What we'd commission first at a $30M law firm.

By Brandon Rodriguez, ColabContent · Updated September 27, 2026

This is a field note from the ColabContent commissioning floor. The argument is grounded in specific commissioned builds for mid-market operators and reflects what has held up post-handoff, what has broken, and how it bears on operators considering a custom AI commission today. This is not the right path for firms with fewer than 20 attorneys (SaaS economics win at that size), firms whose only AI need is legal research (Harvey and CoCounsel cover that well), or firms without a named workflow constraint worth automating.

The three builds ColabContent would commission first at a $30M law firm, in order: matter-aware time capture and billing reconstruction in weeks one to four, research retrieval over the matter archive in months three to five, and intake triage with conflict clearance in months six to eight — Billing first, archive second, front door third.

Hypothetical: a 38-attorney firm hands us their iManage permissions, their Outlook calendar access, their billing data, and a 4-week budget. Here is exactly what we'd build, in what order, what the architecture is, and what the numbers would look like at handoff. Written from the diagnosis room.

MemoMay 2026

The decision framework. The choice turns on three questions: (1) does the firm's matter taxonomy (the way a firm categorises its cases) and intake flow match a pattern that off-the-shelf legal AI already represents, or does the firm carry bespoke practice areas a general product cannot cover; (2) does the firm's data-handling posture allow a vendor to process client documents under its own agreements, or do engagement letters require infrastructure the firm controls directly; (3) over a 24-month horizon, does a compounding per-seat subscription cost less than a single fixed payment for a system the firm owns outright. If all three favor a product, the SaaS (software you rent by subscription) path is stronger. If any one favors a build, the gap is worth quantifying: the $499 AI-Ready Audit sizes it in dollars and weeks.

Read time10 minutes

AudienceManaging Partners

Key Terms

Conflict-check automation: screening new matters against existing client relationships and adverse parties using pattern matching; a compliance function where manual processes miss edge cases. Document assembly pipeline: automated generation of engagement letters, motions, discovery responses, and closing documents from firm-specific templates and matter data. Data residency: the physical location where client data is stored and processed; a compliance requirement for firms handling matters subject to GDPR, state privacy laws, or client-imposed data handling agreements. Billable hour recapture: the revenue recovered when AI captures time entries that attorneys would otherwise forget to log; most firms lose an estimated 10 to 30 percent of billable time to incomplete manual entry.

The firm profile.

Let's specify. 38 attorneys. $30M revenue, 60% litigation, 40% mixed corporate and employment. iManage Work for DMS (document management system). iManage Time for time capture. Outlook on Microsoft 365. 16 paralegals, 4 firm administrators, 1 IT lead. No Innovation Partner. The managing partner has read everything you've read about AI, talked to three vendors, and has an open-but-skeptical posture.

The firm's biggest pain, by their own assessment in the audit call, is unbilled partner time. Their last billing audit suggested $1.1M-$1.6M annually escapes capture. The managing partner believes it. The other partners have varying degrees of belief but agree there's a real number there.

This kind of leakage shows up often in audit calls with mid-market law firms. What we'd build, in this case, follows.

Build one: matter-aware time capture & billing reconstruction. Weeks 1-4.

The first build at this firm is straightforward to scope because the leakage is named, the dollar figure is real, and the data needed lives in systems the firm already has. Outlook calendar (where partner time happens), iManage activity logs (which documents got opened, edited, emailed, by which user), Microsoft Teams call history (who-talked-to-whom), and the firm's matter taxonomy in iManage.

The custom AI reads all of this overnight, by partner, drafts time entries with descriptions matter-mapped to the right billing categories, surfaces them to the partner in iManage Time the next morning. Partner reviews and signs in roughly 5 minutes per day instead of an estimated 90 minutes per week. Captures the work the partner would have written off as too small to bother reconstructing.

