AI Discovery Responses: Briefpoint vs CoCounsel vs Custom

By Jude Lee · August 31, 2026 · Comparison

Two lawyers at a small firm reviewing interrogatory responses on paper and laptop

The job nobody wants: turning a served request set into a defensible draft

A set of 35 interrogatories and 60 requests for production arrives. Someone has to build the response shell, restate each request, insert general and specific objections, pull the client’s prior verified answers on overlapping topics, flag which requests need a document collection, and get it out the door inside the response window. Under the Federal Rules of Civil Procedure, that window is 30 days after service for interrogatories (Rule 33(b)(2)) and for requests for production (Rule 34(b)(2)(A)) — state and local rules vary, so confirm the deadline against the governing rule and the court’s own standing orders rather than a default in your calendaring tool.

The drafting itself is mostly pattern work. That is exactly why it is worth automating, and exactly why it is dangerous to automate carelessly.

30 days

Federal default to respond to interrogatories and RFPs

Fed. R. Civ. P. 33(b)(2), 34(b)(2)(A)

Rule 26(g)

Attorney signature certifying reasonable inquiry into discovery responses and objections

Fed. R. Civ. P. 26(g)

Formal Op. 512

ABA guidance on lawyers' duties when using generative AI

American Bar Association, 2024

Platform announcements don’t decide this for you

Every quarter brings another agentic legal platform announcement from a major cloud provider, document management vendor, or practice management suite. Treat those as infrastructure news, not as answers. A platform gives you somewhere to run agents; it does not give you your firm’s objection library, your client’s verified prior answers, or your judge’s tolerance for boilerplate. Before you assume a headline applies to you, read the vendor’s own product documentation for what is actually generally available versus in early access. (Accurate as of 2026; this category changes fast.)

Path one: buy a tool built for this one job

A small category of vendors builds specifically around this task — ingest a served request set, produce a formatted response shell with the requests restated and objection language inserted, and hand you an editable draft. Briefpoint is the name small-firm litigators mention most often, though we have not independently tested it, and feature sets in this category change quarterly.

So evaluate it against a checklist rather than a demo. From the vendor’s own documentation, confirm: what file formats it ingests and exports; whether it writes back into your document management or practice management system or stops at a download; which jurisdictions and courts it claims format coverage for, and what happens outside them; whether your objection library can be customized or only selected from; and what its data retention and model-training posture is.

The case for buying is speed. No build, no prompt engineering, no integration project. If you run high-volume defense or PI work in one or two jurisdictions with fairly consistent request sets, this is the shortest path from “annoying” to “handled.” The limit is that you get the vendor’s structure, not yours — and deep pulls from your own matter history usually are not part of the deal. You are also adding another subscription to a stack that is often more expensive than anyone tracks.

Path two: use a broad legal AI assistant

CoCounsel (Thomson Reuters), Harvey, and general assistants like Claude or Copilot come at it from the other direction: a capable generalist you point at a discovery task among many others. Give one a request set, your standing objection language, and a few exemplar past responses, and it will produce a credible first draft.

The advantage is coverage. The same tool that drafts your responses also summarizes a deposition, outlines a motion, and reviews an incoming production. If discovery drafting is 10% of your AI need rather than 60%, a single-purpose subscription is hard to justify. We compare the enterprise end of this market in Harvey vs CoCounsel vs a custom AI agent.

The disadvantage is consistency, and it fails hardest in two specific places: jurisdiction-specific caption and formatting drift, and silent template variance from one set to the next — the numbering, preamble, or objection phrasing shifts slightly and nobody notices until opposing counsel does. That is what skills are for: reusable, packaged instructions that teach an assistant to do one job the same way every time, including your exact objection phrasing, your caption format, and your rule that no substantive response gets drafted without a citation to a source document. The same discipline we describe for repeatable document review applies here.

