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Key Features of Octacom's Redaction Services

Rules-Based Automation

Our AI and machine-learning engine uses templates, keyword analysis, and regular-expression matching to identify known patterns - such as SINs, facility layouts, and form-specific zones - with precision.

Name Entity Recognition

Apply natural language processing (NLP) to extract variable entities such as names, dates and organizations within unstructured text.

Human-in-the-loop Validation

Ensuring every redaction decision is verified, audited, and refined through expert human review (particularly valuable on low quality documents where automated redaction rules may fall short)

Secure and Audit-Ready

All data is processed in segregated on-premise environments, and each redaction decision is fully logged for audit or review.

Transparent and Accountable

Pre- and post-deployment bias testing, confidence scoring for every redaction action and continuous model monitoring plans with regular performance evaluations.

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STEP 1: Pre-Redaction Processing

Octacom's redaction workflow begins with Image Quality Enhancement to record preprocessing activities such as skew correction, noise reduction and contrast enhancement to ensure OCR/ICR accuracy. Octacom then extracts structured, unstructured and hand-written text, as well as performing text layout analysis to identify content areas and text blocks.

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STEP 2: Automated Redaction

Octacom's multi-layered approach to data recognition features entity classification, automated redaction and associated confidence scoring. The solution leverages predefined rules-based and named entity recognition models along with machine learning to optimize automated redaction. Overlapping rules-based and NLP-based redactions increases the model's confidence level.

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STEP 3: HITL

Octacom’s experienced team validates the accuracy of automated redactions to ensure they meet strict compliance thresholds. Corrections and approvals are fed back into the system, enabling continuous learning that enhances future redaction accuracy. Accepted and corrected redactions are logged as structured data, which is used to refine dictionaries, regular-expression patterns, entity detection rules, and OCR/ICR confidence scoring. Ongoing assessments aggregate these data points to reduce bias in automated redaction processes. Detailed logs document the rationale behind every automated redaction, supporting model improvement and full auditability.

Compliance as a Service

Octacom partners with government agencies, healthcare providers, research institutions, and other highly regulated sectors to deliver compliant data-redaction solutions. Our platform ensures full explainability and auditability, supported by detailed logs that document every action taken.

Full Service Redaction Solution

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Client Intake and Scoping to Collaborate on Solution Parameters

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Document Discovery to Establish Alignment on Redaction Requirements

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Implementation of Defensible Redaction Solution

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Ongoing AI-Assisted Machine Learning for Continuous System Improvement

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Complete Audit Trails to Satisfy Compliance Requirements

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Final Deliverables Provided in Accordance with SLA Requirements