How Discovarc Works
Discovarc sits above your existing review platform and changes the order in which documents are presented to attorneys. Connect to Relativity, DISCO, Everlaw, or Reveal — then let active learning prioritize the review queue until you hit 90% recall at 30–40% collection depth.
First-pass review costs between $45,000 and $80,000 per 50,000-document collection
Litigation support firms running first-pass document review on commercial matters commit 600 to 800 attorney hours per 50,000-document collection before they identify which documents are actually responsive. At market billable rates, that comes to $45,000–$80,000 per matter—before the review team has produced a single document.
The problem is not that attorneys review slowly. The average contract reviewer codes 60 to 80 documents per hour. The problem is sequence: linear review processes the collection in chronological or random order, which means responsive documents are distributed evenly across the queue rather than surfaced first.
Technology-assisted review has existed for more than a decade. The barrier is not access to the tools—Relativity, DISCO, and Everlaw all include active learning modules. The barrier is the repeatable workflow: how to seed the model, how many iteration batches to run, when to stop and validate recall, and how to document the protocol for production. Most litigation support firms run TAR on fewer than 20% of their eligible matters because the setup burden falls on project managers who already carry full workloads.
Discovarc addresses the process gap, not the technology gap. The active learning infrastructure is already in your review platform. We provide the structured workflow that makes it repeatable at every matter level, with QC sampling at 95% confidence and ±2% margin to generate a defensible disposition record.
Six Capabilities. One Workflow.
From collection ingestion through QC validation, Discovarc covers every step in the active learning review cycle — with all outputs formatted for the review platform your team already uses.
Predictive Coding and Active Learning
Discovarc’s active learning loop starts with a 500-document seed set coded by the reviewing attorney. Those judgments score every document in the collection for relevance probability, and each subsequent review batch draws from the highest-probability queue. After each coding session the model updates across the full collection. By the time 30–35% of the collection is reviewed, 85–90% of responsive documents have been identified — the rest receive a predictive disposition for QC review rather than linear first-pass.
Concept Cluster Mapping
Before the first document is reviewed for responsiveness, Discovarc generates a concept cluster map of the entire collection — groupings by topical content, not metadata. Project attorneys and e-discovery managers use the map to identify which topic clusters are likely to hold responsive material, spot custodian concentration patterns, and make early decisions about targeted review versus full first-pass. The map is generated within two hours of collection ingestion and refreshes as new batches are processed in.
Platform-Native Export Integration
Discovarc exports the prioritized review queue back into Relativity, DISCO, Everlaw, or Reveal as a batch list. Relevance probability scores attach as a document field, so review supervisors can sort and batch by probability tier inside the existing interface. There is no requirement to move documents into a new platform or reprocess the collection. Discovarc changes the order documents are presented; attorneys continue reviewing in the tool they already know.
Quality Control Sampling
When active learning reaches the target recall rate, Discovarc generates a statistically validated QC sample from the non-reviewed document population. Default settings run at 95% confidence and ±2% margin of error. Attorneys code the sample, and Discovarc reports the sample recall rate and error rate against the model’s predictions. The complete output is formatted for inclusion in a TAR protocol disclosure — defensibility documentation built into the standard workflow, not added afterward.
Custodian Contribution Analysis
After the active learning loop processes the first 20% of the collection, Discovarc reports relevance rates by custodian. Review managers identify which custodians are driving the responsive document population and assign dedicated resources ahead of the collection-wide schedule. Custodians with near-zero responsive rates can be moved to privilege-only review early, reducing total review cost without sacrificing overall recall. The analysis updates as the review progresses.
Review Analytics and Matter Reporting
The matter dashboard covers review velocity by reviewer, active learning recall rate at the current review depth, projected total hours to 90% recall, and cost-per-document against the matter’s budget. The dashboard updates after each coding batch submission, allowing managers to identify pace risks and add resources before deadline pressure builds. Matters are tracked individually. Reports export for client billing and internal matter profitability analysis.
Four Steps From Connection to 90% Recall
The full cycle — platform connection, collection analysis, seed coding, and iterative review — runs inside the platforms your team already uses. No retraining, no reprocessing.
Connect Your Review Platform
Connect Discovarc to your existing Relativity RelativityOne, DISCO, Everlaw, or Reveal instance. The integration works with the document collection as it currently sits in your workspace — no reprocessing, no platform migration, no changes to existing review tags or coding fields.
Define the Matter Parameters
Provide the key custodians, the relevant date range, and the initial keyword search set from the reviewing attorney. Discovarc builds a concept cluster map of the full collection within two hours — giving the project manager a view of the collection’s topic distribution before the first document is coded for substance.
Code the Seed Set
The reviewing attorney codes a 500-document seed set for relevance. Discovarc’s active learning loop starts immediately: it scores the full collection for relevance probability using those initial judgments and surfaces the highest-probability-relevant batch for the next review session. The model updates continuously from that point forward.
Review the Prioritized Queue
Attorneys work through the prioritized batch list in their existing platform. After each coding session, Discovarc updates the relevance scores across the remaining collection. At 30–40% review depth, 90% of responsive documents have been identified. The remaining collection receives a predictive coding disposition, and the QC sample is generated with the data needed for a TAR protocol disclosure.
Built for litigation support firms and e-discovery managed service providers
Discovarc is designed for litigation support firms and e-discovery managed service providers handling between 5 and 50 active review matters per month, with 20 to 200 attorney reviewer staff and $2M to $30M in annual review revenue.
The right fit is a firm that already uses Relativity, DISCO, Everlaw, or Reveal as its primary review platform, runs commercial litigation and regulatory matters with collections between 50,000 and 500,000 documents, and wants to deploy TAR consistently without building an internal workflow protocol from scratch.
Discovarc is not the right fit for solo litigation attorneys doing their own review, for firms already subscribed to Relativity Analytics or Everlaw AI managed review services, or for non-litigation document management use cases.
Works With the Platforms Your Firm Already Uses
Discovarc integrates with the seven most common review platforms in US litigation support — Relativity RelativityOne, Reveal AI Review, Everlaw, DISCO, Nuix Workstation, Logikcull, and iPro Eclipse SE. No platform lock-in; no forced migration.
