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

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.

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
Read time10 minutes
AudienceManaging Partners

The firm profile.

Let's specify. 38 attorneys. $30M revenue, 60% litigation, 40% mixed corporate and employment. iManage Work for DMS. 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 diagnosis 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 is the median diagnosis call we run 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 5 minutes per day instead of the current 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 under NDA. Microsoft Graph for Outlook + Teams. iManage REST API 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.

Numbers at handoff: based on the audit pattern across audited firms in this size band, recovery in year one runs 40-60% of the modeled $1.1M-$1.6M leakage. So $440K-$960K in newly-billed work. At the firm's blended realization, that's roughly 8-19x payback on the $90K-$140K engagement fee, in year one alone.

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. Year-one and year-two associates spend 30-40% of their billable time on research that effectively reconstructs work the firm has already done in prior matters. 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 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.

Numbers at handoff: 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.

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.

At a 38-attorney firm with 60% litigation and 40% corporate/employment, inbound intake comes in 22-40 forms per week from web, phone, referral. Median time-to-first-touch from a prospect is currently 8-22 hours. The fast-responding firms (often the firm's actual competition for the better matters) are 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.

Numbers at handoff: 8-22% of qualified leads previously lost to slow intake, recaptured.

Where this ends up.

By month nine, the firm has three custom AI systems in production, running on iManage + Outlook + Teams. The systems are owned by the firm at handoff; maintenance is roughly $4,500/month across all three. Year one new revenue captured runs $1.2M-$2.4M against engagement spend of $250K-$370K plus $40K maintenance.

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 median trajectory at firms in this profile that ship the first build in 2026. It's not aspirational; it's the pattern at the firms that have done it.

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.

Field-note context

Where this argument fits in the practice.

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 a diagnosis call.

If the operator brings this argument to a diagnosis call, the next step is to translate it into the operator's specific business. The forty-five-minute call surfaces the constraint, names the workflow, identifies the integration boundary, and writes the engagement scope. Both sides leave with the constraint in a sentence. Either party can stop the conversation at no cost. 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.

Extended questions

The questions buyers ask after the first one.

How much of the buy decision should the operator make versus delegate.

The right shape of the buying motion has the operator-owner or operating partner in the room for the diagnosis call. The constraint identification is too consequential to delegate to a department head. The implementation work that follows can and should be delegated; the decision on which constraint a commission addresses cannot.

How to evaluate references the consulting house presents.

Three questions per reference. First, what was the named constraint the commission addressed at this operator. Second, what was the measured result twelve months post-handoff, in dollars or hours. Third, does the reference operator still run the system. Vague references on any of those three are flags. ColabContent provides direct introductions to past commission operators for any prospect that asks; a fifteen-minute call to the operator is the most honest signal a prospect can get.

How a fixed-fee commission scopes overage risk.

The fixed fee is set after the diagnosis call, after the integration depth is named, and after both sides have written the constraint in a sentence. Overages occur when the operator changes the scope mid-build (a different workflow, a different integration, an additional system). Either side can pause the build to renegotiate; neither side absorbs hidden overages without explicit agreement. The default is to ship the original scope and address scope expansion in a separate engagement.

What happens to the system one year after handoff.

The system continues to run inside the operator's cloud tenant. Models, prompts, and integration code are versioned and the operator has the source. When the underlying foundation model improves (a new release from the model vendor, a new open-weight option), the operator can swap the component without renegotiating the engagement. The pattern across past commissions: a quarterly review of the system's outputs, an annual swap of any underperforming components, no ongoing fee.

When the right call is not a commission.

The right call is sometimes a product (when the workflow matches a product's calibration target), sometimes an internal hire (when the operator has a five-year horizon and a $5M AI runway), sometimes a Big Four engagement (when the operator is large enough that the strategy-then-build separation makes sense), sometimes no AI right now (when the operator's leading constraint is not actually addressable with AI). We tell prospects when their constraint falls into one of those buckets and route them to whichever path fits. The four-commissions-per-quarter cap is real; the firms that get one of those four slots are the firms where the commission is the right buying motion.

The five-minute fit-check worksheet.

Operators who want to test the fit before booking a diagnosis call can run a five-minute self-check on six questions. First, is the operator's annual revenue in the $8M to $50M band. Second, is there a named workflow where time or money is leaking measurably. Third, has the operator tried an off-the-shelf product and either rejected it or hit a misfit ceiling. Fourth, is the operator comfortable running the system inside their own cloud tenant under NDA. Fifth, can the senior operator commit to forty-five minutes for a diagnosis call. Sixth, is the budget runway for a $45K to $180K fixed fee real this quarter.

Six yes answers means a diagnosis call is worth the forty-five minutes. Three or fewer yes answers means the right next step is probably one of the alternatives. Four or five yes answers means the call surfaces whether the missing one is addressable.

What to bring to the diagnosis call.

Two artifacts make the call substantially more productive. First, a one-page description of the leading constraint, written in the operator's words, naming the workflow and the rough dollar or hour leakage. Second, a list of the systems the operator uses for the workflow (the system of record, the related tools, the integration boundaries). Neither artifact has to be polished. The point is to surface the constraint quickly so the call's forty-five minutes are spent on diagnosis, not exposition.

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