Custom AI systems. Built before you pay.

Why mid-market AI work is different from enterprise AI work.

An AmLaw 100 firm, a Big Four accounting firm, a top-100 national agency, a Fortune 500 manufacturer: each of these has an in-house innovation team, a CIO with a discretionary budget, and the seat count to make a horizontal SaaS platform make sense. A $20M law firm, a $40M specialty manufacturer, a $25M regional insurance agency, a $30M PE-backed home services platform: each of these has the budget to commission a custom system but not the in-house engineering bench to build one, and not the seat count to make per-seat SaaS pricing pencil. Mid-market AI is a separate buying motion with separate failure modes and separate winners.

The failure modes look like this. Off-the-shelf AI is calibrated against the average customer in the category, which by definition is the larger firm. The mid-market operator pays per-seat for features the firm does not use and loses thirty to forty percent of the platform's value to misfit on the workflows that matter. The internal AI hire spends the first six months building infrastructure that consultants ship in the first two weeks, and twelve months in is still one quarter away from a production system. The Big Four consulting deck runs $400K to $1.4M, ships a strategy document, and leaves the build to a third party that the firm then has to manage. None of those motions are wrong; they are wrong for this revenue band.

The custom commission addresses the gap directly. A boutique principal scopes the constraint, builds a working prototype on the operator's real data inside seven to ten days, and ships the production system in four to seven weeks at a fixed fee. The code, prompts, and models transfer to the operator at handoff. The system runs inside the operator's own cloud tenant under NDA. There is no per-seat pricing, no proprietary runtime, no vendor lock-in.

The five verticals where the math works.

We commission builds in five verticals because the buying band is right, the workflows are bespoke enough to defeat horizontal SaaS, and the dollar volume of the leakage justifies a fixed-fee commission. Each vertical has a different constraint and a different recovered-dollar profile.

Mid-market law firms. Twenty to one hundred fifty attorneys. The leakage is unbilled partner time, mis-routed intake, and timesheet reconstruction loss. $1.4M of unbilled partner time recovered in the first quarter post-handoff at a 47-attorney firm. Build cycle five to seven weeks. Compared in depth on our best AI consultants for mid-market law firms guide.

Specialty manufacturers. $15M to $150M revenue. The leakage is quote turnaround time. Quote turnaround compressed at a $34M custom metals shop from six hours to eleven minutes, win rate up twenty-two percent on speed alone. Build cycle seven weeks. Compared in depth on our best AI consultants for specialty manufacturers guide.

Regional P&C insurance agencies. $10M to $50M commission revenue. The leakage is COI turnaround and submission processing. Top-quartile agencies clear COIs in eleven minutes; middle-quartile takes eighteen hours, and the gap costs twelve percent of commission revenue year over year. Compared in depth on our best AI consultants for insurance agencies guide.

Mid-market CPA firms. Thirty to one hundred fifty professionals. The leakage is partner time on PBC reconciliation, season-driven workflow chaos, and CCH Axcess or UltraTax data plumbing. Partner-to-PBC ratios from one-to-four to one-to-eleven at a 75-partner firm, roughly 7,000 chargeable hours recovered over a single tax season. Compared in depth on our best AI consultants for CPA firms guide.

PE-backed home services platforms. $20M to $100M revenue. The leakage is call abandonment and dispatch friction across multiple brands and field service systems. EBITDA improvement that translates directly into exit-multiple lift in the sponsor's investment thesis. Build cycle four to six weeks. Compared in depth on our best AI consultants for PE-backed home services guide.

How the prototype-before-pay structure works.

A commission starts with a forty-five-minute diagnosis call. The principal listens for the constraint, asks where the dollars or hours are leaking, and writes the constraint down in a sentence. Both sides leave with that sentence. Either party can stop the conversation here at no cost.

If both sides decide to proceed, we sign a one-page NDA and the firm provides a representative slice of real data: ten anonymized matters, a hundred recent RFQs, a quarter of COI requests, a season of PBC packages, a month of dispatch logs. Inside seven to ten days we ship back a working prototype that performs the constraint task on that real data. The firm sees the system actually work before any payment changes hands. If the prototype does not perform to the diagnosis spec, the firm owes us nothing and keeps the work product.

If the prototype performs, the fixed-fee commission begins. The fee is scoped against the constraint, the integrations required, and the handoff posture, and it sits in the $45,000 to $180,000 band. Build runs four to seven weeks. The system ships inside the firm's own Azure, AWS, or Google tenant under NDA. The firm receives the code, the prompts, the models, the datasets, and the runbook. The firm owns the system. There is no proprietary runtime to license and no per-seat fee to renew.

Post-handoff stewardship is optional, small, transparent, and droppable on thirty days notice. We do not disguise a retainer as a subscription.

