In automation and controls firms of 20–200 people, senior engineering time spent on documentation, quoting and specification work typically runs 25 to 40 percent. Most owners we talk to guess low — often by half. We build verified AI workflows that take that time back, using the same discipline our founder has applied to nuclear commissioning and high-voltage instrumentation for 52 years. The first question is what your number actually is.
Engineering capacity automation.
Verified AI workflows that automate everything around the expertise — never the expertise itself.
Engineering time in this sector generally costs $150–$250 an hour fully loaded, and a meaningful share of it goes to writing specifications, chasing datasheets, formatting proposals, and hunting down whoever solved this last time. That is precisely the class of work AI handles well — provided the output can be verified.
Ask yourself What share of your senior engineers' week is drafting and searching rather than deciding?
If you have already watched an AI tool invent a part number, you know exactly why verification is not optional. If you have not yet, you will. Consumer-grade AI has no discipline of verification — it will cite a datasheet that does not exist and hand back a bill of materials that does not build.
Ask yourself If a wrong number reached a customer, what would it cost — and who would have caught it?
Getting from "somebody should try AI here" to a workflow the team actually uses is a real engineering project: model selection, workflow design, verification checks, integration with existing systems, and a discipline your people can defend to an auditor or a customer. Most firms have neither the hours nor the appetite to work that out on their own time.
Ask yourself Who inside the business currently owns this — and what else are they supposed to be doing?
We do not know which of these applies to you, and we would not guess. The two instruments further down this page — the fit check and the workflow diagnostic — exist so that you can find out with your own numbers before speaking to anyone.
Most consultants keep the diagnostic behind the meeting. We would rather you saw it first — partly because it tells you more about how we work than any claim on this page could, and partly because a few of these will be uncomfortable to answer, and you should find that out on your own time rather than ours.
"We help companies implement AI."
Unverified output pasted into deliverables.
Single-vendor lock-in on a subscription tool.
A junior consultant with a prompt library.
Verified workflows for engineering and business tasks.
Every output cross-checked against primary sources.
Multi-model architecture — the right AI for each job.
A principal engineer with 52 years and a shipping record.
Not one of them touches engineering judgment. Every service below automates the work around your engineers' expertise — the drafting, searching, cross-referencing and formatting — and leaves the deciding where it belongs.
Specifications, submittals, RFQs, technical narratives, engineering change discipline, and vendor documentation packages — drafted in a fraction of the time, cross-verified against your standards and against the source datasheets. Your engineers review and approve; they no longer draft from scratch.
Datasheet cross-referencing, second-source identification, obsolescence forecasting, and vendor due diligence — done against real datasheets, not the model's memory. We verify every part number against the manufacturer's published record before it enters a bill of materials.
The senior engineer who's about to retire has 30 years of "how we actually solved this" in her head. We build queryable knowledge systems that capture and preserve that expertise — searchable, verifiable, tied to specific projects and decisions. Not a chatbot: a durable engineering record.
For firms with more proposals than time to write them: ingest the RFQ, cross-reference your capability library, extract scope and pricing bases, draft the response, and flag every requirement your team must personally sign off on. Turnaround from days to hours, without lowering quality.
Contract review, regulatory synthesis, competitive analysis, technical literature review — across dozens or hundreds of documents in parallel. Every claim traceable to a source, every source verifiable. The workload of a research team, delivered by a small one.
For owners, CEOs, and department heads: which models to use for what, how to structure a verification discipline your team can follow, how to evaluate vendors selling you AI, and how to build a policy that protects your IP and your reputation. Not theory — the exact playbook we run internally.
Every component, its datasheet revision, approved alternates, lifecycle status, and every assembly and project it has ever appeared in.
Complete document set per job — specs, submittals, drawings, change orders, correspondence, closeout — assembled rather than hunted.
Every agreement, quote, PO, certification, and performance record tied to a counterparty — with obligations and renewal dates surfaced.
What was current on the day that decision was made — the question that matters in a warranty dispute or a liability review.
Which clause of which standard drove which design decision — and everything in the corpus affected when that standard is superseded.
"What did we quote Acme for the 480V panel in 2022, and which breaker did we spec?" — answered from the record, with the source cited.
Identify what the model actually does well on your class of task — not on published benchmarks. Bench-test against representative work from your firm. Document the failure modes.
Design the workflow so the model can only operate where it's proven reliable. Everything else routes to a human or a second verification pass. No blank-page trust.
