Find 90% of Responsive Documents in 35% of the Collection
Discovarc is an active learning layer for litigation-support firms that prioritizes your review queue so attorneys reach 90% recall while reviewing 30–40% of the total collection — working inside Relativity, DISCO, Everlaw, or Reveal.
Built for How Review Teams Actually Work
Every feature addresses a specific bottleneck in the first-pass review workflow — from collection analysis through QC validation and matter reporting.
Predictive Coding and Active Learning
Start with a 500-document seed set, and Discovarc scores every document in the collection for relevance probability after each attorney coding batch. By the time 30–35% of the collection is reviewed, the system has typically identified 85–90% of responsive documents — the remainder can be disposition-coded by exception.
Concept Cluster Mapping
Within two hours of collection ingestion, Discovarc generates a topic cluster map of the entire collection. Project managers use it to identify where responsive material is likely concentrated and to decide whether targeted review or full first-pass is warranted before committing attorney time.
Platform-Native Export Integration
The prioritized review queue exports directly into Relativity, DISCO, Everlaw, or Reveal as a batch list with relevance probability scores attached as a document field. Attorneys keep working in the platform they already know — no reprocessing, no migration, no new tool to train on.
Quality Control Sampling
After active learning reaches target recall, Discovarc generates a statistically validated QC sample from the non-reviewed population at 95% confidence and ±2% margin of error. The output is formatted for inclusion in a TAR protocol disclosure, so defensibility documentation is built into the workflow.
Custodian Contribution Analysis
After the active learning loop processes the first 20% of the collection, Discovarc reports relevance rates by custodian. Review managers can identify which custodians drive the most responsive documents and allocate dedicated resources ahead of schedule — and flag near-zero custodians for privilege-only review early.
Review Analytics and Matter Reporting
The matter dashboard tracks review velocity by reviewer, recall rate at current review depth, projected hours to 90% recall, and cost-per-document against budget. It updates after each coding batch and exports for client reporting or internal matter profitability analysis.
From Connection to 90% Recall in Four Steps
Discovarc connects to your existing review platform and guides the review team through seeding, iteration, and QC — with no new attorney training required.
Connect Your Review Platform
Connect Discovarc to your existing Relativity, DISCO, Everlaw, or Reveal instance. No document reprocessing. No platform migration. The integration works with the collection as it sits in your current workspace.
Upload Collection Parameters
Provide the key custodians, date range, and initial keyword search set. Discovarc generates a concept cluster map of the full collection within two hours — giving your project manager a view of where responsive material is concentrated before the first document is reviewed for substance.
Code the 500-Document Seed Set
The reviewing attorney codes a 500-document seed set for relevance. Discovarc’s active learning loop starts immediately, scoring the full collection and surfacing the highest-probability-relevant batch for the next review session.
Review the Prioritized Queue
The model updates after each coding batch, narrowing in on the responsive population. By the time 30–40% of the collection is reviewed, Discovarc has identified 90% of responsive documents. The remaining documents receive a predictive coding disposition, and the QC sample is generated for protocol disclosure.
First-Pass Review Is a Known Cost Problem
“A 50,000-document collection at market billable rates runs $45,000–$80,000 for first-pass review — before quality control, privilege review, or production.”
“Active learning technology has existed in review platforms for years. The barrier was never technical — it was a repeatable workflow for seeding it, validating recall, and producing a defensible TAR protocol.”
“When 15–25% of reviewed documents get reclassified on QC pass, that’s not a reviewer error rate — that’s the cost of reviewing in the wrong order. Prioritized review changes the sequence, not the reviewers.”