlimited synthetic variants.
Redact, then Generate.
Deploy UDA-Redact as a standalone platform — or as stage one of GenRocket’s complete document pipeline. Pair with the Unstructured Data Accelerator to turn each redacted document into hundreds of synthetic variants for TDM and AI training.
A single docker run deploys the full platform. No GPU required, no external dependencies, no internet access needed.
See UDA-Redact on your documents.
Bring a sample workload. We’ll show you detection accuracy, audit output, and how UDA-Redact fits into your TDM, AI Model training, or Agentic AI pipeline.