Every quantitative output cross-checked against primary sources — datasheets, standards, your own historical record. The invariant checklist tests each fix against every constraint already solved.
Only after the workflow has passed the first three steps does it enter production. Your team runs it; we maintain the discipline. Measurement governs documentation.
"I understand why you don't trust it. I don't trust unverified AI output either. I treat these models the way I've treated engineering systems for decades — characterize them, measure them, constrain them, verify the output, and use them only where they prove reliable."
— Edward L. McCammon, CEO & Principal EngineerMC Consulting helps industrial automation and controls companies recover expensive engineering capacity by automating the repetitive work surrounding RFQs, specifications, proposals, datasheets and technical documentation — while engineers retain every consequential judgment and approval.
Machine builders, panel shops, systems integrators, OEM controls groups, process and motion control houses, and automation companies building engineered-to-order equipment. Firms with expensive engineering talent that have not yet systematically integrated AI into their engineering and commercial workflows. This is the ground the founder has personally worked for decades — schematic capture, BOMs and component verification, PLC and control-system architecture, international vendor coordination, specifications, submittals, and engineering change discipline.
Custom machinery, instrumentation, power electronics, switchgear and assemblies, and other engineered-to-order equipment built in low volume against customer specification. The documentation load per unit shipped is the highest of any manufacturing model — datasheets, BOMs, test procedures, certification submittals and O&M packages — and almost none of it is genuinely one-off. That gap between "written fresh every time" and "actually 70% identical" is the whole opportunity.
Industrial controls and commissioning firms, power distribution, high-voltage systems, data-center infrastructure, and energy projects. Verified AI for bid and proposal throughput, first-pass spec extraction, submittal preparation and review, commissioning documentation, and standards retrieval — for firms absorbing rising project volume without adding equivalent headcount. The founder wrote operational test procedures for nuclear-grade computer, network and field-device systems, and later designed and built three Network Operations Centers and roughly 350 miles of fiber plant across Western North Carolina — for a Library of Congress program, a sovereign tribal nation, and a regional carrier. This is the same discipline applied to the same class of problem.
The founder, Edward L. McCammon, has spent his career in the domains where getting it wrong is not an option: nuclear power startup engineering, DOD/DOE security systems, carrier-grade fiber and network operations centers, high-voltage cold plasma instrumentation, controlled-environment agriculture, and multi-brand operating companies.
The discipline is consistent across every one of those programs: measure before you commit, verify before you ship, and never accept a fix that hasn't been tested against every constraint already solved. That discipline is exactly what current-generation AI systems need — and almost nobody consulting on AI today comes from a career where that discipline was the price of admission.
At MC Consulting, that discipline is now applied to AI systems on behalf of clients who need the leverage without inheriting the failure modes. We use the tools every day on our own programs — a $75M engineering raise and an active R&D facility specification — and on client engagements, including a six-band Class E resonant plasma platform delivered under contract, before we recommend them to yours.
What MC Consulting sells today is not new. It is the same discipline that has been carried, refined, and re-applied through every company the founder has owned and run. Each one solved its era's version of the same problem: how do you take work that costs too much, takes too long, and depends on the memory of one person — and turn it into a system?
Seven years at TVA's Watts Bar Nuclear Plant — on-the-job training working hands-on across every system in the plant, which is where the fundamentals were learned rather than read. Then three years as a startup engineer with Multi-Amp, bringing the Callaway Nuclear Station online — the first commissioning work, and where the discipline got formalized.
This is the company you are reading about. He formed MC Consulting in 1984, immediately after Callaway, in order to contract directly for the next job: two years at the Palo Verde Nuclear Generating Station for Arizona Public Service, across all three nuclear units, bringing the plant's computer systems online. Balance-of-plant monitoring, reactor control rod systems, radiation monitoring, security systems, and the water treatment facility — plus training Arizona Public Service's in-house personnel to run and maintain them.
Then two and a half years at Plant Vogtle for Georgia Power and Southern Company, as procedure writer and startup work package controls processor through the commissioning of Units 1 and 2. Then the classified work. At the Savannah River Site in Aiken, South Carolina — a Department of Energy nuclear weapons facility — he held Top Secret clearance, worked on the team that designed the E3S Electronic Safeguards and Security System, and was onsite as startup engineer overseeing its installation, startup and the training of site personnel. He served as systems engineer on the E3S maintenance system and group leader for site-wide security access control, wrote the operational test procedures for computers, networks, fiber modems and field devices that governed how those systems could be safely commissioned, and re-designed a fiber-modem circuit that prevented a $4M cost overrun on the project. The last MC Consulting contract was Oconee Nuclear — procedure writer for electrical power, control and metering systems — before he turned to building restaurants.
