We build AI systems for engineering and manufacturing companies with the same discipline the founder has applied to nuclear startups, high-voltage instrumentation, and controlled-environment agriculture for over 45 years. Characterize the model. Measure the output. Verify against primary sources. Deploy only where reliability is proven.
Senior engineers billed at $150–$250 per hour spend a third of their week writing specifications, chasing datasheets, formatting proposals, and searching for the last person who solved this problem. That's the work AI is best at — if it can be trusted.
Every engineering owner has heard the "AI made up a part number" story. Every one. Consumer-grade AI has no discipline of verification — it will confidently cite datasheets that don't exist, and hand you a bill of materials that doesn't build.
The path from "somebody should try AI here" to a workflow your team actually uses is engineering work in itself: model selection, prompt design, verification checks, integration with your existing systems, and a discipline your team can defend to auditors and customers.
"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 45 years and a shipping record.
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.
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 EngineerCustom machinery, instrumentation, electrical equipment, automation systems, controls, power electronics, or other engineered products. Companies with expensive engineering talent that have not yet systematically integrated AI into their engineering and business workflows. This is where the founder has personally worked through schematic capture, BOMs, component verification, international vendor coordination, specifications, controls, manufacturing constraints, and engineering change discipline — for decades.
Companies in industrial controls, commissioning, power distribution, critical infrastructure, high-voltage systems, data-center infrastructure, and energy projects. Verified AI for proposal & RFQ processing, technical document search, specification comparisons, submittal review, and internal engineering knowledge systems — for firms handling growing project volume without adding equivalent headcount.
10–100 unit franchisees who want AI systems for daily operating reports, management communications, inventory analysis, labor and scheduling analysis, vendor contract review, maintenance workflows, training knowledge bases, KPI exception reporting, and local marketing. Grounded in the founder's own record of growing a franchise business from 13 stores to 35 locations.
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, 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 Torino Ltd, 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, an active R&D facility specification, a six-band Class E resonant plasma platform — before we recommend them to yours.
What Torino Ltd 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 commissioning the Watts Bar Nuclear Plant with TVA; three years bringing the Callaway Nuclear Station online with Multi-Amp; then eleven years running MC Consulting as procedure writer for Oconee Nuclear, startup engineer for the E3S Electronic Safeguards project, and group leader for the Savannah River Site (Westinghouse / DOE). Wrote the operational test procedures for computers, networks, fiber modems, and field devices that governed how those systems could be safely commissioned. Re-designed a fiber-modem circuit that prevented a $4M cost overrun on a single project.
Grew a 13-store, $7M franchise operation into a 35-location, $31M multi-brand business (Arby's and Bojangles'). But the real story is what got built underneath the growth: this is where Mr. McCammon started applying nuclear-grade engineering discipline to non-engineering problems — and the systems that came out of it were the first automation platform he ever shipped.
He 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 designed and installed automated energy-control systems across every restaurant that saved 40% on electricity and gas — climbing to $126K in annual savings after amortization. He built a Cost Segregation software using 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 growth from $7.5M to $29.2M. Arby's "Innovator of the Year" 2008 and Bojangles' "Rookie of the Year" 2009 recognized the systems as much as the sales.
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 did two things at once: it was also the R&D contractor to ECOS International Ltd, charged with establishing proven methods for large-scale, pathogen-free controlled-environment agriculture using the same energy-conservation discipline.
The applied research inside ECOS is where the sealed-tunnel architecture, mobile grow-rack concept, and centralized nutrient delivery — the platform 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.
Now principal author of the eleven-specification engineering suite — 400+ pages, contractor-ready — for the Phase 2 R&D facility in College Station, Texas. Building shell, HVAC, power distribution, IT and controls, LED lighting, nutrient plumbing, seeding, harvest robotics, wash and sanitization, compressed air, fire protection, elevator systems. Each spec cross-referenced against the others by a discipline that catches silent invalidations before they reach the field.
Chief architect of 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. Drove the program 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. Author of the invariant checklist that tests every proposed fix against every constraint already solved.
And critically: this is the first program in the arc where AI is fully integrated from day one. Anthropic Claude, OpenAI ChatGPT, Perplexity and specialist models run in production across schematic review, datasheet cross-referencing, specification drafting, transformer math, control-system architecture, and the parallel business workstreams — investor materials, financial modeling, contract analysis, multi-document diligence. The discipline that governs the transformer bench governs the AI: measurement governs documentation.
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.
Engineering services is a very large market — one 2026 industry estimate puts US engineering-services revenue at roughly $386.7 billion. Industrial-engineer employment is projected to grow 11% from 2024 through 2034, versus 3% across all occupations.
Deloitte estimates US data-center power demand could rise from 33 GW in 2024 to 176 GW by 2035. That surge is already radiating through industrial supply chains — driving demand for generators, cooling equipment, cables, and components, and driving proposal & documentation volume on power/controls firms.
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.
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.