AI Trial Prep: Everlaw vs CoCounsel vs a Custom Agent

By Jude Lee · · Comparison

Attorneys and a paralegal preparing exhibits and witness outlines for trial in a small law firm conference room

What trial prep looks like to an AI system

Strip the romance out of it and trial prep is a set of derived artifacts built from a fixed record: a case chronology, a witness list with examination outlines, an exhibit list with a foundation map (who authenticates what), deposition designations and counter-designations, motions in limine, proposed jury instructions, and an impeachment sheet that pairs each likely trial statement with the page and line where the witness said something different.

Every one of those is a retrieval-and-format problem layered under a judgment problem. The retrieval half — find every mention of the September inspection across 4,000 pages, pull the exact page:line — is mechanical, and AI is useful at it. The judgment half — is this admissible, will this help, what order do I call witnesses in — is not automatable and should not be.

Three realistic paths for a small firm

Platform-native AI. If your documents and transcripts already sit in an ediscovery platform, the AI features built into that platform have a structural advantage: the evidence is loaded, deduplicated, Bates-stamped, and linked to your coding. Everlaw markets narrative and storybuilding tools alongside its review AI; Relativity and DISCO have their own generative features. Check each vendor’s current documentation for what is actually on your plan — this category moves fast, and what is an add-on today may be bundled next quarter. The catch for small firms is that these platforms are priced for matters with real data volume. A two-witness commercial case with 900 pages doesn’t justify the hosting.

A general legal AI assistant. These are not interchangeable, and the differences matter for trial prep. CoCounsel (Thomson Reuters) is built around named, prebuilt tasks — its documented skill set has included things like document review, summarization, and deposition preparation, so you pick a workflow rather than engineer a prompt. Harvey is sold primarily as an enterprise platform with firm-configured workflows, which usually means a seat count and onboarding commitment sized for larger firms. Lexis+ AI’s differentiator is grounding in LexisNexis’s licensed research corpus with linked citation validation, which helps most on the research and motion-in-limine side rather than on your own transcript record. Verify the current feature list with each vendor directly; all three ship changes faster than any article can track. For a fuller feature-by-feature look at these platforms against a custom build, we compared them here.

A custom agent over your own systems. Here you connect an AI assistant such as Claude to your document and practice management systems through MCP — the Model Context Protocol, an open standard for giving an AI governed access to specific tools and data rather than a folder of uploads. Then you define skills: reusable packaged instructions that teach the assistant to produce your witness outline, your exhibit foundation table, your impeachment sheet, the same way every time. The agent reads from the matter, drafts, and writes the draft back into the matter folder. If you want the mechanics — how the server exposes data, how you scope permissions per matter — we walk through building a custom MCP server over firm matter data.

Platform-native AI (Everlaw, Relativity)
Evidence is already loaded and Bates-linked, so retrieval runs against the real record with your coding intact. Economics assume volume; a small case pays hosting for data it doesn’t have. Outputs live in the platform.
General legal assistant (CoCounsel, Harvey, Lexis+ AI)
Usable this afternoon with no integration. Prebuilt tasks give a decent first-pass chronology or deposition digest. But you upload the record manually, and the assistant doesn’t know your outline format or where the finished product belongs.
Either off-the-shelf option
Working within days. Vendor handles model updates, security posture, and evaluation. Best when trials are occasional. Cost is a subscription you can cancel. Someone still copies, reformats, and files the output by hand.
Custom agent + skills
Weeks of build plus ongoing ownership. Best when the same prep pattern repeats — a plaintiff firm trying similar cases, a defense shop with a house style. Drafts land in your DMS in your format. You own the maintenance, the prompts, and the failure modes.

