The numbers you're up against
Your competitors are already moving. For a precision-electronics or semiconductor plant, the real question now is whether your shop can pull it off. Three numbers first:
On the floor: the two problems
Veterans are retiring. Recipes can't go to the cloud. Across semiconductor test-and-assembly, PCB, and electronics plants, two things come up before anything else.
- You have an ERP and an MES, but when a complaint comes in, root-cause still means an engineer pulling logs for three days. When the veteran retires, that judgment walks out with them.
- Put your recipes in the cloud and you're handing your livelihood to someone else. For a TSMC-supply-chain plant, the first line of the customer's audit is: no AI in the cloud.
Data security & sovereignty: air-gap capable
ResearchAndMarkets' Edge Data Center Market — Forecast to 2030 has the edge data-center market nearly doubling, from about $50.9B in 2025 to about $109.2B in 2030, driven in part by security and regulatory pressure to process data locally. Recipes and process parameters increasingly have to stay in the plant, off the cloud.
MICH keeps every byte on the on-prem LowsiteOS box, with zero cloud dependency. LLM inference runs on local GPUs and sends nothing out. Model and agent updates arrive offline (USB / intranet), and the deployment can be fully air-gapped. One TSMC-supply-chain test-and-assembly plant runs fully air-gapped and passed its customer's security audit, with recipes and process data never leaving the building.
What it solves on your floor: four scenarios
McKinsey finds that manufacturers running Industry 4.0 cut quality-related costs by 10–20% and lift throughput by 10–30%. Here are the four scenarios where MICH puts that to work on the floor.
It connects — and it won't stall in a pilot
Results don't matter if it can't connect to what you run, or if it stalls in a pilot the way the last one did. This part is integration and getting to production.
Read-only integration, nothing touched
Not connecting to existing systems is the most common place AI stalls: Gartner projects over 40% of agentic AI projects will be canceled by the end of 2027, largely because integration gets too complex. LowsiteOS connects through an MES Adapter (OPC-UA / Modbus / database connectors) in read-only mode, so it never writes to or modifies anything in your ERP or MES. Every agent action writes only to LowsiteOS's own database; any write-back you later authorize goes through a separate API gateway with a full audit log, taking "the systems don't talk to each other" off the table from day one.
Why this doesn't end in pilot purgatory
McKinsey and the Global Lighthouse Network find at least 70% of manufacturers stuck in "pilot purgatory": the pilot runs but never reaches the line; of the ones that deliver, about 90% start by focusing on a single high-pain use case. MICH takes exactly that path: we start with one painful scenario and run a 6-week PoC that produces verifiable results on your real data. Hardware and software ship together, with no AI team to staff and no integration to wire up yourself, sidestepping the two things that send pilots to purgatory: too much integration, and no focus.
Who does what, from PoC to production
A lot of the outcome comes down to who does the integrating: MIT NANDA's State of AI in Business 2025 finds buying an integrated solution from a specialist succeeds about two-thirds of the time, versus about one-third for in-house assembly. MICH handles the data-connection scripts (MES/SPC/OQC), agent setup, AutoML model training, and two training sessions. On your side it takes one IT contact and one process or QA contact, typically about 2–3 person-days each, within four weeks.
Cost, payback, and the way out
Last comes money and risk: what you spend, how fast it pays back, and how you walk away with your data if it isn't a fit.
Where on-prem saves, and how fast it pays back
Metered cloud inference has no ceiling, so budgets are hard to set and ROI hard to pin down; McKinsey's The cost of compute estimates cumulative global AI-infrastructure capex reaching $6.7 trillion between 2025 and 2030. MICH's on-prem deployment, hardware and software in one purchase, is a one-time capital cost that is predictable from day one. One customer's cloud bill was climbing about 40% a year with returns that wouldn't converge; after moving on-prem, payback typically lands around 12 months (a 9–14 month range, depending on utilization, based on customer pilots and internal estimates).
Pricing and the 6-week PoC
6sense's survey of nearly 4,000 B2B buyers found 83% had largely decided what they needed before they ever contacted a vendor. MICH keeps the entry low-risk: a 6-week PoC up front, with a free M1 loaner and no commitment. Hardware and software are a single purchase, priced by configuration; we don't list project prices on the site, and full pricing comes through booking a Demo, with the hardware tiers on the AI Server product page.
Exit terms and getting your data back
For a mid-size plant adopting AI for the first time, the real worry is usually the exit: getting locked to a vendor, not getting the data back. MICH puts the exit terms up front: during the PoC (first 6 weeks) you can walk away at zero cost, just return the loaner hardware; and your data comes back on an encrypted SSD, with LowsiteOS providing a standard SQL dump and vector-store export. No data lock-in.
Start with one painful scenario. See results in 4–6 weeks. Tell us the pain you want to tackle first (yield root-cause, predictive maintenance, or labor-hour anomalies) and the MES/ERP you run, and we'll set up a live demo on real data.