Govern
From Shadow AI to governed assistants
Bring scattered ChatGPT use into one platform: the same models teams already like, now with permissions, audit and data that never trains third-party models.
Manufacturing
All your plant's AI, governed and compliant
Centralize how every manufacturing team uses AI on one platform: any model, no lock-in, corporate data that never trains third-party models, cost under control, and protection for trade secrets, IP, NIS2, GDPR and the EU AI Act by design.
See how it worksShadow AI under control
Replace scattered, ungoverned chatbot use with one platform — permissions and audit included
Your data stays yours
Trade secrets and IP never train third-party models; you control residency and access
Industry-grade compliance
Trade secrets, IP, NIS2, GDPR and the EU AI Act — traceability on every AI interaction
What your teams build, governed
One governed platform for every manufacturing team
Production, quality, maintenance, supply chain, ops — each team builds what it needs with any AI model, while you keep one place to set permissions, control cost and prove compliance.
Conversational AI
Conversational AI on your data, under your control
Give teams a governed way to chat with any AI model over approved data and tools (MCP) — instead of pasting machine logs and specs into public chatbots. Every conversation runs with permissions and a full audit trail, and corporate data stays private.
- Any model — switch without re-platforming
- Acts through approved tools (MCP), with guardrails
- Permissions and audit on every conversation
- Trade secrets and IP never leave your governance
Threads · Plant analysis plants_dbmachine_events.csv
Understanding intent…
Querying plants_db
Auto-generated SQL
Calling maximo-mcp…
MCP tool call
{ "tool": "maximo-mcp", "action": "create_task", "summary": "Open a maintenance work order for line 07" }
via maximo-mcp · just now
Assistants
Real assistants for the plant, not Shadow AI
Build assistants your teams actually use every day: a maintenance assistant that answers from equipment manuals and past incidents, a quality assistant for documenting and classifying defects, or a procurement assistant over supplier contracts and specs. Each one runs on any model, uses only the data you approve, and ships with permissions, audit and no code.
- Maintenance assistant — answers from equipment manuals and past incident logs
- Quality assistant — helps document and classify defects consistently
- Procurement assistant — searches supplier contracts and part specs in seconds
- Permissions, audit and no code — IT keeps control, teams move fast
Set the assistant's name, model and base instructions.
🤖
* Name
Data Analyst
Description
An AI assistant that helps you analyze and explore your data.
Model
Claude Opus 4.8 (EU)
🇪🇺
Instructions
You are a data analyst assistant. Help users explore, analyze, and understand their data. Write clear SQL queries, explain results, and suggest insights.
Optimize prompt
Conversation starters (2 / 4) Add
Explore tables What tables are available? Show me their schemas.
Quick summary Give me a summary of the most important table.
Notebooks
Governed notebooks for engineers and analysts
More than Jupyter. For the teams that need code: Python, AI cells on any model and interactive widgets in a collaborative, governed environment — with data connected securely and one-click deployment to live apps.
- 5 cell types: Code, Markdown, AI cell, Widgets, Snippets
- AI cells on any model for code and analysis
- Connected data with permissions, not loose copies
- Real-time collaboration with access control
- One-click deployment to governed live apps
Shift Review 
100%
Code Markdown Assistant Widget Snippet
Shift OEE analysis
Compare OEE and downtime across lines and shifts.
Region: All Regions
Results: 8
Apply df = mineo.query("SELECT line, AVG(oee) FROM machine_events GROUP BY line")
px.bar(df, x="line", y="oee", title="Shift OEE")
NA
EU
APAC
LATAM
Assistant
Key insight: North America drives 43% of total revenue. APAC showed the strongest growth at +23% QoQ, driven by expansion in Japan and Australia.
[2]
df.sort_values("revenue", ascending=False).head()
| Region | Revenue | Growth |
|---|---|---|
| North America | $1.8M | +12% |
| Europe | $1.1M | +8% |
| APAC | $820K | +23% |
| LATAM | $480K | -3% |
v12
ProWorker 2 CPU · 6GB
Deploy as App Live Apps
Internal apps your teams use, with permissions
Turn the work into always-on dashboards and applications for the whole plant org. Deploy with Streamlit, Gradio, Dash and 6+ more frameworks — with permissions and access control built in, ready for your stakeholders.
