TL;DR: eDiscovery moves through the EDRM stages - from identification and collection to review and production. AI has transformed the review stage, where most of the cost lives: early case assessment, issue coding, privilege spotting, summarization, and search. Judicio is a review-and-analysis workspace for that stage, not a collection or production platform - and this guide explains exactly where it fits.
Discovery is where modern litigation is won, lost, and - too often - bankrupted. The volume of electronically stored information in even a routine dispute has grown beyond anything a team can read end to end, and the cost of review now dwarfs every other line in the litigation budget. Artificial intelligence is reshaping that economics, but the marketing around it blurs an important distinction: eDiscovery is a pipeline of distinct stages, and AI helps far more at some than others. This guide maps the process honestly and shows where an AI workspace genuinely earns its place.
What is eDiscovery, and how does the EDRM map the process?
eDiscovery - electronic discovery - is the process of identifying, preserving, collecting, reviewing, and producing electronically stored information (ESI) in litigation, arbitration, or an investigation. The most widely used map of that process is the Electronic Discovery Reference Model (EDRM), which lays the work out as a series of stages that move from a large, messy universe of data down to the focused set of documents that actually matter to the case.
The stages are not strictly linear, and the cost is concentrated in one place: review. The table below walks the model and is candid about where an AI review-and-analysis workspace like Judicio fits and where a dedicated eDiscovery platform is the right tool.
| EDRM stage | What happens | Where Judicio fits |
|---|---|---|
| Information governance | Managing data before a dispute arises | Out of scope - records and IG systems |
| Identification | Locating potentially relevant sources and custodians | Out of scope - eDiscovery platform |
| Preservation & legal hold | Issuing holds and preventing spoliation | Out of scope - no legal-hold or custodian management |
| Collection | Defensibly gathering ESI from its sources | Out of scope - dedicated collection tools |
| Processing | De-duplicating, indexing, and converting ESI | Out of scope - processing engines and Bates pipelines |
| Review | Reading for relevance, issues, and privilege | Core strength - Document Review and Review Matrix |
| Analysis | Finding facts, themes, and chronologies | Core strength - Research, Timeline, fact-finding |
| Production | Producing documents in agreed formats | Out of scope - production and Bates handled elsewhere |
| Presentation | Using documents at hearing or trial | Partial - summaries, chronologies, evidence packs |
What counts as ESI, and why does it balloon so fast?
Electronically stored information is any data created or stored in digital form - and in a modern matter that is almost everything. Email remains the backbone of most productions, but ESI now spans chat and messaging threads, shared documents and spreadsheets, PDFs and scans, calendar entries, and the metadata that travels with each of them. A single mid-sized commercial dispute can put tens of thousands of documents in play; a large one runs into the millions.
Two features of ESI drive the cost. The first is volume: digital communication multiplies copies, drafts, and forwards, so the raw collection is far larger than the set that matters. The second is that relevance is sparse and buried - the handful of documents that decide a case hide among thousands of routine ones. That is precisely the shape of problem AI is good at: reading a great deal quickly and surfacing what deserves a human's attention. The procedural backdrop in the United States comes from the Federal Rules of Civil Procedure, and the Sedona Conference publishes the leading practitioner guidance on proportional, defensible eDiscovery.
Where does AI genuinely help in eDiscovery?
The honest answer is at the review and analysis stages - the part of the EDRM where most of the hours and most of the budget are spent. Once ESI has been collected and processed by a dedicated platform, you are left with a corpus to read, and that reading is where AI changes the economics. Three capabilities matter most.
Early case assessment
Before you commit to a review protocol, you need a fast sense of what the documents say - the key players, the dates, the themes, and the obvious risks. Document Review takes multiple files in a single run and answers a checklist of questions against them, each finding cited to the exact page and quoted passage, so you can size up a representative sample quickly. A Review Matrix applies up to 25 questions across multiple documents at once, producing a grid that tells you at a glance which documents touch which issues.
Issue coding and summarization
The bulk of review is deciding what each document is about and whether it bears on a pleaded issue. AI accelerates this by summarizing long documents, extracting the parties and dates, and answering targeted questions across a batch - turning a cold read of every page into a triage you can scan. Because every answer carries a page-level citation and a confidence signal, you read the source behind anything that matters rather than trusting a summary. Our guide to bulk document review with AI covers the mechanics in depth.
Privilege spotting and sensitive-data review
Privilege review is high-stakes: a single inadvertently produced privileged document can cause real damage. AI helps by flagging documents that look privileged - communications involving counsel, legal-advice language, work-product indicators - so a human reviewer can focus on the borderline calls. The same approach surfaces sensitive personal data that may need careful handling. AI does the first pass and points to the evidence; the privilege call itself stays with the lawyer, every time.
