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Tonic Textual

Tonic Textual detects sensitive data in text and files, then redacts or replaces it for safer AI, analytics, and document workflows.

Tonic Textual

In inglese

Tonic Textual scans unstructured data such as text, audio, documents, images, and spreadsheets to identify sensitive entities. It can redact values, replace them with synthetic values, preserve document formats, and support custom entity types.

Teams use it for AI training, RAG pipelines, LLM prompts, compliance workflows, and lower-environment testing. It is available as a cloud service or self-hosted deployment, with Python SDK and REST API access. Cloud usage is billed by words processed; enterprise deployment and support use custom pricing.

Features

  • Detects sensitive entities with built-in and custom entity types
  • Redacts or replaces sensitive values with synthetic data
  • Processes text, audio, PDFs, Word files, images, spreadsheets, and more
  • Supports 50+ languages
  • Provides Python SDK and REST API access
  • Offers Guided Redaction for human review workflows
  • Deploys through Tonic Cloud or self-hosted Kubernetes and Docker
  • Supports RBAC, SSO, and dataset sharing

Use cases

  • Prepare privacy-safe data for AI model training and evaluation
  • Redact sensitive information before sending prompts to LLMs
  • Enrich RAG data with detected-entity metadata
  • Create realistic lower-environment data from sensitive documents
  • Review and refine redactions for regulated or public-record requests

Pros

    Cons

      Latest updates

      • v490 (v490)

        Fixed blank or incomplete PDF redaction and synthesis output for documents containing inline images.

      • v489 (v489)

        Fixed white gaps in V1-redacted PDFs.

      • v488 (v488)

        Fixed dataset-by-name API requests to return HTTP 404 for missing datasets and improved Python SDK dataset listing reliability during concurrent deletions.

      • v487 (v487)

        Bug fixes and other internal updates.

      • v485 (v485)

        Filter a dataset file list based on file scan status; add manual redactions to Word documents and ignore specific entity instances.

      Capabilities

      • Command line — “Add Textual to Claude Code, Gemini CLI, or OpenCode, and its proprietary NER models automatically detect sensitive entities in your text.” source
      • Self-hosted — “For the utmost in data security and control, deploy Textual on premises using Kubernetes or Docker, in the event that your data is too sensitive to live on the cloud.” source
      • API — “Use Textual pipelines directly in your existing workflows with Python SDK and REST API” source
      • Official SDKs — “Use Textual pipelines directly in your existing workflows with Python SDK and REST API” source
      • Search over your data — “Enrich vector stores with NER-powered entity metadata tags to improve RAG performance” source

      Get it

      Security

      • SOC 2 — “Tonic.ai uses an independent auditor to maintain a SOC 2 report, to ensure adherence to industry standards for security and privacy.” source

      Pricing

      Prices checked
      2026-09-25
      Read
      by web search

      Enterprise

      Price on request

      • Volume-based with custom quote
      • Enterprise-grade pipelines and redaction
      • Google SSO or Tonic Auth
      • Cloud or Self-Hosted deployments
      Official website