The Winning Team was a franchisee — an independent operator building and running restaurants under the Arby's and Bojangles' brands, not the brands themselves. Over fourteen years it went from 13 stores and $7M to 35 locations and $31M. Mr. McCammon's lane was development and systems — site selection, facility design, build-out, and every piece of technology the company ran on. An operations partner oversaw restaurant operations. What he built underneath the growth is why this belongs on the timeline: it is where nuclear-grade engineering discipline first got applied to problems nobody considered engineering problems, and the systems that came out of it were the first automation platform he ever shipped.
He designed and wrote a menu-driven back-office software program for inventory, sale tracking, labor, and scheduling — cutting administrative time by two hours per store per day and saving an estimated $153K per year across 14 stores. He engineered and installed automated energy-control systems across every restaurant, saving 40% on electricity and gas and climbing to $126K in annual savings after amortization. He built a Cost Segregation software on the tax-code allocation model that produced a 75% reduction in taxes across the first five years of any new facility, and the freed cash financed the expansion from $7.5M to $29.2M. Arby's named him Innovator of the Year in 2008 and Bojangles' Rookie of the Year in 2009 — both awarded for the designs and systems, not for the sales line.
Co-founded to turn the Winning Team energy-control systems into a standalone consulting practice — turnkey energy evaluations with 24/7 monitoring for retail and franchise clients. But ECOS ran three workstreams under one roof, and the largest of them was telecommunications infrastructure.
The lineage is direct rather than coincidental. Two decades earlier on the E3S nuclear project he had re-designed the fiber modem transmitter/receiver circuit for an access-control system — the fix that averted a $4M cost overrun — and written the operational test procedures for the fiber modems, network equipment and token-ring hardware that system depended on. The Western North Carolina work applied that same fiber and network discipline at regional scale.
And the third workstream — the applied agricultural research inside ECOS — is where the sealed-tunnel architecture, mobile grow-rack concept, and centralized nutrient delivery that would become Agrifacture was first laid down as engineering, not thesis.
The full-scale execution of what ECOS proved possible. Designed, built, and operated two pilot facilities as fully-instrumented Total Controlled Growing Environments — sealed multi-tunnel structures with centralized nutrient delivery, dock-in / dock-out mobile grow racks, and unified climate, lighting, and CO₂ control across every zone. Chose fruiting mushrooms — the most environmentally demanding CEA crop — as the test article, on the reasoning that any platform holding those tolerances would generalize. Both facilities reached sustained production of ~750 lbs/week per 20-ft tunnel, then were closed on purpose once the architecture, controls, and recovery loops had been validated.
The governing insight was to stop treating a farm as a farm. The rack is the product carrier. The tunnel is the production line. The AGV is the conveyor. Once food production is modeled as an assembly line, every discipline from manufacturing — takt time, work-in-process routing, station-level quality gates, MES-tracked genealogy — becomes directly applicable. That reframe is what made full automation designable rather than aspirational.
Now principal author of that eleven-specification engineering suite — contractor-ready — for the Phase 2 R&D facility in College Station, Texas, where the robotics and the AI layer get hardened at full production rate before the platform is templated for global deployment.
This one is a client's program, not ours. It arrived the way good work often does — a friend of a friend with a hard problem and no obvious place to take it. The brief: take a technology Nikola Tesla patented 135 years ago and bring it into the 21st century on solid-state hardware. The work was contracted through Torino Ltd, the international consulting entity, and has since been consolidated into MC Consulting. The client owns the product and the company — and under the terms of that contract, we retained the intellectual property developed in the course of the work. We own the engineering discipline that got it built, and the design that came out of it.
The deliverable is the Mark V "PIONEER" — a six-band Class E resonant platform spanning 125 to 575 kHz at 15,000 to 25,000 volts per bulb. We took it from concept through schematic capture, bill of materials, international vendor coordination and full transformer characterization: secondary board laws, coupling coefficients, assembled resonances, primary geometry — measured on the bench rather than derived on paper. The invariant checklist that tests every proposed fix against every constraint already solved was written for this program, in response to specific failures, and has since caught others before they reached fabrication.
