Private AI

What is Private AI?

Private AI provides AI capabilities for OCR and document parsing. In this context, the most relevant angles are document redaction, text anonymization, because those features determine how easily the provider can fit into a real application or automation workflow. Private AI is best evaluated through the specific workflow it supports.

For Private AI, the evaluation should start with representative PDFs, scans, receipts, invoices, IDs and operational documents. The goal is to understand whether its strengths in document redaction, text anonymization translate into outputs that are usable for the product, not only technically correct in a demo environment.

Private AI at a glance

Private AI main AI capabilities

• Text Anonymization: to remove or mask sensitive information in text, with Private AI evaluated on realistic document ai inputs.
• Anonymization APIs: to protect sensitive data in documents or text workflows, with Private AI evaluated on realistic document ai inputs.
• Named Entity Recognition APIs: to extract people, organizations, locations or other entities, with Private AI evaluated on realistic document ai inputs.
• OCR APIs: to extract text from PDFs, images or scanned documents, with Private AI evaluated on realistic document ai inputs.
• Document Data Extraction: to transform business documents into structured fields, with Private AI evaluated on realistic document ai inputs.
• Text Moderation APIs: to detect unsafe, sensitive or policy-violating content, with Private AI evaluated on realistic document ai inputs.

When should you choose Private AI?

Private AI is useful when sensitive information must be detected, anonymized or redacted before text or documents move into another workflow. It can fit privacy reviews, data minimization, compliance processes, analytics pipelines and AI preprocessing steps where personal information should not be exposed unnecessarily.

It is less relevant if the goal is to generate content, transcribe audio or edit images. A proper evaluation should include the types of personal data your organization handles, mixed document formats and tricky context cases, then verify whether the system protects sensitive fields without damaging the rest of the content.

Private AI pros and cons

Private AI models, features and capabilities on Eden AI

The useful way to assess Private AI is to start from the feature set, then test whether document redaction, text anonymization matches the expected output format, latency target and production constraints. Private AI should be evaluated through OCR and document parsing, not as a generic AI provider.

Relevant selected features for Private AI

The relevant features for Private AI are the ones that make document redaction, text anonymization easier to run inside a real workflow. Testing should include clean examples, noisy inputs and edge cases, because feature coverage is only useful when the provider returns outputs that remain reliable after integration.

• Text Anonymization to connect text anonymization tasks to the workflow without managing a separate integration.
• Anonymization APIs when anonymization apis is part of the application logic, automation layer or user-facing feature.
• Named Entity Recognition APIs for testing Private AI on named entity recognition apis use cases before deciding how to route production traffic.
• OCR APIs for workflows where Private AI needs to handle ocr apis inside a broader product experience.
• Document Data Extraction to connect document data extraction tasks to the workflow without managing a separate integration.
• Text Moderation APIs when text moderation apis is part of the application logic, automation layer or user-facing feature.

Available Private AI models

Available Private AI models and configurations should be checked before release, especially when model choice affects field-level accuracy, layout handling and review effort. For document redaction, text anonymization, teams should confirm the selected model, input limits and output behavior instead of assuming that every configuration performs the same way.

Supported Private AI capabilities

Supported AI categories

• Document Processing.
• Text Processing.

Private AI API output: what data can be extracted or generated?

Important note on Private AI accuracy and reliability

Private AI should be tested with the same PDFs, scans, receipts, invoices, IDs and operational documents that the final application will process. Accuracy and reliability can shift with language, file quality, prompt length, media format, domain vocabulary and expected output structure, so the safest production decision is based on measured results rather than the provider name alone.

What can you build with Private AI?

Use case 1 — Text quality and compliance workflows

This use case is relevant when Private AI can reduce repetitive work around OCR and document parsing. The test should include typical inputs, edge cases and the volume expected once the workflow is live.

Use case 2 — Content operations

For content workflows, Private AI should be tested on the exact formats the team plans to generate or transform. The goal is to see whether the provider can produce usable drafts, structured outputs or creative assets with limited rewriting and predictable cost. The main evaluation lens should remain field accuracy, document coverage, layout robustness, confidence scores and review effort.

Use case 3 — Governance workflows

Use Private AI for this scenario when document redaction, text anonymization directly supports the business process. Testing should show whether the returned structured fields, extracted entities, normalized values and validation-ready data are consistent enough to feed the next step without heavy manual cleanup.

Private AI use cases by industry

Why use Private AI through Eden AI?

The main reason to use Private AI through a unified layer is control: the team can test its strengths, monitor real usage and still route traffic elsewhere if another provider performs better on a specific input type.

Key benefits of using Private AI on Eden AI

• Access Private AI from the same environment as other AI providers.
• Compare providers before choosing the best default for a workflow.
• Reduce vendor lock-in by keeping routing options open.
• Centralize monitoring, usage and billing across providers.
• Improve production reliability with fallback and routing strategies when relevant.

