Workplace Investigations AI: Casepoint vs Everlaw vs Custom

By Jude Lee · November 1, 2026 · Comparison

Lawyers and a paralegal reviewing email evidence and a timeline during an internal workplace investigation

The short version

Internal and workplace investigations are quietly becoming a named AI-agent category: Law.com’s Legaltech News reported in October 2026 that Casepoint extended its AI agent suite into investigations, and Everlaw announced agentic review automation under the Everlaw Omni and Workflows banner. Re-verify both against the vendors’ own pages before you cite them — this category moves monthly.
Platform-native agents are the better buy when volume, custodian collection, and defensible chain of custody dominate the matter. A custom agent connected to your own systems is the better build when the evidence already lives in your DMS and email, and the repeated labor is chronology-building and report drafting.
No agent should make credibility determinations, scope decisions, or findings. Those are the parts clients are actually paying for.
Size the economics as a formula — hours × blended rate versus hosting plus build — not from anyone’s marketing math.

Why investigations became an agent category this fall

For two years, legal AI agent announcements clustered around the obvious places: research, contract review, discovery. That’s shifting. In October 2026, Law.com’s Legaltech News reported that Casepoint expanded its AI agent suite into investigations. Separately, Everlaw introduced Everlaw Omni and Workflows, positioning agentic intelligence and review automation at complex legal work rather than linear doc review alone — read that announcement at the source on Everlaw’s own site (press and product pages), with secondary trade coverage here.

The logic behind the launches is straightforward. An internal investigation — a harassment complaint, an expense-fraud tip, a whistleblower letter — is a small eDiscovery matter wearing a different hat: preserve, collect, cull, read, build a timeline, interview, write findings. It is also one of the few engagement types where small and mid-size firms routinely do high-volume document work without the budget of a full litigation team.

Skepticism about agents in practice is, in our view, well-placed and useful. The question isn’t whether an agent can summarize a Slack thread — it can — but whether your firm can supervise its output at the standard a client, opposing counsel, or a regulator will later test.

What an investigation agent actually does

Strip away the branding and an “investigation agent” is a multi-step worker that, given a defined scope, will: pull documents from a connected source, filter them against allegation criteria, extract who-did-what-when, assemble a draft chronology with citations back to source documents, flag gaps and contradictions, and produce a structured work product you edit.

That is genuinely different from a chatbot you paste documents into. The agent takes actions in systems — searching, tagging, writing files — which is both the value and the risk.

What it does not do well, in our assessment: scope an investigation (that’s a judgment call with legal consequences), weigh credibility between two conflicting accounts, decide what’s privileged in a close case, or conclude whether policy was violated.

An agent can tell you that three emails contradict the respondent’s account. Deciding what that means about the respondent is the job you cannot delegate.

Platform agents versus a custom build

Platform-native agents (Casepoint, Everlaw, similar)

You get collection tooling, defensible processing, audit logs, and review UI that already exists. Agent features ride on top of infrastructure built for evidence handling. Best when: the matter involves mobile devices, Slack/Teams exports, multiple custodians, or any realistic chance of litigation or regulatory production. Downsides: per-matter hosting economics, a workflow shaped by the vendor’s assumptions, and limited ability to encode your firm’s report format and allegation matrix.

Custom agent over your own stack

You connect an AI assistant — Claude, OpenAI’s or Google’s models, or an open-weight model you host — to the systems you already run, through MCP, direct vendor APIs, an orchestration framework, or your existing integration platform; MCP is one governed path among several, not the only one. Best when: the evidence is already in your DMS or a client-provided folder, volumes are modest, and the repetitive labor is chronology-building and drafting in your house style. Downsides: you own collection defensibility, access control, and logging. Nothing is defensible by default.

There’s a third option that gets skipped too often: don’t automate. If your firm runs two investigations a year, the setup cost of either path outruns the savings. Hire a contract paralegal and buy per-matter hosting. We walked through that comparison honestly in AI agent vs paralegal vs outsourcing.

