Your shop's number, in 90 seconds.
This free estimator is built for specialty manufacturers. Two minutes, your numbers on screen, no sales call unless you ask. It is a self-assessment rubric, not a benchmark study: every figure it returns is your own input run through the arithmetic and the assumptions stated below the result, so the diagnosis-call conversation that follows starts from your reality rather than someone else's average. Custom builds for this vertical are one fixed fee from $10,000, quoted after the $499 AI-Ready Audit, prototype before payment.
ColabContent LLC publishes this page: a boutique AI consulting house in Boston that builds custom automation for job shops and contract manufacturers of 20 to 250 employees. The $499 AI-Ready Audit comes first; after it, the system that matters most is proven as a working prototype on your own data before any build fee. Production builds are one fixed fee from $10,000, one time, with the code owned by the shop at handoff and no per-seat licence. The $499 AI-Ready Audit is ordered at colabcontent.com/ai-ready-audit/.
Enter honest back-of-envelope figures. No exact numbers required. Your answers stay in this browser.
This is addressable revenue, not cost savings.
Most manufacturing shops track cost: labor, materials, overhead. Almost nobody tracks the revenue they didn't earn because a bid went out after the buyer had already priced the job with someone faster. That figure rarely appears on a P&L, which is exactly why it goes unmanaged.
We are not going to tell you what the number is at a shop like yours. We have not shipped a commission for a specialty manufacturer yet, so we have no measured manufacturing result to quote, and we would rather say that than borrow someone else's average. What this tool does is give the number a shape from your own inputs so the audit call starts with a figure you can argue with instead of a brochure.
Where we do have measured results, they are in call handling and in platform work: more than 6,000 live calls handled across the practice, and a full matter, invoice and trust platform now running a law firm. Details are two sections down, with the caveats attached.
The $499 AI-Ready Audit comes back the same day, under NDA before the call. You leave with a written report regardless of whether we go further.
What a commission looks like for manufacturers.
What follows covers the buyer profile a manufacturing commission is built for, where dollars and hours typically leak in the operation, and the stack a build sits inside. It also covers how a commission compares to the alternatives, common misconceptions, regulatory notes for this vertical, the week by week shape of the engagement, and pricing.
The buyer profile, in one paragraph.
Specialty manufacturers in the $15M to $150M revenue band sit in the buying gap that defeats both off-the-shelf SaaS (software you rent by subscription) and Big Four consulting. The owner-ceo, president, or chief estimator has the budget to commission a custom system but not the in-house engineering bench to build one. The seat count is wrong for per-seat SaaS economics. The workflow is custom enough that a horizontal AI product ends up carrying a chunk of features the shop will never touch while missing the one step that actually holds the job up. This is the band ColabContent commissions builds in: fixed fee, working prototype on the operator's real data inside seven to ten days, code owned by the operator at handoff.
Where the dollars and hours leak.
For manufacturers the leakage concentrates in RFQ-to-quote, BOM (bill of materials) construction, production scheduling, shop-floor data capture, vendor RFQ, QC inspection. The pain points worth quantifying on an audit call are quote turnaround, estimator bandwidth, spec parsing accuracy, BOM lookup velocity. None of these are abstract. Each one shows up as a measurable number on the operator's monthly P&L or capacity plan once we look for it.
What we have actually shipped, stated plainly. No specialty manufacturer has commissioned a build from us yet, so there is no manufacturing win-rate number for us to hand you. The closest adjacent work is a regional third-party logistics operator whose inbound calls we handle, 211 of them to date. The rest of the measured record sits in other verticals: Jim Glaser Law, 3,787 AI-handled calls across 5,514 minutes, routed through five channel-specific voice agents (PPC, organic, TV, Meta, LSA) so every answered call carries its own channel attribution. A multi-location home services operator, 1,486 calls and 2,203 minutes. A realty firm, 148 calls. Two marketing agencies that quietly run their AI fulfilment through us. And a law firm whose matter, invoice and IOLTA (the client trust account a law firm must keep separate) trust system runs on a platform we commissioned: 13,296 matters, 4,396 clients, 5,684 invoices, trust reconciled byte-identical against the system it replaced. More than 6,000 live calls handled in total.
