AI Detection

Detection combines several recognition strategies and reconciles their results into a single set of entities, each with a label, confidence, and location.

Recognition Strategies

Patterns

Deterministic matching for structured values

NER

Named-entity recognition for names, places, and organizations

LLM

Model-driven classification for context-dependent data

Vision

Recognition over rendered pages and embedded images

Recognition is deployment-wide: the built-in pattern set plus whichever recognizers the engine has registered. Pipelines choose what to target and how to treat it, not which recognizers exist.

The Catalog

Two read-only endpoints describe what a deployment offers:

GET /catalog/labels/
GET /catalog/recognizers/

labels returns the built-in taxonomy — the categories of sensitive data (PII, PHI, PCI, and so on) that policies can target. recognizers returns the engine’s registered lineup, grouped into ner and llm.

const labels = await client.catalog.listLabels();
const recognizers = await client.catalog.listRecognizers();

console.log(recognizers.ner.length, recognizers.llm.length);

Query the catalog rather than hardcoding label names. It reflects what the deployment you are talking to actually supports, which differs between cloud and self-hosted installs.

LLM Providers

Model-driven recognition runs against a connected provider — openai, anthropic, or ollama for self-hosted inference. Configure one as a workspace connection.

With ollama, inference stays inside your infrastructure. Combined with an on-premise deployment, no document content leaves your network.

Reconciliation

Multiple recognizers frequently find the same thing, or disagree. Rather than taking the first match, the engine reconciles them — and records how. Each entity’s audit chain shows the steps applied:

Event	Meaning
`pattern`	Matched a deterministic pattern
`model`	Recognized by a model
`deduplication`	Merged with an overlapping detection
`conflict`	Competing detections were reconciled
`contested`	Recognizers disagreed
`calibration`	Confidence was calibrated
`refinement`	Boundaries or label were refined

Events carry their parents, so a final entity can be traced back through every merge that produced it. See Audit Trails.

Entities

Each recognized entity records:

Field	Description
`label`	The taxonomy label it was classified as
`confidence`	Calibrated confidence
`location`	Position, in coordinates native to the modality
`language`	Detected language, where applicable
`coref`	Coreference link to other mentions of the same subject

Coreference links matter for redaction consistency: when the same person is mentioned several ways, linked mentions can be treated as one subject rather than as unrelated findings.

Scope

A run’s scope narrows recognition to the languages and jurisdictions that apply, and controls OCR:

{
  "scope": {
    "languages": ["en"],
    "countries": ["US"],
    "ocrMode": { "kind": "force", "dpi": 300 }
  }
}

Pipelines carry a default scope; individual runs may override it.

Review

Detection never modifies a document. A run stops at analyzed with its findings recorded, and redaction is a separate, explicit step — so confidence scores inform a human decision rather than replacing one.

Overview

Features

Cloud

On-Premise

AI Detection

Recognition Strategies

Patterns

NER

LLM

Vision

The Catalog

LLM Providers

Reconciliation

Entities

Scope

Review

Next Steps

Redaction Workflow

Integrations

​Recognition Strategies

Patterns

NER

LLM

Vision

​The Catalog

​LLM Providers

​Reconciliation

​Entities

​Scope

​Review

​Next Steps

Redaction Workflow

Integrations

Recognition Strategies

The Catalog

LLM Providers

Reconciliation

Entities

Scope

Review

Next Steps