The specific ways this breaks on a trial record

“Verify everything” is only useful advice if you know what you’re looking for. On transcript and exhibit work, the recurring failures are narrow and predictable. Page:line cites come back plausible but off — sometimes by a few lines, sometimes by pages — because the model reconstructs the reference rather than reading it. Recall degrades across very long transcripts: ask for every mention of a topic in a 400-page depo and you may get eight of the eleven, with no signal that three are missing. Silent omission is the dangerous one, because a short answer looks confident and complete. Models also conflate witnesses with similar names or roles, and will occasionally attribute testimony to the wrong deponent in a multi-volume set. None of that makes the tool useless — it makes the impeachment sheet a draft that a human cite-checks line by line against the source, not a work product.

The small slice of the record that decides the case

A handful of documents and a few dozen transcript pages carry a case; the rest is context. Lawyers know this. The problem is that you can’t identify the vital fraction without moving through everything else, and that pass is what eats associate weekends. That is the honest case for AI here — not “AI tries the case,” but “AI does the first pass so a human spends judgment where it counts.” The same logic drives agentic discovery triage earlier in the case, and good triage coding makes trial-prep retrieval measurably better later.

The value of AI in trial prep is not the draft it produces. It’s that a human reads the right forty pages instead of hunting for them.

What firms are actually running, in practice

As of 2026, the real answer for most small and mid-size shops is a stack rather than a product: AI features inside the practice management system they already pay for (Clio Duo, Smokeball Archie, MyCase IQ); a general assistant like Claude or ChatGPT for drafting and analysis; one or two point solutions for a high-volume job; and, in more advanced firms, a governed connection between the assistant and the firm’s own document store. That last layer is constrained by where your files live — if you’re weighing that decision, our comparison of iManage, NetDocuments, and SharePoint for AI agents covers which store supports which agent patterns.

Where a human has to stay in the loop

Cite-check everything. The sanctions cases involving fabricated citations — Mata v. Avianca in the Southern District of New York being the best known — were not exotic failures; they were lawyers filing unverified model output. ABA Formal Opinion 512 (2024) addresses lawyers’ duties when using generative AI, including competence, confidentiality, and client communication, and Comment 8 to Model Rule 1.1 has covered technology competence for years. Your state bar may go further — check it directly rather than relying on a summary.

Three steps should never run unattended: deposition designations and counters (a strategic call with page-line consequences), admissibility and foundation conclusions (the agent drafts the foundation map, you decide it holds), and pretrial disclosures under FRCP 26(a)(3) or your state analog. For designations specifically, confirm the required format, exchange sequence, and deadline against the governing pretrial order in that case — not against last trial’s practice.

Modeling the payback without inventing numbers

Don’t take anyone’s headline savings figure, including ours. Measure three things on one real trial:

Then express it as a formula you fill in: (baseline hours − new hours including verification) × your blended rate, minus tool cost. Say your baseline impeachment sheet takes 9 hours and the assisted version takes 4 including checking — plug in your own rate and your own two numbers, on your own matter. Add the second-order effects most calculations miss: recovered hours reallocated to billable work or an additional case, and errors avoided (a missed prior inconsistent statement has a cost you can’t invoice back). Treat AI as a margin lever on work product, not a revenue strategy — it does nothing for a weak referral pipeline or underpriced engagements.

How to pilot it on your next trial

  1. Pick one artifact, not the whole notebook

    Choose the impeachment sheet or the chronology. Single artifact, single matter, measurable.

  2. Write the standard down first

    Before touching a tool, document exactly what a good version contains and how it’s formatted. That document becomes your prompt — and later, your skill.

  3. Run the cheap option first

    Use whatever assistant you already pay for. If an off-the-shelf tool produces a mostly-there draft, you may not need a build at all.

  4. Verify against the source, every line

    Every cite, every page:line, checked against the transcript or exhibit. Track the error rate and the error types — that tells you whether the workflow is viable.

  5. Only then decide about building

    If the artifact recurs across matters and manual reformatting-and-filing is the bottleneck, that’s the signal for MCP and skills. If it was a one-off, stop here.

The unglamorous conclusion: most small firms should start with the assistant they already have and a written standard, and earn their way to a custom agent only after the same prep pattern has repeated enough times to justify owning it.

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