Data silos and broken workflows
ERP, MES, APS, and equipment data each run on their own, and even the timestamps don't line up. Getting the full picture of one event means stitching systems together by hand: slow, and easy to miss things.
Product — LowsiteOS
MICH LowsiteOS: your data never leaves the floor —and your decisions get sharper.
Enterprise AI, built to decide.
LowsiteOS is MICH's enterprise AI data platform. It connects the ERP, MES, IoT, and documents you already run into one on-prem brain that you query in plain English, build knowledge bases on, and deploy AI agents from, with no data-science team and no code. It ships free with every MICH AI Server; usage licensing only starts once you're actually using it. The data scattered across your systems turns into decisions you can see at a glance, and it never leaves the building.
- Data never leaves the floor
- Free with the AI Server
- No code, ready out of the box
- Institutional knowledge, kept
- Hardware + software, pre-integrated
LowsiteOS — powered by MICH
In 30 seconds
Connect the data scattered across your ERP, MES, IoT, and documents into one on-prem, queryable AI brain, included free with every MICH AI Server, and it never leaves the building.
How it works, in three steps:
01
Connect your systems
ERP / MES / IoT / documents plug in directly, with nothing replaced and nothing sent to the cloud.
02
Govern & analyze on-prem
Cleansed and vectorized; a local LLM factory brain reads your data.
03
Get decisions & agents
Ask in plain language and deploy AI agents, for decisions you can see at a glance.
Proven results (every figure sourced):
+10%
On-time delivery
A technology partner, 50+ fabs
−15%
Cycle time
A technology partner
4–8 wks→2–3 days
New-hire handover
MICH early pilot, n=3
~12 mo
Typical payback
Pilots + MICH estimate
01Why you need a data platform
Most enterprise AI doesn't fail on the model — it fails on the data underneath.
Data locked in systems that don't talk to each other, knowledge scattered across people's heads and inboxes, AI budget spent on one pilot after another: these all point to the same gap. There's no platform that connects the data, lets you ask it questions, and acts on it.
Knowledge walks out the door
When a veteran operator, senior researcher, or key account manager leaves, the judgment, recipes, and customer context leave with them. Person-to-person handover takes four to eight weeks, and still leaves gaps.
Pilots that cost money but don't help
PoC costs keep climbing and cloud inference scales linearly with usage, with no ceiling in sight, yet very little turns into decisions you can act on, or AI insight that moves ROI.
The race to 2030: getting enterprise AI to land
15%
Gartner forecasts that by 2028, at least 15% of day-to-day work decisions will be made autonomously by AI agents (0% in 2024). AI is moving from answering questions to making the call.
60%
Gartner forecasts that by 2028, 60% of agentic-analytics projects that wire AI to data with standard connectors (MCP) alone, without a consistent semantic and data foundation, will fail.
2×
ResearchAndMarkets (Edge Data Center Market — Forecast to 2030) estimates the edge data center market will grow from about US$50.9B in 2025 to about US$109.2B by 2030, nearly double, driven by AI processing close to the data source and by security and regulatory demands for local handling.
Whether an enterprise can decide with AI and turn the investment into ROI comes down to one thing: turning scattered data into a governable, orchestratable, sovereign data foundation. LowsiteOS is the operating interface for that foundation, reading, analyzing, and acting on your data, inside your own facility.
02What LowsiteOS is
LowsiteOS: an operating system for your enterprise's sovereign data and AI.
The "OS" in the name means operating system. LowsiteOS runs natively on the MICH AI Server and, the way an OS manages a computer, manages your whole plant's data and AI: it connects ERP, MES, IoT, and document data into one on-prem brain where you read data, run analysis, and flexibly deploy AI agents, integrated without the pain.