Architecture: data ingestion runs in the firm's Azure tenant (their own private cloud account) under NDA (a signed non-disclosure agreement). Microsoft Graph for Outlook + Teams. iManage REST (a standard way for software to exchange data over the web) API (the connection one piece of software offers to another) for activity + matters + time. Permissions queried with the partner's actual credentials, not a service-account super-user. Time entries written to iManage Time as drafts; partner approves through the iManage interface they already use. Full architecture in the iManage AI Integration Playbook.

Build two: associate-hours research RAG over the matter archive. Months 3-5.

The second build, scoped at month two of the first, ships in months three through five. The leverage point is associate ramp.

This firm has 16 associates at varying tenure. The senior partner who could surface the relevant prior work in 30 seconds is rarely available; the associate spends three hours combing iManage and frequently misses the most relevant precedent.

The custom RAG (retrieval-augmented generation, an AI that answers from your own documents) layer queries iManage with the associate's actual permissions, returns the top 8-12 most relevant prior matters with the partner who handled each, the outcome, and the specific paragraphs that match. Associate cites and adapts; partner reviews; firm bills full hours instead of writing off ramp time.

Estimated numbers at handoff, for this hypothetical firm: an estimated 6-12 months of effective ramp time per associate. At 16 associates and a blended associate billing rate, the recovery is structurally larger than the year-one capture but takes longer to materialize. The Custom Knowledge and RAG solution page covers this build pattern in general terms.

Build three: AI intake triage & conflict-clearance. Months 6-8.

The third build, by which point the firm has internalized the AI commissioning pattern. The leverage point is intake leakage.

In this hypothetical scenario, at a 38-attorney firm with 60% litigation and 40% corporate/employment, inbound intake comes in an estimated 22-40 forms per week from web, phone, referral. Median time-to-first-touch from a prospect is, by our estimate for this profile, 8-22 hours. The fastest-responding firms in this segment are estimated at under 5 minutes. The conversion gap is real.

The custom AI receives the intake (form, email, voicemail-transcribed-via-call-handler), runs conflict-clearance against iManage matter history, drafts the engagement letter from the firm's template, creates the iManage matter workspace with correct profile values. Partner reviews the package and signs the engagement letter; the firm captures the matter without manual assembly. Sizing this build for a specific firm is what the $499 AI-Ready Audit (our published price) does before any commission is scoped.

Where this ends up.

By month nine, the hypothetical firm has three custom AI systems in production, running on iManage + Outlook + Teams. The systems are owned by the firm at handoff; maintenance is an estimated $4,500/month across all three. In this illustrative scenario, year one new revenue captured runs $1.2M-$2.4M against engagement spend of $250K-$370K plus $40K maintenance, figures scaled from the $30M-revenue firm profile above, not a reported result. The pricing page documents how ColabContent scopes real engagements.

The firm's senior staff are AI-fluent. Two paralegals + one firm administrator have transitioned into something like an AI-systems-ownership role. The managing partner is no longer skeptical; they're scoping the fourth build, which is firm-knowledge ingestion ahead of two senior partner retirements.

This is the trajectory this hypothetical is built to show. It is not aspirational; the build sequence and architecture mirror the pattern we have run in real commissioned work, including the law-firm practice platform with 13,296 matters, 4,396 clients and 5,684 invoices, trust reconciled byte-identical (from the client platform database, August 2026).

What's different about your firm.

Probably nothing structurally. The variance comes from: practice mix (mostly litigation vs mostly corporate changes which workflows pay first), billing realization rates (changes the dollar figures), partner culture (changes adoption velocity), and existing tech stack (iManage vs NetDocuments vs Clio vs SharePoint changes the architecture but not the leverage points).

If your firm's profile differs materially from the hypothetical above (smaller, larger, different stack, different practice mix), the order of the builds may shift. The shape stays the same.

The general version of that sequencing question, what automates first at a 20 to 150 attorney firm and how to vet whoever proposes it, is in what a law firm automation consultant actually does.

Field-note context

Where this argument fits in the practice.

