Docuflair Redact vs Philter

Docuflair Redact detects and irreversibly removes personal and sensitive information from documents. Its automated detection uses OCR, dictionaries, patterns, and rules across 259 personal data categories. Teams can organize documents into projects with rules, review, and approval, then create separate redacted exports for different recipients. The software accepts more than 100 input formats, including PDF, Word, Excel, PowerPoint, TIFF, JPG, PNG, MSG, and EML, and exports PDF 1.7 and PDF/A-1b, PDF/A-2b, and PDF/A-3b. Security features include an append-only SHA-256 hash-chain audit log, granular permissions, and optional AES-256 encryption at rest. All editions support self-hosting, and processing can run without an internet connection after setup. Basic costs €299 a month, Professional €599 a month, and Enterprise €999 a month, each with annual billing and a 12-month minimum term. A free browser sandbox is available, but it is a public test instance rather than a production deployment. The pricing FAQ says there is no traditional trial model.

Philter is open-source software for finding and redacting personally identifiable information and protected health information in text. For detected entities, users can choose to mask, encrypt, replace with synthetic values, remove, or pass them through. Policies, custom dictionaries, and identifier patterns such as medical record numbers and transaction IDs let teams configure what to detect and how to handle it. General Purpose, Healthcare, and COVID-19 NLP lenses address different types of text. Philter provides an HTTP REST API, a Java SDK, and an OpenAPI specification for generating clients in other languages. It can run in a private environment or through AWS, Google Cloud, and Microsoft Azure marketplaces. The maker says text stays in the customer’s environment, including firewalled or air-gapped deployments. Redaction requests are recorded in a tamper-evident, hash-chained audit log that can be queried and exported to CSV. Detection is probabilistic, so results should be checked against an organization’s own data before production use. The open-source toolkit has community support; commercial licenses include direct support.