Have you done this for a manufacturer before?

Not yet. Our delivered work is in construction, where we built a custom Procore application inside CJM Construction in South Florida. We will tell you plainly what has been proven and what has not, and every engineer in the network follows the same process.

Will AI touch our machines or control systems?

Not in anything we propose. None of the services on this page controls equipment, edits machine programs or setpoints, or makes a safety decision; they use business-side records and exports. Whether a project would ever need live plant data, and how, is decided with your IT and OT owners during onboarding. The CISA joint guidance encourages pushing data out of the control network and avoiding persistent access into it.

Do we have to replace our ERP?

No. We build around the systems you already run, spreadsheets included. What we can connect to depends on what your ERP and other vendors allow, whether that is an API, scheduled exports, or reports, and on what your IT team approves. We confirm that during onboarding before we commit to building anything.

How much will this save us?

We will not promise a number. Published survey figures on AI in manufacturing are self-reported and vary by process; none of them is a forecast for your shop. We measure a baseline during onboarding, setting up the measure first if you do not track it yet, and report against it.

Is this about replacing people on the floor?

No. What we build drafts, organizes, and checks; your people decide. Capturing what experienced people know only works with their help, so they choose what to share, review what is written, and approve it before anyone else relies on it.

Can our team paste customer drawings into a public chatbot?

Not before someone has checked that tool's terms against your customer agreements. If your shop handles export-controlled technical data, your compliance owner decides what may enter any tool; that call is theirs, and we make no legal or compliance guarantees. The CISA joint guidance on AI in operational technology advises minimizing risk by not sharing sensitive data with AI models, especially ones hosted by external parties such as public cloud services. We can help your compliance owner write the policy and choose an approved tool so your team has somewhere appropriate to work.

INDUSTRIES / MANUFACTURING

AI for manufacturers, with your people running the floor

Engineering for manufacturers: the quoting, documentation, quality, and planning work that surrounds production. Our delivered work so far is in construction, and we do not claim a specialty in any one kind of manufacturing. Below is what we would build, where AI stops on the plant floor, and what to ask of anyone who wants your production data.

Book a call

What we would build with a manufacturer

These are proposed services. We have not yet delivered them for a manufacturing client. Each one starts with onboarding, where we find out whether it is worth building for your operation at all.

Quotes and orders

Quote requests checked for what is missing: Requests for quote arrive as emails, drawings, and spec sheets. The tool gathers material, quantities, tolerances, finishes, required certifications, and the due date into one checklist and flags what is missing or unclear, so the estimator asks the customer before quoting. The estimator confirms every field.
Quote math in code, rates from your own records: Quotes built on your own rates and past jobs, with the arithmetic done by software. AI can find comparable past jobs and draft the cover letter; it never supplies a price. The estimator approves every quote before it goes out.
Revisions compared before they reach the floor: A new drawing revision or purchase order compared against the one you quoted, with each change listed for review: dimensions, quantities, materials, notes, and dates. Changes that could affect price or schedule are flagged before work starts.

Knowledge on the floor

Answers from your own work instructions: Work instructions, procedures, manuals, and quality documents answered in plain language, with a link to the page and revision each answer came from. When the documents do not cover a question, the tool says so and names the person who owns the procedure.
What senior people know, written down with them: Recorded walkthroughs and interviews with experienced operators, setup people, and technicians turned into draft instructions. The person who knows the job corrects the draft, and your own document control approves it before it becomes an instruction.
Maintenance history you can search: Work orders, logs, and technician notes searchable by machine and symptom: what failed last time, what fixed it, and which parts were used. Every suggestion links to the records behind it. Technicians decide what to do, and nothing we build changes a machine setting.

Quality, planning, and reporting

Quality records drafted for the people who sign them: Nonconformance reports, corrective action drafts, and replies to customer complaints drafted from your inspection records and notes. Software checks recorded values against the limits you set and flags missing fields. Your quality staff decide every disposition and sign every record.
Schedule options with their reasons: Open orders, due dates, material on hand, and capacity, as your systems record them, in one view. When something changes, the tool lays out the options and what each one would push back. The planner makes the schedule.
Daily reports from data you already keep: Shift and production summaries built from the exports and spreadsheets you already keep. Totals are computed in code; AI writes the summary and points out gaps in the data and leaves them unfilled.

How a manufacturer starts

Every client follows the same process. After a free sales call, you pay a fixed $10,000 for onboarding. We write a questionnaire for your company. On the onboarding call, estimators, planners, supervisors, quality staff, and maintenance technicians each walk us through a normal day, on the floor and in the office. We map where the hours go and which measures you already track, confirm what your ERP and other systems allow and what your IT and OT owners will approve, and deliver an ontology within 72 hours for one workflow worth building first. We then present recommendations. Certified engineers build inside the systems you already run, under a separate contract billed monthly, and we measure the result against the baseline we took. More on how we work.

Where AI stops on the plant floor

AI reads, sorts, drafts, and flags what is missing. Software applies the rules and does the arithmetic. Your people approve every quote, schedule, quality disposition, and work instruction before anyone relies on it. None of the work proposed here controls equipment, changes machine programs or setpoints, bypasses a safety system, or makes a safety decision. It is the same split as our construction tools, where AI never supplies the prices.