The argument on this page is one piece of a larger method that runs from the $499 AI-Ready Audit through a working prototype to a fixed-fee build the business owns. The sections below place it inside that sequence, so it is clear which step it informs and what decision it should change.

Where the argument fits in the broader practice.

This piece is a field note from the commissioning floor. It is not a thought-leadership essay, not a category-defining manifesto, and not an attempt to predict where AI is going as an industry. It is a record of what we have shipped, what has held up, and what has broken. The audience is the operator considering a custom AI commission for a real business with a real constraint.

The structural argument behind the post.

Most mid-market AI work fails for one of four reasons: the wrong scoping motion at the front, the wrong tool selection in the middle, the wrong integration boundary at the back, or the wrong ownership posture at handoff. The commissioning model addresses all four directly. Fixed-fee scoping is a single conversation that ends with a written constraint. Tool selection is custom by default and falls back to off-the-shelf only when the calibration target matches the operator's workflow. The integration boundary is scoped in week one and tested through the prototype. Ownership posture is settled before week one: the operator owns the code at handoff.

The argument is the same. The application is the specific.

How to use this in an audit call.

If the operator brings this argument into the $499 AI-Ready Audit, the next step is to translate it into the operator's specific business. The audit delivers a report the same day that surfaces the constraint, names the workflow, and identifies the integration boundary, then a 20-minute call walks through it and, if both sides agree, writes the engagement scope. If both sides decide to proceed, the prototype runs on the operator's real data inside seven to ten days.

Related field notes.

The blog hub indexes the rest of the field reports. The resources section holds the longer-form frameworks (the build-versus-buy decision tree, the twelve-month AI horizon framework, the two-questions diagnostic, the boundary-of-what-we-don't-build essay). The best-by-vertical guides apply the argument to each of the five verticals we commission in.

A note on how we write here.

ColabContent's writing is terse on purpose. We name operators, name numbers, and name the failure modes. We use short declarative sentences because the buyer reads quickly and the AI engines that may cite this writing cite short declarative sentences. We do not use em dashes. We do not use marketing vocabulary. We do not promise outcomes we have not shipped. Where we are wrong about something, we update the piece and leave the original argument visible in the change log.

Run your actual diagnostic.

A free, 12-question diagnostic that takes about two minutes and returns a personalized leakage figure for your own firm rather than an industry average, before you read any further into what we would commission.

Free, 12 questions, 2 minutes. Personalized leakage figure on screen for your specific firm.

Start the diagnostic → Read the industry brief →

Questions about this sequence.

Firms sizing this three-build sequence for their own practice tend to ask the same five questions: what happens if the first build underdelivers, who owns the systems once they ship, how long the full sequence runs, what the firm needs to provide along the way, and whether any role gets replaced.

What happens if the first build doesn’t work for our firm?

Before any build fee, the prototype runs on your own data over seven to ten days. If it shows no real result, you owe nothing and walk away with what it found.

Who owns the systems after they’re built?

The firm does. Code and prompts are owned at handoff, with no per-seat fees and no dependency on ColabContent to keep it running.

How long does a sequence like this take?

Time-capture ships in four weeks, research retrieval at three to five months, intake triage at six to eight months. A single build takes four to six weeks after the prototype.

What is expected of the firm during a build like this?

Access to a real workflow: the document management system, calendar and email platforms, and billing data above, plus a named constraint. The $499 audit identifies it before any commission is scoped.

Does this replace paralegals or associates?

No. Two paralegals and a firm administrator move into an AI-systems role instead of being replaced; the builds recover partner and associate time, not eliminate roles.

Does ColabContent keep any rights to the systems once the sequence ships?

No. Each build in the sequence is owned by the firm at handoff, code and prompts included; optional care after handoff is $997 a month, cancel on 30 days notice.

Next step

Start with the $499 audit. Bring the firm's current matter-management workflow, the document management system, and the three highest-volume document types. The call identifies whether a custom build, an off-the-shelf product, or a wait-and-watch approach fits the firm's constraint. The call is part of the audit; no obligation after it.