What does Judicio not do in the eDiscovery stack?
Being clear about limits is part of using any tool well. Judicio is a document review, research, and analysis workspace. It is not a full eDiscovery suite, and it deliberately does not try to be. It does not collect ESI from custodians, laptops, or servers; it does not manage legal holds or custodian questionnaires; and it does not run the processing, de-duplication, and Bates-stamped production pipeline that a formal production requires. Those stages - identification, preservation, collection, processing, and production - are the province of dedicated eDiscovery platforms built for defensible data handling at scale.
Where Judicio earns its place is after collection and processing: when you have a set of documents and need to understand them. You export or load the reviewable set into Judicio's File Library, then review, search, summarize, build chronologies, and find facts. Think of it as the analytical layer that sits on top of, not in place of, your collection and production tooling. For repeatable, large-scale culling specifically, predictive-coding workflows belong to dedicated platforms - which is the subject of the next section.
Where do TAR and predictive coding fit?
Technology-assisted review (TAR), also called predictive coding, is a method in which a machine-learning model is trained on reviewer decisions and then used to rank or classify the rest of a large collection by likely relevance. It is a well-established, court-accepted way to prioritize and cull review populations numbering in the hundreds of thousands or millions, and it lives inside dedicated eDiscovery review platforms. If you want a grounding in how it works and where it is appropriate, see our explainer on what TAR and predictive coding are.
It is worth being precise here: Judicio does not offer TAR or predictive-coding relevance scoring, and you should not treat it as a substitute for a platform that does. What Judicio offers is a different, complementary kind of help - question-driven review, cross-document comparison, summarization, and fact-finding cited to the page - on the set of documents you have already narrowed. On a large matter, a sensible division is to use a TAR-capable platform to cull the collection to a workable review set, then bring that set into Judicio for close, citation-backed analysis.
What does an AI-assisted review workflow look like in Judicio?
Once you have a reviewable set, the workflow is straightforward and keeps you in control of every call. The pattern is the same one that runs through all of Judicio's tools: one upload feeds everything, and every output cites its source.
| Step | What you do | What the AI does |
|---|---|---|
| 1. Load | Upload the processed review set into the File Library | Runs OCR on scans and extracts parties, dates, and values per file |
| 2. Triage | Ask checklist or matrix questions across the batch | Returns page-cited answers with confidence signals |
| 3. Read | Open the cited passages behind anything that matters | Highlights the exact region in the source document |
| 4. Sequence | Build a chronology of the key events | Extracts dated events with deadline flags, each cited |
| 5. Export | Assemble findings, summaries, and an evidence pack | Exports to Word, Excel, CSV, or PDF with citations |
Because the same files in the File Library feed Document Review, the Review Matrix, Legal Research, and the Timeline Builder, you never re-upload, and a fact you find in review can flow straight into a chronology. Findings are accepted, edited, or flagged with a note - Judicio does not assign documents to colleagues or support co-editing, so the review stays a structured reading aid rather than a workflow-management system. For organizing the exhibits themselves, see our guide to AI for evidence organization.
How do you keep review proportionate and defensible?
Discovery obligations are governed by proportionality - the effort must fit the stakes - and by defensibility, the ability to show that your process was reasonable. AI helps on both fronts when used carefully. It makes review faster and cheaper, which supports proportionality, and its page-level citations create a clear record of where each conclusion came from, which supports defensibility. But the responsibility for the process remains yours.
Two habits keep AI-assisted review safe. First, verify: every privilege call, every relevance decision, and every fact you intend to rely on should be confirmed against the cited source before it leaves your hands. Second, document your method, so you can explain to a court or an opponent how the review was conducted. Judicio supports both - it does not train on your data, hosts on Google Cloud Platform, and provides role-based access with an audit trail through projects and roles - but the AI assists, and the lawyer remains accountable. Outputs are not legal advice. For turning a reviewed set into a usable fact record, see AI fact management in litigation.
How do you get started with Judicio?
Start where AI helps most: take a processed review set from a live matter - exported from your eDiscovery platform - and run it through Judicio alongside your usual process. Load it into the File Library, run a Document Review or a Review Matrix over a representative batch, and build a chronology of the key events. Compare the time against a manual read, verify the cited findings, and judge it on your own documents.
You can try it with a 7-day free trial - 500 credits, no credit card required. Professional access is $200 per month for 5,000 credits. For litigation and investigations teams that want a walkthrough of where the review-and-analysis layer fits alongside their collection and production tooling, get in touch. Judicio takes on the reading, the comparison, and the fact-finding; the privilege calls, the strategy, and the production stay where they belong.