Your existing systems
Data sources; MICH doesn't replace them
Enterprise systems
ERP (SAP…) · SCM · CRM
Manufacturing systems
MES · WMS · APS · SPC · PLM
Floor & documents
PLC/IoT · 360 cameras · PDFs/docs
MICH hardware + software
AI Server + LowsiteOS
M1–M5 | LowsiteOS built in
- 01Data governance: cleanse, map, vectorize
- 02Lakehouse: structured + vector DB
- 03Local LLM factory brain + AutoML/BI
- 04Agent orchestration (via MCP)
AI decisions & agents
Runs on the floor; data never leaves
- Veteran-knowledge KM base
- Yield root-cause · APS scheduling
- VLM 360 inspection · predictive maintenance
- GenBI conversational queries
Decisions you can see at a glance
What MICH delivers is the box in the middle: a hardware-and-software AI Server with LowsiteOS built in, sized M1–M5 by compute (see the AI Server page). On the left, your ERP/SAP, MES, and floor equipment are systems you already own; once their data is ingested, LowsiteOS governs, analyzes, and grows agents on-prem, and decisions come out on the right.
Technical stack
Every layer on-prem, every layer swappable — open models, open storage, NVIDIA-accelerated.
- On-prem
- Swappable layers
- Open models
- NVIDIA-accelerated
LowsiteOS on-prem appliance
Hardware + software, runs on the floor — data never leaves.
AI agents & apps
LowsiteOS Agents
- Gen BI
- APS Optimizer
- KM Master
- Yield RCA
- Predictive Maintenance
NVIDIA AI Enterprise
- BioNeMo · discovery
- Clara · imaging
- Parabricks · genomics
- Metropolis · vision
- Isaac · robotics
- Omniverse · twin
Local LLM
- gpt-oss
- Gemma
- Nemotron
- Llama
- MiniMax
- Qwen
Data OS
- Relational DB
- Vector DB
- Object storage
Hardware
- GPURTX PRO 6000 · H200 · B300
- CPU · RAM · high-speed storageIntel · RAM · NVMe
Data is ingested upward into LowsiteOS
Your existing systems
Heterogeneous systems and files — MICH doesn't replace them; once ingested, data never leaves.
Systems
- WMS
- CRM
- MES
- ERP
- MSSQL
Files
- PDF · Word · PPT
- CSV · JSON · XLSX
Data is ingested from existing systems and files, governed and vectorized on-prem, and grows upward into analysis and agents.
Five modules ship in the box, from connecting data to deploying agents, end to end:
Data OS
Source connectors, data factory, data dictionary, to clean and integrate heterogeneous systems into usable assets.
Smart analytics + BI
Ask your data in natural language, automated analysis, insight suggestions, drag-and-drop dashboards.
AutoML + scheduling
No-code model building, with evaluation, explanation, and testing in one place.
Knowledge base (RAG)
Document upload, OCR parsing, vector-retrieval Q&A, so enterprise knowledge becomes queryable.
LowsiteOS AI
Local LLM repository, agent configuration, MCP, private enterprise GPT.
03Institutional knowledge shouldn't walk out the door
When a veteran retires, the decades of know-how they built shouldn't retire with them.
The most valuable knowledge usually isn't in any system. It's in a veteran operator's instinct, a senior researcher's trial and error, a key account manager's customer context. When they leave, the company learns it all over again. LowsiteOS turns that experience into an asset that stays and can be queried.
"Why did this batch go wrong?" You used to go ask the veteran. Now, the veteran's instinct already lives in the AI, and new hires can just ask it themselves.
Before
30%
Share of key questions about a predecessor's work that a new hire can answer correctly.
After
80%
Knowledge Relay: an on-prem LLM analyzes a departing employee's work trail and produces technical-decision summaries and open-task reports.
Sources & notes
Source: MICH early-pilot estimate (pilot, n=2), not a statistically significant sample. Another observation in life sciences: researcher handover shortens new-hire onboarding from 4–8 weeks to 2–3 days (MICH early pilot, n=3). Named attribution pending client legal review.
KM Agent uses a local LLM + knowledge graph + RAG to turn a veteran's or researcher's experience into a queryable knowledge base; Knowledge Relay kicks in when someone leaves, analyzing their authorized work trail and organizing the handover automatically. Both run inside the plant; raw data never leaves.