A workflow that keeps the human where it matters

Scope and hold — human only

Define allegations, custodians, date range, and systems in writing. Issue the litigation hold. This drives everything downstream and is not an agent task; see legal hold automation options for the notification mechanics, which can be automated.
Collect with chain of custody intact

If there’s any litigation or regulatory exposure, collect through tooling that logs it. A custom agent reading a shared drive is fine for a low-stakes policy question and a problem in a case that later gets subpoenaed.
Agentic first-pass triage

Have the agent score documents against each allegation, not against a vague prompt like “find anything relevant.” One reusable instruction set per allegation type produces far more consistent output. The failure mode to watch here is recall, not fabrication: the agent quietly scores as irrelevant a document a human reviewer would have flagged, and nothing in the output tells you it happened. Before you rely on any agent-driven cull, pull a fixed sample of its negatives — say 5% or a set number of documents per custodian, whatever your matter can bear — and have a human read them. If the sample surfaces hits, widen the criteria and re-run. The same pattern we described for discovery triage applies directly here.
Chronology with hard citations

Every entry in the draft timeline must carry a document ID and date. If the agent cannot cite it, it doesn’t go in. This is the single most effective guardrail against confident fabrication.
Interview prep and summaries

Agents are strong at generating question outlines from the evidence set and at summarizing recorded interviews into a structured record. Our take on quality control for that output is in AI deposition summaries and case chronologies.
Findings — human authorship, agent assembly

Let the agent assemble the evidence appendix, the exhibit index, and the first draft of the factual background. The analysis and conclusions get written by the lawyer whose name is on it.

Privilege and collection law come before tooling

Internal investigations conducted by counsel implicate corporate attorney-client privilege under Upjohn Co. v. United States (1981), including the warnings given to employee witnesses. Feeding interview notes and draft findings into a tool — platform or custom — raises questions about where that work product lives, who at the vendor can access it, and whether it is retained for model training.

Two employment-side exposures bite earlier than privilege does. First, monitoring and collecting employee communications is constrained by notice and consent rules that vary by jurisdiction and by what your handbook and acceptable-use policy actually say — confirm what notice is required before you touch a mailbox or chat archive. Second, if any custodian sits in the EU or UK, collection and processing fall under the GDPR, which brings lawful-basis, minimization, and transfer obligations that a US-hosted review platform does not satisfy by default.

Review the vendor’s data processing terms and confirm your approach with experienced employment and privacy counsel, plus your state bar’s guidance, before the first collection — not after.

Sizing the economics without inventing numbers

Build your own model; don’t borrow one. The baseline formula is: hours your team currently spends on collection review, chronology, and report assembly × blended rate. The platform path adds per-matter or per-GB hosting plus licensing. The custom path adds a one-time build plus ongoing model usage and maintenance.

The block below is a sizing template — placeholders you replace with your own figures and quotes. None of it is measured data.

Hours × blended rate

How to size the manual baseline for one typical investigation

Sizing template — use your own figures

Per-GB + per-user

Common shape of investigation platform pricing — confirm with a written quote

Editorial observation — confirm with a written quote

2 reviewers

Minimum human review on any AI-assembled chronology before it leaves the firm

Editorial recommendation

The part firms forget is the revenue side. Recovered hours only count if they’re reallocated to billable or business-development work. If the paralegal’s freed time goes to no particular use, you’ve bought a nicer process, not a return. Our automation ROI framework walks through that honestly.

How to decide

Most small firms land in the middle: platform for collection and processing, a custom agent — or a well-defined set of reusable instructions your assistant follows the same way every time — for the drafting work that follows. That hybrid is unglamorous and usually correct.

One last point on supervision. ABA Formal Opinion 512 (July 2024) addressed generative AI and lawyers’ duties of competence, confidentiality, communication, and reasonable fees. Agents that act in systems raise the supervision bar further, because the failure mode isn’t a bad sentence — it’s a bad action, or a missed document, taken quietly at scale. Check your own state bar’s technology and AI guidance, which varies, before you put any of this in front of a client matter.

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Workplace Investigations AI: Casepoint vs Everlaw vs Custom

Why investigations became an agent category this fall

What an investigation agent actually does

Platform agents versus a custom build

A workflow that keeps the human where it matters

Sizing the economics without inventing numbers

How to decide

Where is your firm losing billable hours?

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