What we will not commission.

We will not run engagements for AmLaw 100 firms, Big Four accounting firms, or top-100 national P&C agencies. Those firms have in-house innovation teams that are the right answer for them, and our four-commissions-per-quarter cap is better spent on operators who do not have that internal bench.

We will not commission a per-seat SaaS subscription product. We are a custom build house. The deliverable is owned by the operator at handoff.

We will not run a strategy engagement that does not end with a build. A roadmap without a system is a different category of work. We will refer to firms that do strategy-only work well, where appropriate.

We will not exceed four commissions per quarter. Past four engagements per quarter, partner-level engagement degrades and the work degrades with it. We turn away qualified prospects rather than overbook.

What buyers ask before commissioning.

Is the system replacing staff? No. Across every engagement to date the pattern has held: firms reclaim senior capacity, then choose to grow into the recaptured capacity rather than reduce headcount.

What if our data is messy? Messy is the baseline assumption. The prototype is built on real, unsanitized data from the firm. Cleaning is part of the build, not a prerequisite for it.

What if we already have an internal AI hire? A commission works alongside internal AI capability rather than in place of it. The internal hire owns adoption, governance, and the next twelve months of evolution. The commission ships the first system, on schedule.

What about model risk and confidentiality? The build runs inside the firm's own cloud tenant under NDA. Client data does not leave that environment.

What if the AI category changes again in six months? The build is owned by the firm. When a better model or technique appears, the firm replaces the relevant component without renegotiating a vendor agreement. That is the structural advantage of a commission over a SaaS subscription.

Two questions, in writing, before any commission.

Every commission begins with two questions, asked of the senior operator, answered in writing in the diagnosis call: What costs you the most time? and What costs you the most money? The two answers are usually different. The leverage almost always sits at one of them. The diagnosis is the work of finding which one and writing down the exact constraint that the commission will address.

The forty-five minutes is free. The diagnosis document is the firm's to keep regardless of whether the commission proceeds. We will tell the firm honestly if the right next step is a different consulting house, an internal hire, an off-the-shelf product, or no action at all.

The four-question sequence operators run before booking.

Operators who arrive at a diagnosis call having run the sequence usually book the engagement that same week. The sequence asks four questions in a specific order. First, is the leading constraint actually addressable with AI, or is it a process problem, a staffing problem, or a stack problem that AI would not solve. Second, if AI is the right intervention, is the right buying motion a custom commission, an off-the-shelf product, or an internal hire. Third, if the right motion is a commission, is the operator comfortable running the system inside their own cloud tenant under NDA and owning the code at handoff. Fourth, is the budget runway for a $45K to $180K fixed fee real this quarter.

Operators who answer yes to all four book the call. Operators who answer no to any one of them either change the question (the leading constraint is different, the budget moves, the cloud posture changes) or take a different path. We do not push operators who land at a "no" on any of the four into a commission they will not be served by.

The three signals operators watch for after handoff.

Twelve months post-handoff, three signals tell the operator whether the commission performed against the diagnosis spec. First, the dollar or hour delta on the workflow the commission addressed, measured against the pre-engagement baseline. Second, the percentage of the workflow the AI layer now handles autonomously versus the percentage that still routes to a human reviewer. Third, the number of times the operator's team has modified the build's prompts, models, or integration code on their own without ColabContent involvement. All three should be improving over time. If they are not, the optional small post-handoff stewardship is the lever for diagnosing what changed.

The honest comparison against the alternatives.

A commission is not the right answer for every operator. The mid-market operator with a workflow that matches a horizontal SaaS product's calibration target is better served by the product. The operator with a five-to-ten-year horizon, a $5M AI investment runway, and the willingness to spend twelve months building infrastructure before shipping the first production workflow is better served by an internal hire. The operator at $500M-plus revenue with stakeholder counts that justify a Big Four engagement is better served by that motion. We will tell the operator which of those alternatives fits if a commission does not.

The honest case for a commission is narrow on purpose. Operators in the $8M to $50M revenue band, with a named workflow constraint, with stack systems that the product market does not represent well, with the budget runway for the fixed fee, with the cloud posture to run the system inside their own tenant. Operators in that narrow band are where the math works.

Why we publish the comparisons, the rankings, and the boundaries.

Most consulting houses do not publish ranked comparisons against their competitors, do not publish the boundary of what they will not build, and do not publish fixed-fee pricing bands. We publish all three because the operators we want to commission for are the operators who reward that transparency with a faster booking. The four-commissions-per-quarter cap means we are not optimizing for top-of-funnel volume. We are optimizing for the right four operators each quarter. Publishing the comparisons, the rankings, and the boundaries selects for those operators.