It's the question every buyer asks: "Letting AI read employees' work records, does that hold up on privacy and the law?" LowsiteOS designs for it up front:
Employee consents first
The employee signs an AI-analysis authorization (built-in template) before anything starts.
Only structured summaries delivered
The company receives structured summaries; raw conversations and email are never loaded into the corporate knowledge base.
Full audit log
An audit log can prove to the employee, HR, and legal that only the authorized scope was read.
Designed within compliance
Aligned to GDPR contractual necessity and the right to be forgotten, erased on the employee's request.
04How four industries use it
One data platform, four industries, each with its own use.
Every industry has different pain, but the foundation is the same: connect scattered data and let it make decisions in real time. Every performance figure below is sourced.
Manufacturing
Connect MES/SPC/OQC and let AutoML surface the parameters eating into yield; the scheduling engine optimizes across the whole plant on multiple objectives. Complaint root-cause analysis drops from the "days" of digging through systems and aligning departments to an auto-generated candidate list in 30 minutes (sample customer).
- Yield RCA
- APS scheduling
- MES Adapter
A technology partner's real-world results (deployed at 50+ semiconductor/panel fabs): on-time delivery +10%, cycle time −15%, output +2–8%, over-Q-time −22% (APS); yield +0.5–4%, utilization +20% (Yield RCA).
Scheduling-engine technology partner
Want to see it run on your own industry's use case? Tell us the systems you run, and we'll set up a demo on real data.
Book a demo05AI Agent Store
LowsiteOS is the operating system. AI agents are the apps you install on it.
Like installing apps on a phone or software on a computer: AI agents install on the AI Server running LowsiteOS. Add whichever capability you need, and skip (and skip paying for) the ones you don't. A base set ships with the hardware.
Open, and buildable. Beyond the ready-made agents, you can build your own enterprise-specific AI agents three ways: no-code, low-code, or the Python SDK (see the FAQ below).
Deployed
APS scheduling
Dynamic neural network + genetic algorithm for near-optimal multi-objective scheduling across the whole plant.
On-time delivery +10%, cycle time −15%, output +2–8%.
Deployed
Yield RCA
Integrates MES/SPC/OQC; AutoML surfaces the parameters that drive yield, cutting root-cause time sharply.
Yield +0.5–4%, utilization +20%.
Deployed
KM Agent
Local LLM + knowledge graph + RAG turns a veteran's or researcher's experience into a queryable knowledge base.
Life-sciences onboarding 4–8 weeks → 2–3 days.
Early pilot
Knowledge Relay
When an employee leaves, an on-prem LLM analyzes their authorized work trail and produces decision summaries and a handover to-do list.
New-hire accuracy on key questions 30% → 80%.
Industry reference
Predictive maintenance
Feeds PLC/HMI/IoT signals into a time-series database and flags vibration, temperature, and current anomalies early.
Unplanned downtime down 20–50%.
Industry reference
AI Vision 360 inspection
360 cameras / AI glasses / Jetson Edge; a VLM identifies line anomalies and safety violations in real time.
PPE / safety violations move from after-the-fact spot checks to real-time, per-shift alerts.
Demo
GenBI Agent
One-click integration of ERP + manufacturing Excel; conversational queries replace manual tables and report waits.
Query ERP/Excel by conversation, replacing hand-built reports.
Design target
Logging Agent
Timestamp tracking and auto-generated daily work logs, append-only + hash chain for tamper-evidence.
Audit logs follow the FDA electronic-records regulation (21 CFR Part 11) in principle: timestamped, tamper-proof, traceable to a person.
Demo
AR aging analysis
Connects via SAP OData for dynamic credit scoring, AI-drafted collections, and an aging root-cause dashboard.
Demo environment + synthetic data.
Don't see the agent you need? Tell us your use case: build it through the three paths, or build it together with MICH.
Book a demo06Why it's purpose-built for the M-series
Why MICH built a new LowsiteOS specifically for the M-series AI Server.
① Your data never leaves — three mechanisms, all on-prem.