You should read that list for what it is. It says we can build and run a system that carries real operational volume and reconciles to the penny under audit. It does not say we have already solved RFQ-to-quote at a metals shop. If a manufacturing result is what you need before you spend money, the honest answer is that yours would be the first, and the prototype exists so you can see the work perform on your data before any invoice.
The stack the build sits inside.
Manufacturers typically run on some combination of Epicor Kinetic, JobBOSS, Global Shop Solutions, IQMS, Made2Manage. The commissioned system is built to integrate with the operator's actual stack, not to replace it. ColabContent does not sell a platform; we commission a custom layer that sits on, beside, or inside the existing systems and addresses the specific constraint the audit identified.
Integration depth varies by engagement. A read-only data layer that pulls structured records out of the existing system and writes nowhere is the lightest touch and the fastest to ship. A bidirectional integration that drafts records back into the system after human approval is the usual middle ground, and the one most shops end up asking about. A fully autonomous workflow that closes the loop end-to-end without human-in-the-loop review is the heaviest touch and is reserved for tasks where the failure cost is bounded and the audit trail is structured.
How a commission compares to the alternatives.
The manufacturers market has four real alternatives to a custom commission. Each has a buying pattern that fits a particular operator profile.
Off-the-shelf AI quoting and CPQ products. Strong fit for operators whose workflow matches the product's calibration target, which tends to be the larger end of the category. Per-seat or per-user pricing scales with headcount. The operator does not own the code or models. Strong on horizontal features (drafting, review, lookup); weak on operator-specific workflow. Get current pricing and a trial from the vendors directly rather than from us; we have no interest in characterizing a competitor's price for you.
Internal AI hires. Right answer for operators with the runway to fund a salaried team and the willingness to spend roughly a year building internal infrastructure before the first production workflow ships. The internal hire owns adoption, governance, and the next twelve months of evolution. A commission and an internal hire are not substitutes; the commission ships the first system, on schedule, while the internal hire builds the second.
Big Four consulting engagements. Right answer for large enterprises with enough stakeholders to justify a strategy engagement priced separately from the build that follows it. We do not publish their rate cards and will not guess at them; ask them for a scoped quote if you are considering that route. The structural point stands regardless of the figure: two sequential engagements is a heavier economic shape than most mid-market shops need.
Boutique commissioning houses (we are one). Right answer for the mid-market operator with a known constraint, a senior owner-operator decision-maker, and a posture of running the system inside the operator's own cloud tenant (a private cloud account) under NDA. Fixed-fee, prototype before payment, owned code at handoff.
Common misconceptions buyers walk in with.
Generic CPQ products work for engineer-to-order. Configure-price-quote tooling assumes a catalog of options with known rules. Engineer-to-order work re-derives the bid from a customer print every time, and the hard part is reading the print, not applying the rules. A CPQ product prices a variant well. It stalls where the estimator has to interpret a drawing, a tolerance callout, or a revision marked up by hand.
ERP (the system that runs finance, inventory and orders) vendor AI add-ons cover the same ground. The off-the-shelf products are excellent at one specific slice, usually the slice that lives inside the vendor's own data model. The operator-specific workflow that bridges that slice to the rest of the operation is what the commission addresses. The right comparison is not "product versus product"; it is "product as one layer in a larger custom system."
Spec parsing is a solved problem. Parsing a clean digital datasheet is close to solved. Parsing a real RFQ packet, with scanned prints, superseded revisions and a customer-specific tolerance table, is not, and any accuracy figure quoted at you should come with the document set it was measured on. The largest operators in the category run on stacks, workflows and budgets that do not port down. Their case studies are interesting; they are not predictive of a mid-market outcome. The right reference engagements are operators in your own revenue band, in the same vertical, with the same stack family.