And this is where the two halves of the practice met. We had already been running AI inside our own work at Agrifacture — specification authoring, cross-referencing, verification. ION-FLUX was the first engagement where we integrated full AI systems into a client's design and build-out from day one, as operational infrastructure rather than an assistant bolted on afterward. Anthropic Claude, OpenAI ChatGPT, Perplexity and specialist models run in production across the entire program, under the same rule that governs the transformer bench: measurement governs documentation.
AMT reports $3.44 billion in US manufacturing-technology orders in the first half of 2026 — 36% above the first half of 2025, and the strongest half-year since the data series began in 1998. AMT attributes part of the rising order value to additional automation content.
Deloitte's 2026 manufacturing outlook reports that 80% of 600 manufacturing executives surveyed planned to put at least 20% of their improvement budgets into smart-manufacturing initiatives, with AI specifically named across supplier engagement, knowledge capture, work instructions, and production-office activities.
BLS projects industrial-engineer employment to grow 11% from 2024 through 2034 against roughly 3% across all occupations, citing cost reduction, process optimisation and automation as the demand drivers. Electrical and electronics engineering is projected at 7% over the same period.
Deloitte estimates US data-center power demand could rise from 33 GW in 2024 to 176 GW by 2035. That surge is radiating through industrial supply chains — driving demand for generators, cooling equipment, cables and components. Transformer and grid-equipment lead times now exceed 160 weeks in some cases, and every deferred order still carries proposal and documentation load.
We diagnose whether your firm is actually a fit for verified AI workflows right now. Some are, some aren't. If the timing is wrong or the workload doesn't justify the investment, we say so, and the call ends without an ask.
Two-week engagement to characterize the repetitive engineering and administrative work your team is doing today, map it against what verified AI can and cannot do reliably, and produce a written report ranking candidate workflows by ROI and risk. This is a fixed-fee deliverable, not a lead-in to a bigger sale.
We select the single highest-ROI workflow from the assessment, build it, characterize its performance against your work, and deploy it with your team trained and the verification discipline documented. You measure the impact before deciding whether to expand.
Additional workflows, advisory retainer for your executive team, quarterly discipline audits, or a full-time embedded partnership. Every client stays engaged only for as long as the value is measurable. No lock-in, no auto-renewal.
Specifications, RFQs, datasheets, BOMs, submittals, document control. This is where the leverage is largest and where the verification discipline has to be established. Nothing downstream is worth building until this holds.
Agents, CRM, intake, front-of-house. Genuinely valuable once the technical core is under control — and genuinely a distraction before it. We will tell you which side of that line you are on.
Autonomous execution of the workflows the audit has already characterized and bounded — triggered by events in your systems, operating inside the verification envelope, escalating to a human at the edges rather than guessing.
A CRM that actually reflects how engineered-to-order business works — quote revisions, long cycles, spec-driven scope changes, and the relationship between an RFQ, its proposal, and the job it becomes. Built or rebuilt around your process, not a template.
Voice and message handling that knows what your company actually builds — qualifying an inbound RFQ, routing a technical question to the right engineer, capturing a service call with the right equipment details, and never inventing a capability you do not have.
Closing the loop from inbound enquiry through proposal, order, engineering release, and invoice — so the same part number, revision and scope description survive the whole journey instead of being retyped four times by four people.
The queryable version of your own record — what was specified, why, on which project, against which standard. Answers cited to the source document rather than asserted, so an engineer can check the claim in one click instead of trusting it.
The connective work that makes the rest of it hold together — API integration between systems that were never designed to talk, event routing, and the plumbing that stops your staff being the integration layer between four pieces of software.
The argument for consolidating this under one provider is real: the agent that drafts your proposal, the CRM that stores it, and the receptionist that took the call all depend on the same underlying record. Split across three vendors, nobody owns the seam — and the seams are where these systems fail. Under one provider with one discipline, the record stays coherent from first contact through engineering release.
The argument against is equally real, and we will make it to you where it applies. If your engineering documentation is still uncontrolled, an AI receptionist is a distraction with a monthly fee. If your CRM problem is that nobody enters data, no amount of automation fixes a process problem. We would rather sequence you correctly and earn the second engagement than sell the whole stack on day one and watch half of it go unused.
If your firm is a fit, we'll say so and outline what a Workflow Audit would look like. If it isn't — because the timing is wrong, the workload doesn't justify it, or the discipline isn't ready — we'll say that too. Either way, you leave the call with a clearer picture of where verified AI fits in your operation than you had going in.