Insights for modern legal teams.

Every escalation and QC decision your review process makes is a labeled example of your own firm's judgment — and today it evaporates when the matter closes. The case for fine-tuning and owning a private model instead of renting a frontier API, and what building one requires.

Six ordered architecture decisions — model selection, deterministic pre-filtering, cascading, prompt caching, sampled QC, and fine-tuned open models — take combined AI review cost from 40¢ a document to under a nickel. Includes a worksheet for pricing it against Relativity.

A Discord or Slack channel has no natural document boundary. Here are the 25 architecture decisions behind DecoverAI's chat pipeline — collection, threading, message-level tagging and redaction, and RSMF production.

Instead of four separate ingestion pipelines, DecoverAI normalizes iMessage, Slack, Teams, and Discord into one canonical message schema, and only wraps it into an RFC 5322 / RSMF-style container at the production boundary.

Relativity's aiR prices AI-assisted privilege review at roughly 30 cents a document. Decover's ACP engine runs the same task for about $0.003 — 100x less — by clearing 58% of documents deterministically before any model runs.

A working cost comparison for a typical 100 GB commercial matter under traditional per-GB/per-document pricing vs. AI-augmented all-inclusive pricing — with full methodology.

Storage costs fractions of a cent per GB. AI handles the responsiveness pass. Yet vendors still quote $25/GB and $1.50/document. Here's the structural reason the model persists.

At $1.50 per document, a 250,000-document matter costs $375,000 in first-pass review alone — before a single privileged document is logged. Here's how the fee works and what AI replaces.

AWS S3 costs $0.023/GB/month. eDiscovery vendors charge $10–$40. Here's what the per-GB fee actually includes, what it silently excludes, and what the all-in alternative looks like.

Text extraction, OCR, and deduplication are near-zero marginal cost operations in 2026. They're still billed as major line items by legacy eDiscovery vendors.

On a 2,000-document privilege set, manual log entry fees run $20,000–$30,000. AI-assisted generation changes the math — here's how.

PM hours at $200–$300/hr accumulate as matters expand. Here's what they cover, how they compound, and what self-serve platforms eliminate.

Seat fees are the single largest cost driver in legacy eDiscovery platforms — and the reason bringing in an expert witness or co-counsel triggers a billing event.

Some vendors charge $25–$100 per gigabyte to release your own data when you switch platforms. Here's what the contract language actually says and what a mid-matter switch costs.

The five questions that reveal whether an eDiscovery quote is designed to be understood — or to be impossible to evaluate until the final invoice arrives.

With per-document pricing, doubling your data more than doubles your bill. With AI-augmented all-inclusive pricing, it doesn't. Here's the math on marginal cost scaling.

A reference table of realistic all-in eDiscovery costs at five matter sizes — from a small employment dispute to large commercial litigation — under two pricing models.

Per-GB hosting, per-doc review, AI-augmented workflows — a working benchmark of the same matter under two pricing models, anchored to Rule 26(b)(1) proportionality.

Forty capabilities every modern eDiscovery platform should ship — anchored to Victor Stanley on technology competence and FRCP Rule 26(g)'s certification mandate.

How LLMs can draft privilege descriptions and reduce QC cycles — while managing hallucination risk and the FRCP Rule 26(g) attorney certification obligation.

From Da Silva Moore to Hyles, Rio Tinto, Pyrrho, and In re Broiler Chicken — what the case law actually says about predictive coding and validation.

What the post-2015 FRCP 26(b)(1) line of cases actually says about scope, cost, and the burden-vs-benefit test — with the leading US and UK authorities.

What Zubulake, Pension Committee, and the 2015 amendment to FRCP Rule 37(e) require — and how to draft a hold that survives a spoliation motion.

FRE 502(b) and 502(d), Mt. Hawley v. Felman, SEC v. Thrasher, and West African Gas Pipeline — building a privilege workflow that survives a clawback dispute.

In re Xarelto, the Hague Evidence Convention, Aérospatiale, and the leading English data-protection authorities — planning a defensible cross-border review.

From Hubbell to the SFO's Section 2 powers and the ENRC litigation-privilege framework — a working playbook for the first 48 hours of a regulatory investigation.

Multi-model consensus classifiers, few-shot vs fine-tuned approaches, and the validation framework courts will actually expect from LLM-based review.

Relativity, Everlaw, CS Disco, Logikcull, and DecoverAI compared on pricing, AI capability, and matter fit — a practitioner-focused guide for 2026.

Seven eDiscovery platforms compared as Logikcull alternatives — pricing, AI capabilities, privilege log automation, and ease of migration.

Multi-model AI classifiers, privilege log automation, and TAR/CAL workflows compared across the leading legal AI platforms for eDiscovery in 2026.

How law firms are deploying AI for first-pass review, privilege detection, and automated privilege logs in 2026 — with the court cases that validate each approach.

How small and mid-size law firms can run AI-powered eDiscovery without a dedicated litigation support team — covering platform selection, pricing, and defensible workflows.

The eDiscovery platforms and AI review tools litigation support teams are using in 2026 — covering Relativity, Everlaw, Disco, and the AI-native challengers.

Drop it in. Get reviewable documents back. PST, MBOX, Office docs, scanned TIFFs, audio, chat exports, nested archives — here's what you can load into a case, and what DecoverAI does with it.

Boolean operators, phrase matching, proximity, and wildcards — the full reference for the search bar's exact-match mode.

Weighted multi-dimension scoring, multi-judge consensus, and statistical testing for high-stakes AI in legal workflows.

DecoverAI co-founder Ravi Tandon and Tom Whittaker examine how generative AI is transforming dispute resolution and eDiscovery workflows — the opportunities, the validation challenges, and the case for human oversight. Published by the Society for Computers & Law.

DecoverAI joins EDRM's global Trusted Partner Network, bringing AI-powered eDiscovery and the 360° Legal Brain™ to a community spanning 145 countries.

DecoverAI secures $2M in seed funding led by Leo Capital to transform legal practice through AI-driven eDiscovery, legal research, and case strategy.