AI replaces estimators. The design intent is the opposite. A senior estimator's judgment on price, risk and feasibility is the scarce asset; the administrative work of assembling the packet around that judgment is what a commissioned system takes over. The leverage shows up in the cost of the next dollar of revenue, not in a smaller payroll, and any operator whose plan depends on cutting the estimating bench should say so out loud on the audit call so we can tell them whether the math actually works.
Client data has to leave the building for this to work. It does not. Risk and confidentiality are addressed by where the system runs, what data crosses the boundary, and what model selection is allowed. The build runs inside the operator's own cloud tenant under NDA. Client data does not leave that environment. Model selection (open-weight, closed-weight, mix) is part of the diagnosis and constrained by the operator's confidentiality posture, which matters more than usual when the drawings are ITAR-controlled.
Regulatory and compliance notes for this vertical.
The commission accounts for the regulatory environment of manufacturers from the audit onward. ITAR/EAR for defense work; AS9100 for aerospace; ISO 9001 quality systems; supplier-specific portal requirements. We do not commission systems that put the operator on the wrong side of a regulator or a state board. Where the right move is no AI, we say so and the engagement does not proceed.
What the engagement looks like, week by week.
Week 0. The $499 AI-Ready Audit. Both sides leave with the constraint written down in a sentence. Either party can stop here at no cost.
Week 1. NDA signed, representative data slice provided. Prototype begins on the operator's real data, not synthetic. Senior hands are on the keyboard from day one.
Day 7-10. Working prototype ships. The operator sees the system actually perform the constraint task on real data before any payment changes hands. If the prototype does not perform to the target written down after the audit, the operator owes nothing and keeps the work product.
Weeks 2 through 7. Production build runs. Standard cycle 6 to 7 weeks. The same senior people who ran the diagnosis run the build. There are no account managers, no junior staff running the build, no offshore hand-offs.
Handoff week. Code, prompts, models, datasets, runbook (the written operating instructions), and integration documentation transfer to the operator. The system is owned by the operator at handoff. Optional care after handoff is $997 a month and cancels on 30 days notice.
Pricing for this vertical.
Fixed-fee custom builds from $10,000, scoped against the constraint the $499 AI-Ready Audit identified and the integration depth required. There is no per-seat pricing, no proprietary runtime to license, no annual renewal. The fee is paid in two installments: one at production-build start (after the prototype works), one at handoff.
Operators considering the work typically compare it against the all-in cost of one of the four alternatives above. The math that wins is not "lower than" but "owned at the end." A SaaS subscription compounds. A custom commission is paid once.
Further reading inside the site.
The questions buyers ask after the first one.
These are the questions that come up once the first one, whether to build at all, has been answered. Each answer below is the one we give on the call that ends the $499 AI-Ready Audit, written down here so it can be checked against your own report before anything is commissioned.
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. Apply them to us first. Our nameable reference is Jim Glaser Law, where five channel-specific voice agents have handled 3,787 calls across 5,514 minutes; Jimmy will take a reference call and has referred work to us. The other operators we serve, including the litigation firm running its matters, invoices and IOLTA (the client trust account a law firm must keep separate) trust on a platform we commissioned, are under confidentiality and stay anonymized here. We do not have a manufacturing reference to offer, and we will say that on the call rather than around it. A fifteen-minute call to a live operator is the most honest signal a prospect can get, so ask for it.
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 multi-year horizon and the payroll runway to fund a team), 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. Commission capacity is deliberately limited, because the same senior people run every build; the shops that get a slot are the ones where a commission is genuinely the right buying motion.
Six yes answers means the $499 AI-Ready Audit is worth ordering. 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.
Start with the $499 AI-Ready Audit.
Report the same day, a 5-minute video walkthrough and a 20-minute call with Brandon, under NDA. Every finding priced and ranked. Full money-back guarantee if you did not get value.