The production stack for litigation teams running AmLaw-grade discovery — where data volumes are measured in terabytes, review populations number in the tens of thousands of documents, and privilege log production timelines are contractually constrained. This is not a stack for occasional discovery work; it is the operational infrastructure for firms and corporate legal departments whose docket makes e-discovery a recurring, high-stakes workflow.
How the pieces fit
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Relativity is the review platform. The core environment where document batching, linear and AI-assisted review, coding decisions, and quality control all live. At AmLaw scale, Relativity is the environment where contract review attorneys, associates, and senior reviewers work concurrently on the same matter. Its role in this stack: the authoritative workspace for all document-level coding decisions. When Everlaw or Logikcull exports a production-ready set, it often lands back in Relativity for final QC before going out the door. Relativity’s aiR for Review layer (included in RelativityOne as of early 2026) handles initial relevance and privilege pass-through before human reviewers touch a document, reducing first-pass review volume by 30–50% on well-configured matters.
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Logikcull is the early-case assessment and intake layer. When new custodian data arrives — hard drives, PST exports, cloud collection outputs — Logikcull ingests, processes, and deduplicates it. Its matter-based pricing model (~$29K/year average, per Vendr transaction data) makes it the right tool for firms running dozens of discrete matters where per-matter cost predictability matters more than deep review depth. The handoff: Logikcull processes and deduplicates the raw collection, runs early keyword culling to eliminate clearly-irrelevant populations, then exports the responsive universe for Relativity review. Logikcull’s self-service intake removes the litigation support bottleneck that otherwise delays matters by 3–7 days per collection cycle.
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Everlaw is the case strategy and deposition intelligence layer. Where Relativity is review-depth optimized, Everlaw is argument-construction optimized — its storyboarding, chronology, and deposition prep tools let trial teams build the case narrative from within the same platform where documents are reviewed. Everlaw’s AI Deep Dive (generally available as of late 2025) allows attorneys to query the document corpus in natural language and surface thematic clusters without batch coding. Annual subscriptions start at approximately $2,000–$5,000/month base plus $18–$35/GB for hosted data, with 15–20% discounts for committed annual volume (per pricing published by Everlaw). The handoff: after Relativity linear review identifies the hot documents and key custodians, trial counsel moves those document sets into Everlaw for deposition and argument preparation.
Why this combination
The three tools occupy different positions in the discovery lifecycle — intake and culling (Logikcull), production-scale linear review (Relativity), case construction (Everlaw) — and substituting one for another collapses functionality that matters at scale. Trying to run the entire lifecycle in a single platform forces trade-offs: Relativity’s review throughput and workflow management outperforms Everlaw at large reviewer populations, but Everlaw’s argument-construction tooling outperforms Relativity when trial counsel needs to build a narrative across thousands of hot documents.
The economics support the three-tool model when matters are large enough. Relativity’s RelativityOne pricing eliminates per-user fees (per the October 2025 pricing restructure), making it cost-effective for large review teams. Logikcull’s matter-based pricing keeps early-case assessment costs predictable. Everlaw’s per-GB model aligns cost with the data volume that actually makes it to trial-prep stage — typically a small fraction of the total ingested universe.
Cost reality
A fully-loaded instance of this stack for a mid-size AmLaw 200 firm running 15–30 active matters annually:
- Relativity (RelativityOne): $6,000–$25,000/month, scaling with peak-day active review volume; the median contract is around $9,600/year for smaller deployments, but AmLaw-grade production instances run $75K–$250K/year (estimate based on vendor pricing tiers and publicly available contract benchmarks).
- Logikcull: ~$29K/year average based on Vendr transaction data; heavy-matter firms typically negotiate $50K–$120K annual subscriptions.
- Everlaw: $2,000–$5,000/month base plus $18–$35/GB active data; a firm with 5–10TB under review at any time can expect $150K–$400K/year all-in.
