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.
Education
All your institution's AI, governed and compliant
Centralize how every education team uses AI on one platform: any model, no lock-in, institutional data that never trains third-party models, cost under control, and GDPR and EU AI Act 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
Institutional data — including minors' records — never trains third-party models; you control residency and access
Education-grade compliance
GDPR and the EU AI Act — traceability on every AI interaction
What your teams build, governed
One governed platform for every education team
Faculty, registrar, IT, research, student services — 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 student records into public chatbots. Every conversation runs with permissions and a full audit trail, and institutional data stays private.
- Any model — switch without re-platforming
- Acts through approved tools (MCP), with guardrails
- Permissions and audit on every conversation
- Institutional data never leaves your governance
Threads · Outcomes analysis students_dbcourses.csv
Understanding intent…
Querying students_db
Auto-generated SQL
Calling sis-mcp…
MCP tool call
{ "tool": "sis-mcp", "action": "create_task", "summary": "Enroll the at-risk students in the tutoring program" }
via sis-mcp · just now
Assistants
Real assistants for your institution, not Shadow AI
Build assistants people actually use: a student-support assistant that answers questions from course materials, an admissions Q&A assistant for applicants, and a staff assistant that drafts routine administrative replies. Each one runs on any model, only approved data, with permissions, audit and no code.
- Student-support assistant — answers from course materials and FAQs, 24/7
- Admissions Q&A assistant — guides applicants through requirements and deadlines
- Staff assistant — drafts and answers routine administrative requests
- Every assistant: approved data only, permissions, audit, no code
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 analysts and researchers
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
Student Outcomes Lab 
100%
Code Markdown Assistant Widget Snippet
Termly completion analysis
Compare completion across cohorts and courses.
Region: All Regions
Results: 8
Apply df = mineo.query("SELECT cohort, AVG(completion_rate) FROM classes GROUP BY cohort")
px.bar(df, x="cohort", y="completion_rate", color="avg_score")
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 institution. 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 · Learning dashboard Live
Programs overview
Last updated: 2 minutes ago
Filters Scope: intro courses Export
Active students
3,842
+7% vs Q3
Completion rate
76%
+4pp vs Q3
At-risk cohorts
4
-1 vs Q3
Completion by cohort 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 | $98K | Active |
learning.mineo.app Powered by Streamlit
Dev Environments
Full VS Code in the cloud, governed
A complete Linux environment in the browser for technical teams. Install any tool — Claude Code, Codex, Gemini CLI — connect governed data sources and collaborate, without code or data leaving your control.
- Full VS Code with extensions and terminal
- Any tool: Claude Code, Codex, Gemini CLI
- Custom Docker images and GPU acceleration
- Connected to governed data sources and Git integration
VS Code — Dev Environment Explorer
src
app.py
pipeline.py
config.yaml
requirements.txt
Dockerfile
learning_dashboard.py
pipeline.py
import streamlit as st
from mineo import DataSource
import plotly.express as px
st.set_page_config(layout="wide")
st.title("Learning Dashboard")
ds = DataSource("students_db")
df = ds.query("SELECT * FROM classes")
# Layout
col1, col2 = st.columns(2)
col1.metric("Completion", f"{df.completion.mean():.1%}")
col2.metric("Students", f"{df.student_id.nunique():,}")
# Charts
fig = px.bar(df, x="cohort", y="completion", color="course")
st.plotly_chart(fig, use_container_width=True)
TERMINAL | zsh
mineo-dev@workspace:~/project$
Successfully installed plotly-5.22 scikit-learn-1.5
Your app is live at https://learning.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 / education / Termly outcomes refresh General Elements 3 Executions API
Elements
1 extract_enrolments.ipynb
2 compute_completion.ipynb
3 publish_term_report.ipynb
Resource Config
Worker Environment
EduWorker 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 education leaders choose MINEO
Govern AI across the institution
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 education regulation
Every AI interaction is logged and traceable, helping you evidence GDPR and the EU AI Act requirements without extra tooling.
Govern all your institution's AI on one platform
Every model, no lock-in, data that never trains third-party models, cost under control and education-grade compliance — GDPR and the EU AI Act.