Build, buy, or commission: a decision framework

The frameworks on this section of the site are the same ones we use to scope a commission. They are not consulting frameworks borrowed from somebody else and rewrapped. They are the artifacts of having shipped enough commissions to converge on a few decision patterns that hold up under pressure.

Each framework is meant to be picked up and applied. We will walk an operator through any of them on a diagnosis call. The call is free. The frameworks are free. The artifacts the operator leaves the call with are owned by the operator. The commission only begins if the operator and ColabContent both decide to proceed.

Every mid-market AI buying decision runs through three layers. The first layer is "is this the right problem to solve right now," which is the two-questions framework and the twelve-month-horizon framework together. The second layer is "what is the right buying motion for this specific problem," which is the build-versus-buy commission framework and the what-we-don't-build boundary essay. The third layer is "what is the right vendor for the chosen motion," which is the best-by-vertical guides and the comparison pages.

This framework belongs to one of those three layers. The other frameworks are linked below for the operator running the full sequence.

Skipping the constraint identification. The framework only works once the constraint is written down. Operators that try to apply the framework to "general AI strategy" never converge. The framework is applied to one specific named constraint at a time.

Applying it to the wrong layer of the decision. A framework meant to surface buying motion will not help an operator who has not yet decided that the problem is worth solving. A framework meant to choose a vendor will not help an operator who has not yet decided whether the right answer is build or buy.

Treating it as a one-time exercise. The frameworks are meant to be re-applied as the operator's situation changes. The twelve-month-horizon framework in particular gets re-run quarterly.

Many operators leave a diagnosis call having applied the framework and concluded that the right answer is no AI right now. We tell operators when that is the right answer. The commissioning house economics work for us only when the operator has a real constraint that a custom AI build can address. Operators without that constraint are better off without an engagement.

The honest "no" outcome is the most common single outcome of a diagnosis call. We turn away more operators than we accept. The four-commissions-per-quarter cap means we cannot do otherwise.

The two-questions framework is the entry point to any diagnosis: what costs the most time, and what costs the most money. The build-versus-buy commission piece is the framework for deciding the buying motion. The twelve-month horizon is the framework for sequencing investments quarter by quarter. The what we don't build essay is the boundary statement, the work we will not commission. The AI isn't tooling piece is the structural argument for why AI investments fail at the tooling layer.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.