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.
Legal
All your firm's AI, governed and privileged
Centralize how every legal team uses AI on one platform: any model, no lock-in, privileged data that never trains third-party models, cost under control, and attorney-client privilege, 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 privileged
Privileged and personal data never trains third-party models; you control residency and access
Legal-grade compliance
Attorney-client privilege, GDPR and the EU AI Act — traceability on every AI interaction
What your teams build, governed
One governed platform for every legal team
Litigation, M&A, IP, contracts, legal ops, 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 matters or contracts into public chatbots. Every conversation runs with permissions and a full audit trail, and privileged data stays private.
- Any model — switch without re-platforming
- Acts through approved tools (MCP), with guardrails
- Permissions and audit on every conversation
- Privileged data never leaves your governance
Threads · Matter analysis matters_dbbilling.csv
Understanding intent…
Querying matters_db
Auto-generated SQL
Calling clio-mcp…
MCP tool call
{ "tool": "clio-mcp", "action": "create_task", "summary": "Open a task in Clio for the M&A team to review billable hours" }
via clio-mcp · just now
Assistants
Real assistants for legal work, not Shadow AI
Build the assistants your firm actually needs: a contract-review assistant that flags risky or missing clauses, a legal-research assistant over your matter archive, or a client-intake assistant that drafts a first case summary. Pick any model, connect only approved data and tools (MCP), and publish without code — with permissions and a full audit trail.
- Contract review: flags risky or missing clauses before signing
- Legal research over your matter archive, with sources cited
- Client intake: drafts a first summary of each new case
- Every assistant runs 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 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 legal analysts and ops
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
Contract risk review 
100%
Code Markdown Assistant Widget Snippet
Contract risk review with GPT-5.4
Score liability across active contracts using OpenAI structured outputs.
Region: All Regions
Results: 8
Apply from openai import OpenAI
from pydantic import BaseModel
class Risk(BaseModel): level: Literal["low", "medium", "high", "critical"]; flags: list[str]
df = mineo.query("SELECT counterparty, body, exposure FROM contracts WHERE active")
df["risk"] = df.body.apply(lambda t: OpenAI().responses.parse(model="gpt-5.4", input=t, text_format=Risk).output_parsed)
Low
Medium
High
Critical
Assistant
GPT-5.4 flagged 3 active contracts as Critical, all linked to the same vendor — playbook review needed.
[2]
df[df.risk != "low"].sort_values("exposure", ascending=False).head()
| Counterparty | Exposure | Risk |
|---|---|---|
| Acme Corp | $4.2M | Critical |
| Globex Inc. | $2.1M | High |
| Initech | $890K | Medium |
| Sterling LLP | $480K | Low |
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 legal org. Deploy with Streamlit, Gradio, Dash and 6+ more frameworks — with permissions and access control built in, ready for partners and associates.
- 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 · Practice dashboard Live
Firm overview
Last updated: 2 minutes ago
Filters Practice: M&A Export
Revenue
$5.5M
+19% vs Q3
Active matters
428
+8% vs Q3
Realization
92%
+2% vs Q3
Revenue 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 | $98K | Active |
practice.mineo.app Powered by Streamlit
Dev Environments
Full VS Code in the cloud, governed
A complete Linux environment in the browser for technical teams. Install any tool — Claude Code, Codex, Gemini CLI — connect governed data sources and collaborate, without code or data leaving your control.
- Full VS Code with extensions and terminal
- Any tool: Claude Code, Codex, Gemini CLI
- Custom Docker images and GPU acceleration
- Connected to governed data sources and Git integration
VS Code — Dev Environment Explorer
src
app.py
pipeline.py
config.yaml
requirements.txt
Dockerfile
matter_dashboard.py
pipeline.py
import streamlit as st
from mineo import DataSource
import plotly.express as px
st.set_page_config(layout="wide")
st.title("Matter Dashboard")
ds = DataSource("matters_db")
df = ds.query("SELECT * FROM matters")
# Layout
col1, col2 = st.columns(2)
col1.metric("Revenue", f"${df.amount.sum():,.0f}")
col2.metric("Matters", f"{df.matter_id.nunique():,}")
# Charts
fig = px.bar(df, x="practice_area", y="amount", color="partner")
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://matter-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 / legal / Nightly contract ingestion General Elements 3 Executions API
Elements
1 fetch_new_contracts.ipynb
2 extract_clauses.ipynb
3 publish_contract_index.ipynb
Resource Config
Worker Environment
LegalWorker 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 legal leaders choose MINEO
Govern AI across the firm
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 practice groups.
Prove compliance
Audit-ready for legal regulation
Every AI interaction is logged and traceable, helping you evidence attorney-client privilege, GDPR and EU AI Act requirements without extra tooling.
Govern all your firm's AI on one platform
Every model, no lock-in, data that never trains third-party models, cost under control and legal-grade compliance — attorney-client privilege, GDPR and the EU AI Act.