Govern
From Shadow AI to governed assistants
Bring scattered ChatGPT use into one platform: the same models teams already like, now with <strong>permissions, audit and customer data that never trains third-party models</strong>.
Marketing & Sales
All your marketing and sales AI, governed and compliant
Centralize how every marketing and sales team uses AI on one platform: any model, no lock-in, customer and CRM data that never trains third-party models, cost under control, and GDPR, ePrivacy 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
Customer and CRM data <strong>never trains third-party models</strong>; you control residency and access
Marketing-grade compliance
<strong>GDPR, ePrivacy and the EU AI Act</strong> — traceability on every AI interaction with customer data
What your teams build, governed
One governed platform for every revenue team
Demand gen, marketing ops, sales ops, RevOps, customer marketing — each team builds what it needs with <strong>any AI model</strong>, while you keep one place to set permissions, control cost and prove compliance.
Conversational AI
Conversational AI on your data, under your control
Give marketing and sales teams a governed way to chat with any AI model over approved data and tools (MCP) — instead of pasting customer lists into public chatbots. Every conversation runs with permissions and a full audit trail, and customer data stays private.
- Any model — switch without re-platforming
- Acts through approved tools (MCP), with guardrails
- Permissions and audit on every conversation
- Customer data never leaves your governance
Threads · Revenue analysis crm_dbcampaigns.csv
Understanding intent…
Querying crm_db
Auto-generated SQL
Calling salesforce-mcp…
MCP tool call
{ "tool": "salesforce-mcp", "action": "create_task", "summary": "Create follow-up tasks for the 15 stalled enterprise accounts" }
via salesforce-mcp · just now
Assistants
Real assistants for marketing & sales, not Shadow AI
Build the assistants your teams actually need — a copywriter that drafts on-brand campaigns, a CRM assistant that writes follow-up emails from deal context, an enablement bot that answers reps' product and pricing questions. Each runs on any model, with only approved data, permissions, audit and no code.
- Brand copywriter — drafts campaigns and ads in your tone of voice
- CRM follow-up assistant — writes next-step emails from each deal's context
- Sales enablement bot — answers reps' product and pricing questions instantly
- No code — only approved data, with permissions and audit
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 ops teams
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
Quarterly Pipeline Review 
100%
Code Markdown Assistant Widget Snippet
Quarterly pipeline analysis
Compare pipeline evolution across segments and campaign sources.
Region: All Regions
Results: 8
Apply df = mineo.query("SELECT segment, SUM(amount) FROM opportunities GROUP BY segment")
px.bar(df, x="segment", y="amount", title="Q4 Pipeline")
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 revenue 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 · Revenue dashboard Live
Revenue overview
Last updated: 2 minutes ago
Filters Segment: Enterprise Export
Pipeline
$8.4M
+24% vs Q3
Qualified leads
2,318
+17% vs Q3
Win rate
27%
-2% vs Q3
Pipeline by quarter 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 | $98K | Active |
revenue.mineo.app Powered by Streamlit
Dev Environments
Full VS Code in the cloud, governed
A complete Linux environment in the browser for technical teams. Install any tool — Claude Code, Codex, Gemini CLI — connect governed data sources and collaborate, without code or data leaving your control.
- Full VS Code with extensions and terminal
- Any tool: Claude Code, Codex, Gemini CLI
- Custom Docker images and GPU acceleration
- Connected to governed data sources and Git integration
VS Code — Dev Environment Explorer
src
app.py
pipeline.py
config.yaml
requirements.txt
Dockerfile
revenue_dashboard.py
pipeline.py
import streamlit as st
from mineo import DataSource
import plotly.express as px
st.set_page_config(layout="wide")
st.title("Revenue Dashboard")
ds = DataSource("revenue_db")
df = ds.query("SELECT * FROM opportunities")
# Layout
col1, col2 = st.columns(2)
col1.metric("Pipeline", f"${df.amount.sum():,.0f}")
col2.metric("Deals", f"{df.opportunity_id.nunique():,}")
# Charts
fig = px.bar(df, x="month", y="amount", color="segment")
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://revenue-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 / revenue / Weekly revenue review General Elements 3 Executions API
Elements
1 extract_pipeline.ipynb
2 score_leads.ipynb
3 publish_weekly_review.ipynb
Resource Config
Worker Environment
RevenueWorker 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 marketing and sales leaders choose MINEO
Govern AI across revenue teams
For a CTO or CISO the value isn't one more tool — it's one governed place where every revenue 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 — <strong>no more surprise invoices</strong> across marketing and sales.
Prove compliance
Audit-ready for marketing and sales regulation
Every AI interaction is logged and traceable, helping you evidence <strong>GDPR, ePrivacy consent and EU AI Act</strong> requirements on customer data without extra tooling.
Govern all your marketing and sales AI on one platform
Every model, no lock-in, customer data that never trains third-party models, cost under control and marketing-grade compliance — GDPR, ePrivacy and the EU AI Act.