Legal intelligence for litigation and transactions: practical guides on document review, privilege, contract review, and due diligence, plus how we evaluate the AI behind them.
Press articles coming soon.
Why language models fabricate, why purpose-built legal AI tools still do it at measurable rates, and how hallucination, weak prompts, and automation bias land inside a document review — where nobody checks a citation. Plus the verification habits courts and ethics opinions already expect.
A step-by-step guide to side-aware review, clause-quoted findings, and tracked changes in your name — without leaving Word.
Five factors that make AI-assisted eDiscovery defensible: use-case scoping, ESI preservation, Rule 26(b)(1) proportionality, vendor vetting, and validation.
The seven eDiscovery capabilities that matter for a 20-to-200-attorney litigation practice: AI review that scales, defensible chain of custody, transparent pricing, automated privilege review, native chat data, collaborative workflow, and court-ready production.
AI-assisted privilege detection, automated log entry generation, and attorney QC workflow — a step-by-step guide to automating privilege logs under FRCP Rule 26(g).
Disclosure obligations, validation protocols, and how to document your AI-assisted workflow for the court.
Collection, BYOD complications, and production formatting for the data source nobody's figured out.
The difference between a visual redaction and a defensible one — and why it matters.
A step-by-step guide for moving your active matters to a new platform safely.
The pre-production QC steps that protect you from motions to compel, sanctions, and do-overs.
A practical guide for in-house legal teams and small firms managing discovery without dedicated eDiscovery staff.
Negotiate smarter, build flexibility, and avoid the traps that derail discovery.
A practical workflow from receipt to production — without the panic.
A step-by-step guide to defensible privilege logging — from tagging to production.
Privilege review is the most expensive line item in most eDiscovery budgets — $4–$8 per document plus $5–$15 per log entry. Where the AI savings actually come from: contextual triage instead of linear reading, automated log drafting, and the compounding effect at scale.
Lifecycle coverage, AI that survives contact with your own data, the litigation support gap, privilege log billing, and the ancillary fees that never make it onto the quote — eight things worth understanding before you sign a contract.
Which AI eDiscovery platforms corporate legal departments actually run in 2026, and how to compare them on the three things that decide it — cost you can forecast, security your InfoSec team will approve, and the handoff to litigation that demos never show.
Document review is 70–80% of discovery spend, and the driver is not volume. Five cost drivers the Winter 2026 eDiscovery Pricing Survey exposes — the billing unit, over-collection, rework, unquoted charges, and rate opacity — and what to do about each.
Most legal departments inherited their eDiscovery platform rather than chose it — and the cost shows up as accumulated delay nobody attributes to the tooling. Five sources of friction, the tell for each, and the questions worth asking before you renew.
In-house teams buy eDiscovery software for the Tuesday a hotline complaint lands, not for trial. Nine decision criteria for evaluating AI eDiscovery software on internal investigations and compliance reviews — with the question to ask each vendor and how to test the answer.
The most used and least defined word in e-discovery. Judge Shaffer answered it in 2012 with four criteria that had nothing to do with software, and those criteria still govern how courts evaluate generative AI document review.
Why legal departments inherit a stack built for law-firm economics, what changes when AI reads the corpus first, and eight criteria for evaluating platforms on internal investigations — with the denominator behind every savings claim.
DecoverAI vs Relativity, Everlaw, DISCO, and Logikcull: an AI-native system that unifies your document store, review database, and AI, on flat published pricing, with data that never leaves your control.
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.
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.
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.
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.
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.
DecoverAI vs Relativity, Everlaw, DISCO, and Logikcull: an AI-native system that unifies your document store, review database, and AI, on flat published pricing, with data that never leaves your control.
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.
A controlled benchmark across eight large language models on a gold-labeled 498-document set. Accuracy plateaus near F1 0.86 — and the most expensive model tested cost 54× more while scoring lower.
Nine models. A 68× cost spread. An 11-point F1 range. The data shows the most expensive model scores lower than options costing 30× less — and why precision matters more than recall for privilege.
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.
Side-aware contract review in Microsoft Word and data-room due diligence join the litigation workflows behind a $15.4M jury verdict and a million-document fraud matter.
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.
9 step-by-step guides. Review, redline, extract, and track commercial agreements, clause by clause. Each comes with a copyable template and a worked example.
8 step-by-step guides. Discovery, privilege, review, and case-preparation workflows for litigators. Each comes with a copyable template and a worked example.
8 step-by-step guides. Diligence, disclosure, and closing workflows for deals and financings. Each comes with a copyable template and a worked example.