Keep text useful
Replace sensitive details with realistic alternatives and restore originals in your application.
Documentation
Compare ShinrAI
Useful AI context. Control of your data. A deployment that fits your business.
Choose where it runs
Use the managed API in Germany or run ShinrAI on your infrastructure, including disconnected deployments.
Start with a clear budget
Published plans and non-expiring packs, with optional help from implementation partners.
Compare what matters to your workflow
Published capabilities, reviewed on . Availability depends on the product, version and contract.
Available · Preview · Custom implementation · Not documented in the cited source
| Your decision | ShinrAI | Azure AI Language PII | Google Sensitive Data Protection | Amazon Comprehend | Presidio | Limina | Tonic Textual |
|---|---|---|---|---|---|---|---|
| Useful replacement text | Available Realistic, consistent replacements; masking and labels also available. Source | Preview Synthetic replacements alongside masking and labels. Source | Available Cryptographic tokens; format-preserving encryption available. Source | Available PII detection and character redaction. Source | Available Replace, mask, hash and encrypt; custom operators supported. Source | Available Synthetic PII, pseudonymisation and redaction. Source | Available Synthetic replacement and reversible tokenisation. Source |
| Restoration and control | Available Your application retains the mapping and restores original values. Source | Not documented A reversible customer-held mapping is not documented in the cited PII policy. Source | Available Re-identification with customer-managed cryptographic keys. Source | Not documented A restoration mapping is not documented for PII redaction. Source | Custom implementation Decrypt supported; application manages keys and custom mappings. Source | Available Re-identification using a supplied entity mapping. Source | Available Reversible tokens and configurable replacement consistency. Source |
| Deployment choices | Available Managed on STACKIT in Germany, or your infrastructure with signed offline activation. Source | Available Azure service and PII containers; container billing conditions apply. Source | Available Managed Google Cloud service. Source | Available Managed AWS service. Source | Custom implementation Self-hosted library and containers; you operate the service. Source | Available Private VPC or on-premises container. Source | Available Cloud or self-hosted deployment, depending on plan. Source |
| Languages and quality | Available 15 model locales; Hebrew beta. Versioned evaluations are public. Source | Available Language and entity support vary by model and API version. Source | Available Detector-specific language support; evaluate your text and infoTypes. Source | Available PII detection supports English and Spanish. Source | Custom implementation Coverage depends on installed recognisers and NLP models. Source | Available Vendor advertises 52 languages; evaluate your use case. Source | Available Evaluate the language and entity coverage of your selected configuration. Source |
| Integration and documents | Available Native API, documented compatibility adapters and application guides; optional partner help. Source | Available Azure APIs, SDKs and asynchronous text jobs. Source | Available Cloud APIs and configurable de-identification workflows. Source | Available Real-time detection and asynchronous document redaction. Source | Custom implementation Developer toolkit; document pipelines and operations need assembly. Source | Available API and container integration for structured and unstructured data. Source | Available Document datasets and configurable synthesis workflows. Source |
| Pricing and help | Available Published monthly plans, non-expiring packs and optional onboarding. Source | Available Published service pricing; Azure support purchased separately. Source | Available Usage pricing for inspection and transformation. Source | Available Usage pricing by API operation and text volume. Source | Custom implementation Open-source toolkit; budget for hosting, implementation and support. Source | Available Contact the vendor for commercial terms. Source | Available Published plans and enterprise quotes; compare included volume. Source |
Language counts and vendor-reported scores use different definitions. More languages or categories alone do not establish better quality on your documents.
Measured quality, with the context attached
Business and clinical letters; 200 texts per locale. Strict entity spans. Hebrew beta is included. Model-only evaluation by Innovius, not a service-wide accuracy guarantee.
Customer chat: a published head-to-head
Same 27-message chat-attack set, using overlap matching. Small sample; scores cannot be compared directly with the strict-span locale evaluation. Azure preview leads this set. Versions and run details are on the benchmark board.
ShinrAI 1.384,9 84,9
Azure GA81,1 81,1
Azure preview88,8 88,8
Presidio75,6 75,6
GLiNER274,3 74,3
Overlap-span F1 · 27 messages · Innovius evaluation
Open the full benchmark board →How to read the evidence
F1 balances precision and recall. Exact-span and overlap scoring answer different questions. Results depend on the dataset, entity mapping, model and configuration.
ShinrAI has strong multilingual results, while other systems lead some long-document and synthetic-paperwork tests. Use the full board and a representative pilot to choose your configuration.
Switching from Azure PII?
Keep the supported client contract and evaluate ShinrAI predictions on your own documents.