Bespoke AI Systems.
Bespoke AI Systems for mid-market operators ($8M-$50M revenue) delivered as a custom commissioned build. ColabContent commissions custom bespoke ai systems at fixed fee ($45,000 to $180,000), integrated with the operator's actual stack, with code owned at handoff. The system runs inside the operator's own cloud tenant under NDA. Prototype on real data within 7 to 10 days before payment.
Predictive pricing, forecasting, novel agent workflows, internal copilots, audit systems, if it's a real business problem with a real budget, we scope it and build it.
"When the leverage doesn't fit a category."
The symptoms we hear before the call.
- 01You've looked at the market and there's no product for this
- 02The problem is specific enough that no vendor understands it
- 03A custom system would pay for itself in under 12 months
- 04You want something built for you, not sold to you
How we approach it.
Every engagement is scoped individually against your operation. The four phases below describe the shape. The specifics are yours.
Numbers we typically see.
Drawn from the last eighteen months of engagements in this category. Your mileage will vary with your baseline, your team, and your data, but the direction holds.
Predictive pricing · +8-14% margin
Outcomes by category
Error rate down 30-60%
Forecasting
Net-new capabilities, per engagement
Agent workflows
Typically 6-14 months
ROI horizon
Other systems we commission.
Most engagements touch two of our five solution categories. If more than one of these sounds like you, say so on the diagnosis call, we'll tell you honestly where the leverage is.
How a Bespoke Ai build lands.
What Bespoke Ai commissions look like.
Bespoke Ai is one of the five solution categories we commission against. The category is named for the workflow shape, not the underlying technology. A Bespoke Ai build can use one model or many, can run on open-weight or closed-weight foundations, and can sit on top of any of the operator's existing systems. The defining characteristic of the category is the workflow shape, not the implementation detail.
Where Bespoke Ai fits inside the operator.
Bespoke Ai systems sit at a specific layer of the operator's stack: between the system of record where structured data lives and the human reviewer who approves the resulting action. The AI layer reads structured records, runs the workflow it was commissioned to run, and produces a suggested action that the human reviewer either approves, modifies, or rejects.
The boundary between the AI layer and the human reviewer is scoped in the diagnosis call. The diagnosis identifies which decisions the AI layer is allowed to make autonomously, which require human approval, and which are out of scope entirely. The scoping holds for the life of the build.
Why mid-market operators commission Bespoke Ai rather than buying a product.
Off-the-shelf Bespoke Ai products exist. They are calibrated against the average customer in the category, which by definition is the larger operator. The mid-market operator's workflow is not the average. A commissioned Bespoke Ai build addresses the operator-specific workflow that the product cannot represent.
The trade-off is up-front cost versus ongoing subscription. The commissioned build is a one-time fixed fee in the $45K to $180K band. The product is a per-seat subscription that compounds. For operators with a five-to-ten-year horizon on the workflow, the math favors the commission.
The build cycle for Bespoke Ai.
A Bespoke Ai commission runs four to seven weeks from production-build start to handoff, depending on integration depth and workflow complexity. Before the production build begins, the prototype runs on the operator's real data inside seven to ten days. The operator sees the system actually work on real data before any payment changes hands.
The build is led by a ColabContent principal. There are no account managers, no junior staff running the engagement, and no offshore hand-offs. The operator works directly with the principal who scoped the engagement.
Common pitfalls in Bespoke Ai commissions.
Scoping too broad. Bespoke Ai is a category. The commission addresses one workflow inside the category, not all of them. Operators who scope a Bespoke Ai build to address every workflow in the category never converge. The commission scopes one workflow.
Skipping the diagnosis call. Operators who arrive with a pre-written specification for a Bespoke Ai build often miss the actual constraint. The diagnosis surfaces the constraint by asking what costs the most time and what costs the most money. The two answers are usually different. The leverage almost always sits at one of them.
Treating Bespoke Ai as a product purchase. The commission is a build, not a product. The operator owns the code at handoff. The vendor relationship ends at handoff (or continues optionally as small, transparent, droppable stewardship). Operators that approach the commission as if it were a product purchase end up disappointed by the ownership posture and over-paying for a product they could have bought instead.
The other solution categories we commission against.
The solutions hub indexes the five categories: revenue operations AI, content operations AI, workflow automation, knowledge and RAG, and bespoke AI systems. Each one names the workflow shape it addresses. The diagnosis call is where the operator and ColabContent decide which category the commission falls into.
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.
How this solution category fits into the engagement model.
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.
Book the 45-minute diagnosis.
No pitch. No fee. A written map of the two line items bleeding your business.