- 9+ Python frameworks: Streamlit, Gradio, Dash, Panel, and more
- Always-on apps with custom domains and 99.9% SLA
- Permissions and access control on every app
- Real-time data from governed data sources
MINEO · Plant dashboard Live
Plant overview
Last updated: 2 minutes ago
Filters Line: Assembly Export
OEE
84%
+3% vs Q3
Throughput
12.4K
+6% vs Q3
Downtime
42 min
-8% vs Q3
OEE by shift FY 2025
$850K Q1
$920K Q2
$1.1M Q3
$1.3M Q4
| Customer | Region | Revenue | Status |
|---|---|---|---|
| Acme Corp | NA | $245K | Active |
| TechFlow GmbH | EU | $182K | Active |
| Sakura Ltd | APAC | $156K | Pending |
| DataBr SA | LATAM | $98K | Active |
plant.mineo.app Powered by Streamlit
Dev Environments
Full VS Code in the cloud, governed
A complete Linux environment in the browser for technical teams. Install any tool — Claude Code, Codex, Gemini CLI — connect governed data sources and collaborate, without code or data leaving your control.
- Full VS Code with extensions and terminal
- Any tool: Claude Code, Codex, Gemini CLI
- Custom Docker images and GPU acceleration
- Connected to governed data sources and Git integration
VS Code — Dev Environment Explorer
src
app.py
pipeline.py
config.yaml
requirements.txt
Dockerfile
plant_dashboard.py
pipeline.py
import streamlit as st
from mineo import DataSource
import plotly.express as px
st.set_page_config(layout="wide")
st.title("Plant Dashboard")
ds = DataSource("plant_db")
df = ds.query("SELECT * FROM machine_events")
# Layout
col1, col2 = st.columns(2)
col1.metric("OEE", f"{df.oee.mean():.1%}")
col2.metric("Downtime", f"{df.downtime_min.sum():,} min")
# Charts
fig = px.bar(df, x="shift", y="oee", color="line")
st.plotly_chart(fig, use_container_width=True)
TERMINAL | zsh
mineo-dev@workspace:~/project$
Successfully installed plotly-5.22 scikit-learn-1.5
Your app is live at https://plant-dashboard.mineo.app
✓ Deployed successfully
Pipelines
Automate AI workflows with control and audit
Chain notebooks and AI steps into production workflows. Schedule with cron, trigger via REST API, and govern every run from one platform — with full history and audit trail.
- Chain notebooks as sequential pipeline steps
- Schedule with cron or trigger via REST API
- Configurable compute resources per pipeline
- Execution history with logs and audit trail
/ Projects / plants / Daily shift rollup General Elements 3 Executions API
Elements
1 extract_machine_events.ipynb
2 compute_oee.ipynb
3 publish_shift_report.ipynb
Resource Config
Worker Environment
PlantWorker 2 cores · 6 GB
Scheduling
Crontab Expression 0 6 * * *
Every day at 06:00
Active
API
Enabled
POST /v1/pipelines/{id}/run
Last Execution Running • -- • Waiting
Why manufacturing leaders choose MINEO
Govern AI across the plant
For a CTO or CISO the value isn't one more tool — it's one governed place where every team's AI is visible, controlled and compliant.
Control cost
AI spend you can actually see
Track AI cost per team and use case, set budgets and route to the right model — no more surprise invoices across departments.
Prove compliance
Audit-ready for industrial regulation
Every AI interaction is logged and traceable, helping you protect trade secrets and IP and evidence NIS2, GDPR and EU AI Act requirements without extra tooling.
Govern all your plant's AI on one platform
Every model, no lock-in, data that never trains third-party models, cost under control and industry-grade compliance — trade secrets, IP, NIS2, GDPR and the EU AI Act.