Network layer
Fully air-gappable
Deploy fully air-gapped with offline updates; or an outbound-only allowlist, outbound direction only, no inbound.
Storage layer
Encrypted on-site
All data (vectors, raw documents, agent logs) is written to the on-prem LowsiteOS host and encrypted at rest (AES-256).
Compute layer
On-prem inference
LLM inference runs 100% on local GPUs; no tokens are sent out; model weights are signature-verified against back doors.
② Why it only runs on the M-series — two reasons.
Firmware-level data sovereignty
Data protection lives in BMC/Secure Boot at the firmware layer, active from boot, and only on MICH's own hardware.
One support and upgrade path
Hardware and software ship and are warranted together by MICH, on a single upgrade path, with one place to call when something goes wrong.
According to MIT NANDA's State of AI in Business 2025, integration solutions bought from specialized vendors succeed about two-thirds of the time, versus about one-third for in-house DIY builds; Gartner also forecasts that over 40% of agentic AI projects will be canceled by the end of 2027 due to integration complexity.
③ How licensing works — free with the hardware, billed only on overage.
Free with the hardware
The matching LowsiteOS license is included, with no separate software fee.
Billed only on overage
Charged only when monthly active agents or inference volume exceed the included quota (quota scales with level).
Most never trigger it
A predictable cost ≈ hardware + annual maintenance.
07Cloud vs. on-prem
Should your enterprise AI live in the cloud — or on your floor?
Almost all enterprise AI on the market runs in a vendor's cloud: your data goes up, and you're billed by usage. LowsiteOS runs on your floor; your data never leaves.
Cloud enterprise AI
- Data
- Uploaded to the vendor's cloud
- Cost
- Metered by usage, no ceiling
- Integration
- Usually tied to a single ecosystem
On-prem LowsiteOS
- Data
- Stays on your floor, not one row leaves
- Cost
- Inference runs on your own GPU, with no usage-metered cloud AI bill
- Integration
- Across ERP + MES + the floor, with sovereignty in your hands
LowsiteOS connects the operational systems you already run and turns their data straight into analysis and decisions. It covers, today:
Finance / supply chain
Cost, inventory, sales-production balance
- SAP S/4HANA
- Oracle ERP
- Microsoft Dynamics 365
Manufacturing execution
Scheduling, on-time delivery, quality
- Rockwell FactoryTalk
- Siemens Opcenter
- Wonderware
Equipment control
Utilization, predictive maintenance
- Siemens S7
- Mitsubishi MELSEC
- Allen-Bradley (OPC-UA)
- Ignition
- AVEVA
IoT
Floor sensing, real-time alerts
- AWS IoT
- Azure IoT Hub
- ThingWorx
Documents / knowledge
SOPs, veteran-knowledge Q&A
- Office docs
- SharePoint (RAG)
Human resources
Handover, hiring (PoC required)
- Workday
- SAP SuccessFactors
Sources & notes
HR and legacy ERP (Workday, SuccessFactors, SAP ECC 6.0, etc.) are technically integrable but have no customer deployments yet, so an individual PoC is required.
LowsiteOS capability levels are included with the M-series AI Server you choose, with no separate software license:
With M1
Base
Data OS + RAG + BI + AutoML, one primary model on a single box, with a base agent set. A fit for a single team or department to start.
With M2–M3
Advanced
Everything in Base, plus parallel multi-model, the full Agent Marketplace, lightweight LoRA fine-tuning, and multiple workspaces. A fit for cross-department deployment.
With M4–M5
Flagship · AI Factory
Everything in Advanced, plus rack-scale orchestration, multi-tenancy, and full-scale fine-tuning/training. A fit for whole-plant or whole-organization scale.
FAQ
Questions about LowsiteOS
Tap any question to expand the answer.
Book a demo
Start with one use case. Stand up your first agent in weeks.
Tell us the pain point you want to tackle first and the systems you run, and we'll set up a live LowsiteOS session demoed on real data. Typically 4–6 weeks to a first working agent.
Fast response · Free consultation