One API for Private AI and 50+ AI providers

Private AI can sit inside a broader AI architecture while remaining configurable. This is useful when document redaction, text anonymization must be tested alongside other capabilities, monitored over time and routed differently depending on input type, expected quality or cost sensitivity.

Compare Private AI with other AI models

Comparing Private AI with alternatives only makes sense when the same task, same data and same success metric are used. For document redaction, text anonymization, the comparison should measure field accuracy, layout robustness, confidence scores and human review effort, then look at how much post-processing is required before the output can be trusted.

Add fallback and routing for production reliability

Fallback matters when Private AI fails, slows down or returns weaker results on inputs outside document redaction, text anonymization. A production setup can keep Private AI for the scenarios where it performs best, while sending other requests to a provider that is more suitable for the specific constraint.

Monitor usage, billing and costs in one place

Cost management for Private AI should be based on how PDFs, scans and structured business documents behave in production. Long inputs, retries, failed requests, quality checks and manual correction can all change the true cost of using document redaction, text anonymization, even when the listed price looks predictable.

How to integrate Private AI with Eden AI

Integration starts by matching Private AI with the capability that fits the workflow, then testing it on representative PDFs, scans and structured business documents. Developers should inspect the response schema, validate error handling and confirm how document redaction, text anonymization behaves before the provider is connected to customer-facing or business-critical logic.

Integration overview

• Create or log in to an account.
• Generate an API key from the dashboard.
• Choose the feature that matches the workflow you want to build with Private AI.
• Select Private AI as the provider when it is available for that feature.
• Send requests through the current current API route documented for that feature.
• Parse the normalized response when available.
• Monitor usage, costs and provider performance from the dashboard.

Authentication

Authentication for Private AI should be handled from a secure backend environment. API keys should not be placed in frontend code, public repositories or shared documents, particularly when the workflow processes PDFs, scans, receipts, invoices, IDs and operational documents or other sensitive business data.

Provider selection

Private AI should be selected because it performs well for the target workflow, not because it belongs to a broad category. The team should confirm that document redaction, text anonymization match the expected use case and keep the provider choice configurable for future benchmarking.

Response format

The response format from Private AI must be validated before it is consumed by downstream systems. Developers should check required fields, optional metadata, error cases and confidence indicators where available, so that document redaction, text anonymization can be used reliably in automated flows.

Production integration best practices

• Test with representative real data before launch.
• Validate required fields and confidence scores when available.
• Implement error handling, retries and timeouts.
• Avoid hardcoding provider-specific assumptions.
• Monitor latency, cost and accuracy over time.
• Compare providers periodically as model quality and pricing evolve.

Private AI pricing and cost management on Eden AI

How Private AI pricing works

Private AI pricing should be reviewed together with the selected feature, expected usage volume and complexity of the input data. For document redaction, text anonymization, the final cost often depends on retries, processing time, output validation and the level of human correction needed after the provider returns a result.

How to monitor Private AI costs

Cost monitoring for Private AI should include request volume, successful responses, retries, latency and the amount of manual review needed after output generation. For document redaction, text anonymization, the cheapest unit price is not always the lowest real cost if results require repeated calls or heavy correction.

How to optimize costs with provider comparison and routing

Cost optimization starts by separating easy, complex and high-value requests. Private AI may be the strongest option for document redaction, text anonymization, while a different provider can be reserved for simpler traffic, fallback scenarios or tasks where quality requirements are lower.

Best Private AI alternatives and comparisons on Eden AI

Private AI vs ReadyRedact

The best way to compare Private AI and ReadyRedact is to map each one to a concrete job. Private AI behaves like a privacy-focused provider for detecting, redacting and anonymizing personal or sensitive information, whereas ReadyRedact behaves like a redaction-focused provider for removing sensitive information from documents. If the current bottleneck is that the application must protect PII across text or documents before data is stored, analyzed or sent to another system, Private AI should be tested first. If the bottleneck is that teams need document redaction workflows where PII, confidential fields or regulated content must be hidden before sharing, ReadyRedact may provide a cleaner starting point. Measure PII recall, false positives, language coverage, format preservation and compliance workflow fit, plus missed sensitive data on real inputs.

Private AI vs Base64.ai

The real difference between Private AI and Base64.ai appears when the same use case is pushed through both providers. Private AI is best understood as a privacy-focused provider for detecting, redacting and anonymizing personal or sensitive information. Base64.ai is better viewed as a document AI provider for OCR, ID documents, forms, redaction and structured extraction use cases. Choose Private AI when the application must protect PII across text or documents before data is stored, analyzed or sent to another system; move Base64.ai higher in the shortlist when teams need broad document intake across IDs, financial files, forms and mixed business documents. The benchmark should focus on PII recall, false positives, language coverage, format preservation and compliance workflow fit, plus document coverage.

Similar providers available on Eden AI

• ReadyRedact
• Base64.ai
• Amazon Web Services

Frequently asked questions about Private AI on Eden AI