Two AIs in Your Review Tool: Investigation vs Classification

Review AI sorts the boxes; investigation AI asks what the boxes say.

Suppose a solo litigator receives several thousand emails on one contract dispute. Two machines could help. The first reads each document alone and assigns it a label: responsive or not, privileged or not. The second reads across the whole collection and answers a question, such as when the client first learned of the defect, with every sentence tied to an exhibit. Both are sold as AI. They are not the same instrument.

I call the first review AI, a classifier judged by recall and precision. I call the second investigation AI, an agent that builds a sourced fact narrative. DISCO's August 2026 announcement of Advanced Research belongs to the second kind.

The two are defended differently. A 2026 Journal of Technology Law & Policy article by Aron Ahmadia and four co-authors holds that courts and opposing parties "rightly demand heightened verification" of AI-driven review, and it answers that demand statistically, measured against the Federal Rules of Civil Procedure. The research section below walks through these findings. Sampling says little about one sentence in a chronology.

A practitioner described the asymmetry well: weeding out non-responsive documents is low-value work, closer to spam filtering, while the real goal is understanding what happened and how it bears on the claims and defenses. In other words, before choosing a tool, a small firm should ask which question its matter turns on: what each document is, or what happened. Either way, the first thing to examine is exactly what a classifier does and does not know.

What is review AI, and why is it a classifier?

Review AI is software that assigns a label to one document at a time: relevant or not, privileged or not, about an issue or not. It answers sorting questions, not factual ones.

The frustration of reading thousands of documents and still not knowing the story comes from treating two jobs as one. One job is sorting documents into piles. The other is answering what actually happened. To tell whether a tool does the first job, ask three questions:

1. Does its output attach to a single document?
2. Is that output a label drawn from a fixed set of choices?
3. Can its accuracy be stated as recall and precision against a sample?

If all three answers are yes, the tool is a classifier, whatever its marketing calls it.

83.9% recall and 84.7% precision are the relevance figures a 2026 Journal of Technology Law & Policy case study recorded when Relativity's aiR for Review coded a 1,600-document sample from a real matter alongside human reviewers. Recall is the share of truly relevant documents the tool found; precision is the share of its relevant calls that were correct. The same study found issue coding "more mixed," with accuracy depending on the nuances of each issue. Those are strong numbers for a sorting task. They say nothing about whether anyone now understands the case.

The lineage is older than the label. Redgrave Data scientists, speaking on the LawNext podcast in 2024, described technology-assisted review (TAR) as a process rather than a particular model: the classifier inside it can be logistic regression, a support vector machine, or now a generative model. They placed the technical settlement of TAR 1.0, which trains a classifier and then applies it, and TAR 2.0, which feeds reviewer decisions back continuously, around 2012-2013. A practitioner made the same point in a 2024 forum thread: predictive models have handled review coding for more than a decade, while generative AI "literally generates new things, like summaries of documents."

The common assumption is that placing a large language model inside a review tool changed what the tool does. It changed the classifier. It did not change the job. A tool that waits for a reviewer's prompt and returns a label is an assistant to a sorting task, however often it is described as an agent.

Combining 5 sources points to one practical step: before buying, ask the vendor for recall and precision measured on a sample from your own matter, not a benchmark drawn from someone else's. In practice, it shrinks the pile. It does not tell you what the pile means. A classifier can also be steered by what it reads, a risk examined in whether a planted document can fool your AI review tool.

I find Relevant e-Discovery's own test instructive here: the buyer brings a messy collection and a hard question, then watches the tool read, code, and cite their own evidence. Coding is only one of the three verbs in that sentence, and the other two belong to a different kind of machine, the kind the 2026 agentic-research launches set out to build.

Can an attorney trust and defend facts an AI agent found on its own?

Yes, but only when every answer links back to the exact exhibit it came from, so the attorney can verify each claim before filing rather than trusting the narrative whole.

A review classifier is defended with arithmetic. The team draws a random or stratified sample, measures how often the tool's labels agree with a human's, and reports confidence intervals and margins of error. That statistical showing is how technology-assisted review earned acceptance, and practitioners note the same analysis has applied to TAR for a decade. It now extends to AI review on one condition: a licensed attorney decides relevance and signs off that the process meets the standards of the jurisdiction.

Investigation AI is different in kind. It is software that reads across a collection, chooses its own searches, and returns a narrative answer to a factual question. Consider what sampling would mean for such an answer. Suppose the agent reports that a supplier was warned about a defect before the contract was signed. There is no population of labels to sample. There is one sentence. Either an exhibit supports it, or nothing does.

