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.
Transportation & Logistics
All your logistics AI, governed and compliant
Centralize how every transportation and logistics team uses AI on one platform: any model, no lock-in, operational data that never trains third-party models, cost under control, and NIS2, GDPR and EU AI Act compliance by design.
See how it worksShadow AI under control
Replace scattered, ungoverned chatbot use with one platform — <strong>permissions and audit included</strong>
Your data stays yours
Operational data <strong>never trains third-party models</strong>; you control residency and access
Logistics-grade compliance
<strong>NIS2, GDPR and the EU AI Act</strong> — traceability on every AI interaction
What your teams build, governed
One governed platform for every logistics team
Dispatch, network planning, operations, customer ops, IT — 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 operational data 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 · Fleet analysis fleet_dbdeliveries.csv
Understanding intent…
Querying fleet_db
Auto-generated SQL
Calling tms-mcp…
MCP tool call
{ "tool": "tms-mcp", "action": "create_task", "summary": "Reassign 2 trucks from the north corridor" }
via tms-mcp · just now
Corporate assistants
Corporate assistants for every team, not Shadow AI
Give each department its own assistant — fleet operations, delivery support, warehouse — shared across the company, on any model, with permissions, team memory and skills. They work like agents: they check shipment status, read operating manuals and open tasks in your tools, always within the access of the person asking.
- Fleet operations: answers drivers and dispatch on routes and SOPs
- Delivery support: resolves where-is-my-shipment questions from tracking data
- Warehouse: guides staff through operating procedures
- Team memory: assistants read and update a knowledge base of depot rules, customer delivery instructions and recurring incidents
- 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 operation's know-how, as reusable skills
Turn the way your operation works into skills: the incident procedure, the delay notices to customers, the claim format for carriers. 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
- Team skills for dispatch, warehouse 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 1.6k
/incident-procedure
Steps and categories for delivery incidents
- Project 900
/delay-notice
Templates for delay and rescheduling notices
- Project 1.3k
/carrier-claims
Evidence and format for claims to carriers
- Organization 1.1k
/depot-rules
Loading hours and access rules per depot
- Personal 450
/my-route-notes
My notes on tricky drop-offs
incident-procedure delay-notice
Draft the incident report for this late delivery and notify the customer
Only the skills you pick travel with this message.
Artifacts
Apps built from a conversation, organized and governed
Ask for a late-deliveries tracker or a depot capacity calculator 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 fleet and delivery data through MINEO, server-side
Build a late-deliveries dashboard by corridor that the ops leads can open.
Querying fleet_db · 3 corridors
index.html · app.js · styles.css
Done — version 3 adds the delay causes you asked for. It reads live from fleet_db through MINEO, so it stays up to date.
Show which carriers account for most delays…
v1v2v3 Restore
Publish mineo.app/a/late-deliveries
Network · Weekly
Late deliveries by corridor
Late deliveries 240
Open exceptions 18
On-time rate 94%
- West 120
- South 72
- East 48
Notebooks
Governed notebooks for analysts and planners
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
Weekly Network Review 
100%
Code Markdown Assistant Widget Snippet
Weekly on-time performance
Compare delivery performance across corridors and carriers.
Region: All Regions
Results: 8
Apply df = mineo.query("SELECT corridor, AVG(on_time) FROM deliveries GROUP BY corridor")
px.bar(df, x="corridor", y="on_time", title="On-time by corridor")
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 control towers and applications for the whole logistics 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 · Control tower Live
Fleet control tower
Last updated: 2 minutes ago
Filters Scope: delayed Export
On-time deliveries
94.2%
+1.7pp vs Q3
Open exceptions
18
-4 vs Q3
Idle capacity
7.8%
-1.2pp vs Q3
Exceptions by corridor 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 | $98K | Active |
ops.mineo.app Powered by Streamlit
Dev Environments
Full VS Code in the cloud, governed
A complete Linux environment in the browser for technical teams. Install any tool — Claude Code, Codex, Gemini CLI — connect governed data sources and collaborate, without code or data leaving your control.
- Full VS Code with extensions and terminal
- Any tool: Claude Code, Codex, Gemini CLI
- Custom Docker images and GPU acceleration
- Connected to governed data sources and Git integration
VS Code — Dev Environment Explorer
src
app.py
pipeline.py
config.yaml
requirements.txt
Dockerfile
fleet_dashboard.py
pipeline.py
import streamlit as st
from mineo import DataSource
import plotly.express as px
st.set_page_config(layout="wide")
st.title("Fleet Control Tower")
ds = DataSource("fleet_db")
df = ds.query("SELECT * FROM deliveries")
# Layout
col1, col2 = st.columns(2)
col1.metric("On-time %", f"{df.on_time.mean():.1%}")
col2.metric("Exceptions", f"{df.exception.sum():,}")
# Charts
fig = px.bar(df, x="corridor", y="on_time", color="carrier")
st.plotly_chart(fig, use_container_width=True)
TERMINAL | zsh
mineo-dev@workspace:~/project$
Successfully installed plotly-5.22 scikit-learn-1.5
Your app is live at https://ops.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 yesterday's late deliveries and their causes
/ Projects / logistics / Daily network refresh General Elements 4 Executions API
Elements
1 extract_deliveries.ipynb
2 compute_on_time.ipynb
3 publish_network_review.ipynb
4 Sales assistant: summarize the day Assistant step
Resource Config
Worker Environment
NetworkWorker 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 logistics leaders choose MINEO
Govern AI across the operation
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 logistics regulation
Every AI interaction is logged and traceable, helping you evidence NIS2, GDPR and EU AI Act requirements without extra tooling.
Govern all your logistics AI on one platform
Every model, no lock-in, operational data that never trains third-party models, cost under control and logistics-grade compliance — NIS2, GDPR and the EU AI Act.