How Conversational AI Answers Questions About a Case File

In most litigation and prosecution work, the answer to a question about the case is already somewhere in the case file, and that file might hold two hundred hours of body camera video, eleven recorded interviews, four months of surveillance footage, three banker boxes of scanned reports, and a deposition transcript that runs to four hundred pages. A question such as "have we seen this pattern before" or "where was the witness at nine o'clock" is reasonable to ask, but answering it by hand means watching, listening to, and reading most of that material, and the recordings take about as long to review as they took to record.

A conversational Artificial Intelligence (AI) agent for legal evidence lets the legal team put that question to the case file directly, in plain language, and get back a written answer that cites the evidence it was drawn from. The answer is composed from the team's own evidence rather than from the model's general knowledge, and the citations exist so that a lawyer can open each source and check that it supports the answer before relying on it.

What a conversational agent for legal evidence actually is

A conversational agent in this context is a Retrieval-Augmented Generation (RAG) system grounded in a specific case file, which means every answer is built in two steps from evidence the team already holds. When a question arrives, the agent first finds the passages in the case file most relevant to it, such as a stretch of interview transcript, a description of what a piece of footage shows, or a page of a scanned report. It then composes a written answer from those passages and shows citations to them alongside the answer, so the reader can open the material the answer drew on.

The boundary on what the agent draws from is what makes it usable in a litigation setting, where every fact carries weight and every claim has to be traceable to something in the record. Because the answer is composed from retrieved evidence, it points back to sources the team can examine. The agent can also be bounded to a single matter, so that it answers from that case file rather than from everything else the organization stores. Grounding narrows what an answer rests on, but it does not replace reading, because the citation shows which material an answer used while the question of whether each statement actually follows from that material remains the lawyer's judgment.

A conversational agent for legal evidence is also different from a legal research tool, because it works over the evidence in a particular matter rather than over case law, statutes, or a library of contracts. Legal research and drafting assistants answer questions about the law and about the files a firm loads into them, while an evidence agent answers questions about what the recordings, interviews, and reports in this case actually contain.

How a conversational agent answers across mixed evidence

The agent can only answer from evidence it can read, so before any question is asked, the recordings and documents in the case file are processed into text the agent can work with. Speech in audio and video is transcribed and aligned to the timeline, with different speakers separated. What a video shows is described in words and timestamped, which matters for surveillance and body camera footage that carries little or no speech, and Optical Character Recognition (OCR) reads the text in scanned reports and on screen in video frames. Without that processing, a recording is a file the agent cannot see into, and a question about what happened in it would go unanswered.

Because transcripts, visual descriptions, and document text are all available to the agent at once, a single question can draw on several kinds of evidence. An answer might combine a sentence from a recorded interview, a description of a moment in body camera footage, and a paragraph from a scanned report, with a citation to each. The reviewer reads the written answer first and then opens the citations to see the material behind it, and because transcripts and visual descriptions are aligned to the recording's timeline, a cited passage from a recording belongs to a specific point in it that the reviewer can go to and watch.

Asking in plain language matters because the questions a litigation team actually has are questions about the case, not lists of words to look up. "Where does the witness's account of the evening differ from what the surveillance footage shows" is one such question, and "what did each interviewee say about the supervisor's instructions" is another. The agent works out which evidence bears on the question, composes an answer from it, and cites what it used, which leaves the lawyer with the work that needs a lawyer: reading the cited material and deciding what it means for the case.

What this looks like in actual case work

The examples below show the kinds of questions a legal team puts to a case file, and in each one the answer is a starting point that a lawyer checks against its citations.

A district attorney's office handling a string of armed robberies wants to know whether the suspect's method appears in the office's earlier cases, and asks an agent whose scope covers its closed matters. The answer names earlier matters with similar fact patterns, drawn from interview transcripts and descriptions of surveillance footage, and cites the specific passages in each. The attorney opens those citations and decides whether any of the earlier matters is genuinely comparable.

A litigation team preparing for trial asks what the six cameras covering a loading dock show between nine and ten o'clock on the night in question. Because the footage has been described in words and timestamped, the answer gives an account of what the descriptions record in that hour and cites the moment behind each part of it, and the team watches every cited moment to confirm it before relying on the account.

A prosecutor asks where a witness's deposition testimony differs from the surveillance recordings of the same evening. The answer quotes the relevant lines of testimony beside the described footage for the same times and cites both sources, and the prosecutor watches each cited moment and reads each cited line before using any of it to plan an examination.

An investigator working a multi-defendant case asks what each interviewee said about where one of the defendants was on a particular afternoon. The answer gathers the relevant statements from the recorded interviews and the document production, attributes each to its source, and cites it, including statements that exist only in audio and can be read because the speech was transcribed.

Answers like these often feed a larger case chronology, which the team assembles from the cited material and checks entry by entry before any of it is used in court.

In each of these examples the agent does not decide what the evidence means or what to do with it, because its job is to answer the question from the case file, cite where the answer came from, and put that in front of the lawyer in a form the lawyer can verify.

Why grounded answers with citations matter for litigation

Two properties of the agent's design decide whether its answers can be defended when a judge or opposing counsel asks how an AI-assisted finding was produced.

The first property is grounding, meaning that every answer is composed from passages retrieved from the team's own evidence and shown with citations to the material it drew on. Grounding makes an answer checkable, because each assertion can be compared against the source the answer cited, but it does not make the answer correct by itself. A model can still misread a passage or connect two sources in a way they do not support, which is why the citation is where the lawyer's review begins rather than where it ends.

The second property is control over where the evidence sits and who can reach it through the agent. The agent can run in the organization's own data center, in its own cloud subscription, or in a disconnected environment with language models hosted locally, so that privileged work product, protective-order material, and witness identities stay inside infrastructure the legal team controls. Retrieval also runs as the person asking, with that person's access permissions applied as a filter before any evidence is retrieved, so the agent cannot surface material the person could not have opened directly.

The defensibility argument here is structural rather than contractual, because a promise in a vendor's terms of service still leaves the team trusting the promise. When the agent runs in infrastructure the team controls and retrieval is bounded by each user's permissions, the deployment itself determines where the evidence goes and who can reach it, which is a stronger position to explain to a court than a contract clause.

Confidentiality is also where privilege questions turn, as the first written federal opinion on AI and privilege shows. In United States v. Heppner, the court found that a consumer AI tool's terms, which let the provider collect and disclose what was typed into it, left no reasonable expectation of confidentiality. An evidence agent that runs where the team controls the data, answers from the case file, and cites the material it drew on gives the team a clear account of how an AI-assisted finding was produced and where the underlying material went.

Asking questions of evidence in VIDIZMO AI Intelligence Hub

VIDIZMO AI Intelligence Hub provides the kind of conversational agent described in this piece, and it runs on the same VIDIZMO portal that stores the evidence and processes it into transcripts, visual descriptions, and recognized text. Users ask questions in natural language and receive answers grounded in the content the agent has been scoped to, with citations to the material each answer drew on, and a recording or document can also be attached to a conversation and asked about without first adding it to the library.

The deployment options and permission controls described above are properties of the platform rather than of a particular agent, so they apply to every agent a team configures. As with any grounded system, the citations show where an answer came from, and a person still confirms that each statement follows from the source it cites before the answer is relied on. For how law firms store and manage the evidence itself, see our article on how a legal digital evidence management solution helps law firms.