Path three: build a custom agent wired into your systems

The custom path uses an assistant like Claude connected to your actual systems through MCP — the Model Context Protocol, an open standard for giving an AI governed, permissioned access to your tools and data. Practically, the agent can read the matter record in Clio or your DMS, retrieve the client’s prior verified interrogatory answers, check what has already been produced, draft the response set into your template, and save it back to the matter folder for attorney review. For the mechanics, start with connecting Claude to Clio via MCP, and pair it with the discovery triage playbook.

This is a build. It costs real money and someone has to own it. It makes sense when your objection library is a genuine asset, when the request sets you receive are too idiosyncratic for a packaged tool, or when retrieval — not drafting — is the bottleneck. If pulling prior verified answers drops from a half-hour dig through closed matters to a single query, that changes the shape of the work; if it doesn’t, the build is hard to justify.

The short version of the decision: buy purpose-built if your volume is high and uniform and you don’t need your own files; buy general if discovery drafting is one of many AI jobs and you’re willing to write skills to hold the format steady; build custom if your prior answers and objection library are the thing that makes the draft good.

Buy purpose-built

Fastest to value, fixed monthly cost, vendor maintains jurisdiction formats. You inherit their structure. Middle option — a general assistant plus a written skill — sits between these: low cost, broad coverage, but you own consistency yourself.

Build custom

Slower and costlier up front. Uses your objection language and your prior verified answers, and can file the draft back into the matter. Justified when discovery drafting is core volume and your DMS holds real reusable content.

What has to stay human, regardless of the tool

Under Rule 26(g), an attorney signs discovery responses and objections, certifying after reasonable inquiry that they are consistent with the rules and not interposed for improper purpose. Interrogatory answers themselves are verified by the party under oath (Rule 33(b)(3), (b)(5)). No agent absorbs that. The ABA’s Formal Opinion 512 addresses competence, confidentiality, and supervision when using generative AI — read it alongside your own state bar’s opinions, which may go further.

Before any served request set or client document leaves your systems, get written answers on the confidentiality mechanics: how long inputs and outputs are retained and whether you can force deletion; whether your data is used to train or fine-tune models, and whether that setting is off by default or off only on request; which subprocessors and model providers touch the data and where it is hosted; whether a business associate–style data processing agreement is available; and what admin controls exist for logging and access. Confirm the answers against the vendor’s own documentation, and check the arrangement with a qualified legal professional and your malpractice carrier before adopting it firm-wide.

Operationally, three hard stops: the agent never asserts a factual response the client has not confirmed, every substantive answer carries a citation to its source document, and no draft leaves the building without a named attorney reading it line by line.

Automating the shell is safe and boring. Automating the substance is where firms get in trouble — and the tools rarely make that line visible for you.

Automate the boilerplate majority, not the contested minority

In most request sets, a minority of requests carry the real dispute; the rest are structural, duplicative, or boilerplate-objectionable. The right target for automation is that boilerplate majority. The contested minority is where a partner should spend the time the automation gave back. Any pitch that inverts that — automating judgment and leaving you the formatting — is selling the wrong thing.

Modeling the payback without fooling yourself

Skip the vendor ROI headline and build your own with numbers you can defend:

Count the real volume

Pull the last six months. How many request sets did the firm respond to? Not “about” — actual count.
Time the current process

Have someone log a full cycle: shell build, objection insertion, prior-answer retrieval, review rounds. Use the median, not the worst case.
Estimate the honest reduction

Assume the agent handles the shell and boilerplate only, then add back review time. Plug in your own percentage and be conservative.
Price the hours at the right rate

Hours saved × the loaded hourly cost of whoever actually does this work — paralegal or associate, not partner. If those hours flow to billable work, count them at the billing rate; if they only reduce overtime, count them at cost. Those are different numbers.
Subtract everything

Subscription or build cost, plus the maintenance hours someone will spend keeping templates and skills current. That line is real and routinely omitted.

The number that decides this is the loaded hourly cost of the person currently building response shells at 9pm — and whether the hours you free up get reallocated to billable work or simply evaporate. Our automation ROI walkthrough covers that reallocation trap in more depth.

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