E-Discovery Triage Agent
Ingests collected document sets, applies technology-assisted review scoring, and routes documents into responsive, privileged, and non-relevant queues for attorney review.
In large litigation matters, outside counsel and internal legal teams must review hundreds of thousands of documents to determine responsiveness and privilege, and manual first-pass review is slow, expensive, and inconsistent between reviewers
Review costs frequently balloon into the hundreds of thousands of dollars, and inconsistent tagging across reviewers creates quality-control headaches during production
This agent applies large language model classification informed by seed sets and review protocols to pre-sort documents by likely responsiveness, privilege risk, and topical relevance, dramatically cutting the volume that requires first-level human review
It continuously learns from reviewer overrides to improve its scoring accuracy across the life of the review, and it flags borderline documents for senior attorney escalation rather than making silent determinations
The agent is triggered when a new document collection is loaded into the review platform, and it begins by extracting text and metadata from each file, including embedded attachments and email threads. It uses an LLM fine-tuned on the matter's issue tags and a reviewer-approved seed set to score each document for responsiveness, privilege likelihood, and confidentiality, then applies clustering to group near-duplicate and thread-related documents for batch decisions. Scored documents are routed into review queues by risk tier, with privilege-flagged items automatically routed to senior counsel, and every reviewer coding decision is fed back into the model to recalibrate scoring thresholds in near real time.
Ingest and Process Collection
- Extract text, metadata, and attachments from collected files
- Deduplicate and thread email conversations
- Normalize file formats for OCR where needed
- Load documents into the review platform index
Score and Classify Documents
- Apply LLM classification against issue tags and seed set
- Score responsiveness, privilege likelihood, and confidentiality
- Cluster near-duplicates and email threads for batch coding
- Flag borderline scores for manual verification
Route to Review Queues
- Assign documents to responsive, non-relevant, or privileged queues
- Route privilege-flagged items to senior attorney review
- Prioritize high-relevance documents for first-pass reviewers
- Generate review batches sized for reviewer throughput
Learn and Report
- Capture reviewer overrides and coding decisions
- Recalibrate scoring model thresholds continuously
- Generate review progress and cost-savings reports
- Produce a defensibility memo documenting the TAR protocol used