AI for Body-Cam Evidence: JusticeText vs Everlaw vs Custom

By Jude Lee · · Comparison

Defense lawyer and paralegal reviewing timestamped body-camera video and transcript on office monitors

The job nobody prices correctly

A single misdemeanor DUI can arrive with four officer body-cam files, a dash-cam, booking video, and a 911 call. A felony case with multiple officers and a jail-call production can run into dozens of hours of media. The fee was set at intake. The motion deadline is fixed. Somebody still has to watch all of it.

Most small firms resolve this by not watching all of it — scrubbing to the stop, the search, and the Miranda warning, and hoping nothing important lives in minute 47 of the third officer’s camera. That’s a real risk, not a theoretical one. It’s also a work profile that machines handle unusually well: long, linear, low-density material where most minutes carry nothing and a few carry everything.

What “AI review” of audio and video actually means

Strip away the marketing and there are four distinct capabilities, and they are not equally mature.

Transcription with timestamps and speaker separation. Modern speech-to-text handles clean audio well. It degrades on roadside wind noise, crosstalk, and poor-quality jail recordings — exactly the conditions criminal evidence arrives in. Expect to correct names, street names, and slang.

Search across an entire production. Once media is transcribed, you can search every file for “consent,” “I want a lawyer,” or a street name. This is the majority of the practical value and the least hype-dependent part of the stack.

Summarization and flagging. An AI can produce a chronology of a stop or flag segments where a model thinks something notable happened. Useful as a triage map. Treat every flag as a pointer to footage you’ll actually watch, not as a finding.

Cross-source comparison. Lining up what an officer wrote in the report against what the video shows is where defense lawyers find leverage. Models surface candidate discrepancies; they also invent discrepancies that aren’t there. This is a human-verified step, always.

Where each of the three options fits

JusticeText was built for this exact job — audio and video evidence in criminal defense — and that focus shows in the workflow: clip creation, timestamped notes, transcripts organized by case rather than by document. If you handle a steady docket of media-heavy cases and want something working this month, a purpose-built tool is usually the right first move.

Everlaw is an ediscovery platform that also handles media. If your practice mixes criminal defense with civil litigation — or you occasionally catch a large, mixed production of documents plus media — a platform gives you one review environment and real production tooling. For a solo running pleas and suppression motions, it is likely more machinery than the work requires.

A custom agent means wiring the pieces yourself: a transcription service writing to your own storage, an agent that runs a defined review routine over the transcripts, and an MCP connection into your practice-management system so output lands on the matter instead of in someone’s downloads folder.

Purpose-built evidence AI
Working in days. Interface designed around clips and testimony. Vendor handles transcription quality and infrastructure. You accept their summary format, retention policy, and roadmap. Per-seat or per-hour-of-media pricing scales with caseload — fine until caseload grows.
Custom agent on your own stack
Weeks of build time, real maintenance. You choose where media and transcripts live, including on-premises if a protective order or your own judgment demands it. Output format matches your motion templates. Cost is build-plus-usage rather than per-seat. You own the failure modes too.

Evaluating a vendor properly

A buy decision deserves the same rigor as a build. Run this before signing anything longer than month-to-month.

  1. Trial on a real production, not the demo file

    Load an actual closed matter with its full media set. Demo footage is clean; your discovery isn’t.

  2. Spot-check accuracy on your worst audio

    Take your roughest jail call or windiest roadside clip, transcribe it, and hand-verify a five-minute stretch word by word. That number — not the vendor’s — is your working accuracy.

  3. Read the retention and deletion terms

    How long is media stored, who can access it, how do you force deletion, and is deletion confirmed in writing? Map that against your protective orders.

  4. Ask for the subprocessor list

    Who actually performs transcription and model inference, in what jurisdiction, and is your data used for training? Get it in the contract, not the sales call.

  5. Confirm the export path before you need it

    Transcripts, clips, notes, and timestamps out in an open format if you leave or the vendor shuts down. No export path is a lock-in you’ll discover at the worst moment.

Building the custom path

  1. Fix the intake of media

    One place media lands per matter — a folder in your DMS or practice-management system — with a consistent naming convention. Nothing downstream works without this.

  2. Pick a transcription engine deliberately

    Two realistic shapes: a Whisper-class open-weights model you run on your own hardware (strongest answer when a protective order argues against third-party cloud storage), or a hosted ASR vendor such as Deepgram or AssemblyAI (better diarization tooling, less infrastructure to own). Keep originals untouched; write transcripts alongside them.

  3. Write the skill, not the prompt

    Package review instructions as a reusable skill: what a chronology entry looks like, which moments to flag (stop, consent, search, warnings, use of force), and a requirement to cite file name plus timestamp for every assertion. Same discipline as standardized document review skills.

  4. Expose your systems over MCP

    A custom MCP server lets the assistant read the case file and write its chronology back to the right matter, with permissions you control. MCP is an open specification, and the agent layer is not vendor-locked — this pattern can run on Claude, on OpenAI or Google models, or on an open-weights model you host. Verify current MCP support in whichever assistant you choose.

  5. Require verification before anything is used

    No flagged moment enters a motion until a human watches that segment in the source media. Build it as a checklist step, not a hope.
Every AI flag is a pointer to footage you still have to watch. The time saved is in not watching the other six hours.

For context on the broader stack: at the small-firm end, the common setup is a general assistant (Claude, ChatGPT, Gemini, or Copilot) for drafting, whatever AI shipped inside the practice-management system, and one or two specialist tools for workflows that hurt enough to justify a separate subscription. Evidence review is one of those. The pattern that works is narrower than the pitch decks suggest: pick one painful repetitive job, define exactly how you want it done, keep the lawyer in the decision seat — the same logic behind running discovery triage as an agentic workflow.

A calculator, not a benchmark

Don’t take anyone’s published hour-savings claim, including ours — there isn’t one here. The panel below contains no data; it’s blank inputs and a formula you fill in from your own files.

Input: H
Hours of media review avoided per case — measure it on your next three matters
Blank input, not a published figure
Input: C
Cases per year with heavy media
Blank input, not a published figure
H × C × R
Recovered capacity, where R is what an hour of your time is worth in new matters handled
Formula only — no values supplied

In a flat-fee practice the savings don’t show up as billables; they show up as capacity and as motions you can actually support. That reframes the income question people search around. Lawyers who reach high personal revenue in criminal defense generally get there through volume, referral flow, and leverage over associates and staff rather than by raising an hourly rate. On the figures that circulate online — rates around $900 an hour do appear in large-firm specialist billing, but we have not independently verified any such figure; check a published rate survey from your state bar or a commercial legal-market report rather than a forum thread. Either way, a headline rate tells you nothing about whether a firm is profitable. Our automation ROI model separates recovered hours from recovered revenue.

How to decide

If you handle a handful of media-heavy cases a year, buy the purpose-built tool or do nothing yet — a custom build won’t earn back its cost. If evidence review is the structural bottleneck, if your protective orders or your own risk tolerance push you toward keeping media in your own environment, or if you want output that drops into your suppression-motion template, the custom path starts making sense. Start with the vendor, measure for a quarter, and let the measurement — not the demo — decide whether you build.

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