Total annual stack cost: approximately $250K–$750K for a firm with steady AmLaw-grade litigation volume. The hidden costs are larger than most estimates capture: contract attorney billing ($50+/hour), litigation support headcount (1–2 FTE dedicated to platform management), processing fees for collection data before ingestion, and implementation/training time when adding new practice groups or offices.
The economics break at smaller scales. A firm running fewer than 5 significant matters per year at sub-500GB data volumes should evaluate Logikcull’s standalone matter-based tier or Everlaw alone before committing to the three-platform model.
Match rules
Right fit:
- AmLaw 50–200 firms with active litigation dockets generating 5TB+ of annual discovery data
- Corporate legal departments with recurring large-matter commercial litigation, government investigations, or regulatory review obligations
- Litigation support teams that handle concurrent matters and need platform-level workflow separation between intake, review, and production
- Teams subject to EDRM compliance requirements where audit trails, processing logs, and production records need to be defensible in court
Wrong fit:
- Solo practitioners or small firms with fewer than 5–10 matters per year — the per-matter cost structure of Logikcull alone is sufficient, and Relativity’s overhead is unjustifiable
- In-house teams whose discovery work is entirely outsourced to outside counsel — the platform investment makes no sense if your litigation support team isn’t driving review
- Firms whose matters are routinely under 50GB — the per-GB hosting math inverts badly at small volumes; a flat-fee eDiscovery service is cheaper
Common variations
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Everlaw-only for mid-size firms. Firms with 3–8 active matters at any time and data volumes under 2TB frequently run Everlaw end-to-end (intake through trial prep), skipping both Relativity and Logikcull. Everlaw’s unlimited-user model and built-in processing make the economics work at this scale. Switch to the three-platform model when review team size exceeds 20 concurrent reviewers on a single matter or when peak-day data volumes strain Everlaw’s pricing tier.
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Add Reveal/Brainspace as the AI review layer inside Relativity. Reveal (which acquired Brainspace in January 2021, making Brainspace a product line inside the Reveal platform) is an AI-native document review platform that integrates directly with Relativity as a plugin, adding continuous active learning, conceptual clustering, and cross-matter learning to the Relativity workspace. Teams using Reveal inside Relativity typically see first-pass review populations cut by 40–60% before attorney eyes touch documents. This is a Relativity-ecosystem addition, not a replacement — include it when review team size is 15+ attorneys and per-document review cost reduction is a budget priority. Note that Reveal acquired Logikcull (this stack’s intake layer) in August 2023, so Reveal/Brainspace and Logikcull are now the same vendor — Logikcull still ships under its own brand for self-service intake, but if you adopt the Reveal review layer you are consolidating onto a single vendor rather than adding an independent third party. Reveal/Brainspace does not have an ooligo page; reference the Relativity app marketplace for current integration details.
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DISCO as a Relativity alternative. DISCO’s cloud-native review platform competes directly with RelativityOne for the same AmLaw audience. The argument for DISCO: stronger native AI review capabilities pre-2026 (before Relativity bundled aiR) and a simpler per-GB pricing model. Post-2026, Relativity’s aiR bundling narrows the AI gap. Firms already in the Relativity ecosystem rarely switch; DISCO is the more likely choice for firms building a new discovery infrastructure from scratch.
What this stack does NOT replace
- Outside counsel review teams — the platform automates coordination and reduces per-document cost, but attorney judgment at privilege and responsiveness decisions remains human
- A litigation hold and legal hold notification system (Exterro, Zapproved) — hold management is upstream of collection and outside the review platform’s scope
- A contract attorney staffing relationship — the platform manages workflow but not the legal labor that executes it
- An EDRM-compliant collection tool (Nuix, Lighthouse) for forensic-grade custodian data collection from endpoints, mobile devices, and cloud systems — ingestion into this stack assumes collection has already happened
- A TAR (Technology Assisted Review) protocol or validation methodology — the AI tools in these platforms assist review but the defensibility of the TAR protocol in court requires separate methodology documentation and expert support