Epiq AI integration

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TAR Integration with Epiq Discovery

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1. Collection Types & Logic

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Integration Strategy

• TAR Only: Use tar_only.
• TAR + Chat: Use discovery.
• Any other model type: Upgrade the collection to review.

Note: Do not create separate collections to categorize documents for the same project. If a user needs to Chat over a specific subset of documents within a collection, they should use Datasets as the filtering mechanism rather than collection boundaries.

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2. Predictive Coding Model

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Model Specification: predictive_coding

• Purpose: TAR model for document ranking and continuous active learning.
• Constraints: Unlike standard LLM models, predictive_coding models do not have access to instructions or examples parameters. They are used strictly for the TAR scoring engine.

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3. Billing Model

1. Basic Storage: A flat rate for all documents stored within any collection type.
2. Chat Enablement: Billed if the collection type allows Chat (e.g., review , or discovery). Chat has access to all documents except documents in the tar_only collection.
3. Workflow & Models: Billed on a per-document basis. This applies to any document processed through a workflow or a non-TAR model (e.g., text classification).

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4. API Example

POST /api/v1/cases/{case_id}/sources
​{​
  "name": "Epiq Discovery TAR",​
  "description": "",​
  "source_type": "tar_only"
​}

POST /api/v1/cases/{case_id}/models
​{​
  "name": "TAR model",​
  "description": "",​
  "model_type": "predictive_coding",​
  "positive_labels": [​
    "responsive"​
  ],​
  "negative_labels": [​
    "not_responsive"​
  ],
​}

GET /api/v1/cases/{case_id}/models?model_type=predictive_coding

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5. SSO Redirect

https://{aida_domain}/api/v1/auth/start?account_id={account_id}&returnTo=/projects/{case_id}/models/{model_id}