The research section above walks through these findings. Sampling cannot score a sentence. The defense has to come from the structure of the tool, and three properties carry it:

1. Every claim traces to its source. Each statement in the narrative links to the exhibit it came from, so the attorney checks the document rather than the summary. This is the structural answer to the sanctions risk that Mata v. Avianca made famous.
2. The record cannot drift. Originals stay immutable under content hashing, the audit trail is append-only, chain of custody is documented, and a fail-closed privilege gate stops privileged material at production.
3. The evidence stays in the firm's control. Processing runs single-tenant, or inside the firm's own AWS account under its own keys, with no vendor retention and no model training.

Relevant e-Discovery builds all three into the same tool, which matters because a narrative is only as defensible as the weakest link between a sentence and its exhibit. The third property also bears on privilege, a question with its own wrinkle that is examined in how AI use alone won't waive work product, but one setting can. An in-account deployment under the client's own keys is a trust posture that no consumer AI tool, and almost no small-firm tool, can match.

Responsibility does not move with the work. Practitioners point to documented cases in which attorneys were disciplined for errors an AI produced. The tool holds no license; the signature on the filing does. Comment 8 to ABA Model Rule 1.1 asks lawyers to keep up with the benefits and risks of relevant technology, which includes knowing how a tool reached its answer.

That shift in where trust lives has a price attached, and for a small firm the next question is what each kind of AI actually costs on a single messy matter.

What does each kind of AI cost a small firm on one messy matter?

AI-assisted review runs cents per document, against dollars per document and roughly $19K per gigabyte for manual review, a difference a small firm can check on its own matter.

Market pricing for prompt-based review has been far less settled. In a 2025 r/ediscovery discussion, practitioners reported genAI review products at $0.20 to $0.95 per document, sometimes charged per document per run. In that 2025 thread, one commenter put Relativity aiR's entry price at $0.20 per document, or $20,000 per 100,000 documents for each AI run. A vendor in the same thread claimed that self-hosting open-source models cut the cost per document 5 to 7 times, though that figure was the vendor's own.

Per-run billing has a consequence that is easy to miss. Every revision to the review prompt is billed again. A firm that refines its instructions three times pays for the collection three more times.

The year before, in 2024, a practitioner on the same forum judged genAI review to cost roughly the same as human review and set the goal at one-tenth of that price. Other practitioners have described current AI review workflows as a real undertaking, expensive, and a poor fit for some cases.

Consider what these numbers optimize. For a classifier, cost scales with documents multiplied by runs. That is the right variable for a large production. For a firm of 1 to 20 attorneys holding one messy collection, a different number is worth asking for: what one factual question, answered across the whole collection with its exhibits attached, will cost. I would therefore price the questions before the labels.

Three checks make the comparison concrete:

1. Ask whether the price is per document, per run, or per matter, and what a revised prompt or a second pass costs.
2. Ask whether the tool can be operated by the attorneys on the matter, or whether it assumes an administrator.
3. Ask how a factual question asked across the whole collection is billed, and whether each answer arrives with its exhibits attached.

The second check is where enterprise economics usually fail a small firm. Relevant e-Discovery was built to be operable and affordable for a solo or small firm on a single messy matter, the segment that Relativity and Everlaw price out and cannot serve without an administrator. The design joins the defensible spine of enterprise platforms (Bates numbering, privilege handling, chain of custody) to the cited Q&A and chronologies of the newer AI entrants, so the firm is not buying one product per kind of AI.

The third check matters most, because a cheap answer that cannot be traced is not cheap at all; the reasons are set out in why source-linked review is the new defensibility bar. Once the price of an answer is known, the remaining question is what the 2026 launches changed about who asks it.

Which kind of AI should a small firm adopt first?

Investigation AI, because a small firm's real bottleneck is understanding what happened, while classification will keep running underneath as the layer that makes a production defensible.

Classification is not going away. In a 2025 r/ediscovery thread, one commenter expected some clients to keep requesting linear review or CAL workflows, and noted that aiR "isn't capable of handling all review scenarios and/or document types." Generative models also reduce document labeling without ending it, since labeled examples are still needed to write prompts and to measure the results. The classifier stays. It becomes plumbing.

Consider how slowly the plumbing arrived. By 2024, 12 years had passed since Magistrate Judge Peck approved technology-assisted review, and adoption was still described as slow. My judgment is that investigation AI will move faster, for one reason: an attorney can check a cited sentence against its exhibit in minutes, without a statistician. Trust that can be verified one claim at a time does not have to wait for a validation protocol.

That reverses the usual order of the market. Review AI was built for the matters with the most documents and the largest teams. Investigation AI earns the most where there is one attorney, one collection and no time, which means the solo litigator with a single messy matter is not the last customer for this technology but perhaps the first, and the practical question becomes which tool lets that attorney ask.