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.
Energy & Utilities
All your utility's AI, governed and compliant
Centralize how every energy team uses AI on one platform: any model, no lock-in, operational data that never trains third-party models, cost under control, and NIS2, critical infrastructure and GDPR compliance 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
Operational data never trains third-party models; you control residency and access
Sector-grade compliance
NIS2, critical infrastructure and GDPR — traceability on every AI interaction
What your teams build, governed
One governed platform for every energy team
Grid operations, demand forecasting, asset management, trading, compliance — 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 telemetry into public chatbots. Every conversation runs with permissions and a full audit trail, and operational data stays private.
- Any model — switch without re-platforming
- Acts through approved tools (MCP), with guardrails
- Permissions and audit on every conversation
- Operational data never leaves your governance
Threads · Grid analysis grid_dbmeter_readings.csv
Understanding intent…
Querying grid_db
Auto-generated SQL
Calling slack-mcp…
MCP tool call
{ "tool": "slack-mcp", "action": "create_task", "summary": "Alert the grid ops team in Slack" }
via slack-mcp · just now
Corporate assistants
Corporate assistants for every area, not Shadow AI
Give each area and department its own assistant — field operations, billing support, regulatory reporting — shared across the company, on any model, with permissions, team memory and skills. They work like agents: they search procedures and asset history, read documents and open work orders in your tools, always within the access of the person asking.
- Field operations: instant answers from procedures and safety manuals
- Billing support: resolves customer queries faster at the service desk
- Regulatory reporting: drafts recurring filings from your own data
- Team memory — assistants read and update a knowledge base of known asset issues, workarounds and lessons from past outages
- Acts as an agent through approved tools (MCP), with permissions, audit and cost limits
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 Fable 5.1 (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.
Skills
Your company's know-how, as reusable skills
Turn the way your company works into skills: the outage report template, the root-cause format for grid incidents, the plain-language tariff explanations. Anyone picks them with / while writing, and the assistant only loads them when they're needed.
- Company-wide skills shared from one project and edited in one place
- Area skills for grid operations, trading or customer service, visible only to that team
- Pick them while typing with / — only what you pick travels with the message
- The assistant can write a new skill from an answer that worked
Search skills…
AllOrganizationProjectPersonal
- Organization 1.8k
/outage-report-template
Structure and fields for regulatory outage reports
- Project 1.4k
/incident-root-cause
Root-cause format for grid incidents
- Organization 2.3k
/safety-manual-index
Where each procedure lives in the safety manuals
- Project 1.1k
/tariff-explainer
Plain-language tariff explanations for customers
- Personal 380
/my-shift-handover
My format for shift handover notes
outage-report-template incident-root-cause
Draft this month's outage report for the regulator using our template
Only the skills you pick travel with this message.
Artifacts
Apps built from a conversation, organized and governed
Ask for a work-order tracker or a substation load view and the assistant builds it as an artifact — a live app. Each one stays with its conversation, keeps every version and is published under its own URL with permissions, ready to become a team tool.
- Lives with the conversation that created it — no loose files
- A version per iteration; restore or republish any of them
- Published under its own URL, with permissions
- Export a copy to the project when it becomes a team tool
- Reads grid and maintenance data through MINEO, server-side
Build a dashboard of open work orders by region that the operations managers can open.
Querying grid_db · 3 regions
index.html · app.js · styles.css
Done — version 3 adds the SLA status you asked for. It reads live from grid_db through MINEO, so it stays up to date.
Highlight orders past their SLA…
v1v2v3 Restore
Publish mineo.app/a/open-work-orders
Maintenance · Q4
Open work orders by region
Open work orders 486
Overdue 37
Closed within SLA 94%
- North 204
- Central 151
- South 131
Notebooks
Governed notebooks for analysts and engineers
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
Demand Forecast 
100%
Code Markdown Assistant Widget Snippet
Daily demand forecasting
Compare load evolution across regions and substations.
Region: All Regions
Results: 8
Apply df = mineo.query("SELECT region, SUM(load_mw) FROM meter_readings GROUP BY region")
px.line(df, x="hour", y="load_mw", title="Demand Forecast")
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 energy org. Deploy with Streamlit, Gradio, Dash and 6+ more frameworks — with permissions and access control built in, ready for your operators.
- 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 · Grid dashboard Live
Grid overview
Last updated: 2 minutes ago
Filters Region: North Export
Peak load
4.2 GW
+6% vs Q3
Active meters
182,540
+2% vs Q3
Outage rate
0.8%
-4% vs Q3
Demand by hour FY 2026
$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 | $98K | Active |
grid.mineo.app Powered by Streamlit
Dev Environments
Full VS Code in the cloud, governed
A complete Linux environment in the browser for technical teams. Install any tool — Claude Code, Codex, Gemini CLI — connect governed data sources and collaborate, without code or data leaving your control.
- Full VS Code with extensions and terminal
- Any tool: Claude Code, Codex, Gemini CLI
- Custom Docker images and GPU acceleration
- Connected to governed data sources and Git integration
VS Code — Dev Environment Explorer
src
app.py
pipeline.py
config.yaml
requirements.txt
Dockerfile
grid_dashboard.py
pipeline.py
import streamlit as st
from mineo import DataSource
import plotly.express as px
st.set_page_config(layout="wide")
st.title("Grid Dashboard")
ds = DataSource("grid_db")
df = ds.query("SELECT * FROM meter_readings")
# Layout
col1, col2 = st.columns(2)
col1.metric("Peak load", f"{df.load_mw.max():,.1f} MW")
col2.metric("Meters", f"{df.meter_id.nunique():,}")
# Charts
fig = px.line(df, x="hour", y="load_mw", color="region")
st.plotly_chart(fig, use_container_width=True)
TERMINAL | zsh
mineo-dev@workspace:~/project$
Successfully installed plotly-5.22 scikit-learn-1.5
Your app is live at https://grid-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
- Assistants as scheduled steps — every morning, a summary of overnight grid alarms and open work orders
/ Projects / grid / Daily demand forecast General Elements 4 Executions API
Elements
1 load_meter_data.ipynb
2 run_forecast.ipynb
3 publish_demand_alerts.ipynb
4 Sales assistant: summarize the day Assistant step
Resource Config
Worker Environment
ForecastWorker 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 energy leaders choose MINEO
Govern AI across the utility
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 energy & utilities regulation
Every AI interaction is logged and traceable, helping you evidence NIS2, critical infrastructure and GDPR requirements without extra tooling.
Govern all your utility's AI on one platform
Every model, no lock-in, data that never trains third-party models, cost under control and sector-grade compliance — NIS2, critical infrastructure and GDPR.