Government security agencies draw the same line. Joint guidance from CISA, the NSA, the FBI, and cybersecurity agencies in six other countries says large language models "almost certainly should not be used to make safety decisions" in operational technology (OT) environments, advises limiting AI's active control of OT without a human in the loop, and states: "Ultimately, humans are responsible for functional safety." The guidance is written for critical infrastructure owners and operators.

Source: CISA and partner agencies, Principles for the Secure Integration of AI in Operational Technology (December 2025)

Plant network and data

The proposed work runs on business records: Every service proposed above works from business-side records: quotes, orders, drawings, documents, and the exports and reports your systems already produce. The CISA joint guidance notes that large language models are typically used at the business level, potentially on data exported from the OT network. Whether a project would need any plant-floor data, and how it would get there, is for your IT and OT owners to decide, and it is settled during onboarding before anything is built. For that conversation, the guidance encourages architectures that push data out of the OT network and avoid persistent access into it. It is a fair question to put to any vendor, us included.
Source: CISA and partner agencies, Principles for the Secure Integration of AI in Operational Technology (December 2025)
Knowing where production data goes: The same guidance advises knowing where OT data used for AI is stored, keeping it within your organization's control, and setting a data usage policy, noting that operators may not want vendors to train AI on operational data because it can include intellectual property. We map who can see what, where data is stored, and for how long, so your team can make that call before anything is built. You own the code we write and the infrastructure it runs on.
Source: CISA and partner agencies, Principles for the Secure Integration of AI in Operational Technology (December 2025)
Customer and controlled data: Customer drawings, specifications, and pricing may be covered by confidentiality terms, and some shops handle export-controlled technical data. Your compliance owner decides what may enter any tool, and we design so the work needs as little of it as possible. We are engineers and cannot act as your counsel, and we make no legal, compliance, or safety guarantees.

Please do not send customer drawings, controlled technical data, or details of your plant network through this site's chat, contact form, or booking form.

What the research shows so far

Many manufacturers already use some AI: NIST's Manufacturing Extension Partnership, citing a Manufacturing Leadership Council survey, reports that 46% of manufacturers use AI tools such as chatbots in manufacturing operations and that more than 80% expect to increase their AI use in the next two years. The NIST page does not give the survey's sample or method; those are with the Council.
Source: NIST Manufacturing Extension Partnership, The Rise of AI in U.S. Manufacturing
Pilots show value; scaling them is the hard part: In Deloitte Switzerland's 2026 survey of more than 140 manufacturing organizations across industries, regions, and company sizes, around 84% of respondents reported measurable value from AI in their operations, but only around one in five use cases had been scaled consistently across sites or enterprise-wide. The most cited implementation challenges were high implementation costs (43%), lack of technical expertise (35%), and resistance to change (35%), and 79% cited operational disruption among the risks. These are respondents' own reports. None of them forecasts results for any one plant.
Source: Deloitte Switzerland, AI in Manufacturing 2026

Questions manufacturers ask first

Have you done this for a manufacturer before?: Not yet. Our delivered work is in construction, where we built a custom Procore application inside CJM Construction in South Florida. We will tell you plainly what has been proven and what has not, and every engineer in the network follows the same process.
Will AI touch our machines or control systems?: Not in anything we propose. None of the services on this page controls equipment, edits machine programs or setpoints, or makes a safety decision; they use business-side records and exports. Whether a project would ever need live plant data, and how, is decided with your IT and OT owners during onboarding. The CISA joint guidance encourages pushing data out of the control network and avoiding persistent access into it.
Do we have to replace our ERP?: No. We build around the systems you already run, spreadsheets included. What we can connect to depends on what your ERP and other vendors allow, whether that is an API, scheduled exports, or reports, and on what your IT team approves. We confirm that during onboarding before we commit to building anything.
How much will this save us?: We will not promise a number. Published survey figures on AI in manufacturing are self-reported and vary by process; none of them is a forecast for your shop. We measure a baseline during onboarding, setting up the measure first if you do not track it yet, and report against it.
Is this about replacing people on the floor?: No. What we build drafts, organizes, and checks; your people decide. Capturing what experienced people know only works with their help, so they choose what to share, review what is written, and approve it before anyone else relies on it.
Can our team paste customer drawings into a public chatbot?: Not before someone has checked that tool's terms against your customer agreements. If your shop handles export-controlled technical data, your compliance owner decides what may enter any tool; that call is theirs, and we make no legal or compliance guarantees. The CISA joint guidance on AI in operational technology advises minimizing risk by not sharing sensitive data with AI models, especially ones hosted by external parties such as public cloud services. We can help your compliance owner write the policy and choose an approved tool so your team has somewhere appropriate to work.

Bring us a bottleneck to look at

The first call is free. Walk us through how one job moves from quote to shipment today, and leave with an honest read on whether AI belongs in it. The method comes from our construction work, and the network behind it is on the site.

Book a call