Powered by MiMo V2.5 Pro

Ask Questions. Get Insights. Ship Decisions.

Natural language to SQL in seconds. No SQL skills required. MiMo Analytics turns your data warehouse into a conversation.

2,400
Queries per Second
97%
SQL Accuracy
340
Data Connectors
42ms
Avg Query Time

Natural Language → SQL

Ask any question in plain English, get instant visualizations

$14.2M
Total ARR
23%
MoM Growth
847
Active Accounts
$16.7K
Avg Deal Size

Revenue by Quarter (2025)

Revenue Mix

SaaS 35%API 25%Enterprise 20%Other 20%
⏱️ 38ms🧠 4 joins, 2 aggregations📊 96.4% confidence

Enterprise Data Platform

Six AI-powered modules

🧠

Natural Language to SQL

MiMo V2.5 Pro understands complex business questions. Supports joins, CTEs, window functions.

📊

Auto-Visualization

Automatically picks the best chart type. Custom themes, export to PDF/PNG.

🔗

340+ Data Connectors

Snowflake, BigQuery, PostgreSQL, MySQL, MongoDB, S3, Stripe, HubSpot. One-click OAuth.

🔒

Row-Level Security

Per-user data access policies at query time. SSO, audit logs, SOC2 built-in.

⚡

Real-Time Dashboards

Sub-second refresh. Scheduled reports via email/Slack. Alert on thresholds.

🤖

Proactive Insights

AI surfaces anomalies and trends. Root cause analysis included.

Built for Every Team

From C-suite to frontline

Revenue

Sales Analytics

"Show me deals at risk this quarter" — instant pipeline analysis.

3.2x
Faster decisions
89%
Adoption rate
$2.1M
Revenue saved
Product

Product Analytics

"Which feature has highest retention impact?" — cohort analysis in seconds.

60%
Less analyst time
4.8K
Queries/day
23min
Avg insight time
Finance

Financial Reporting

"Reconcile revenue vs. bookings" — automated multi-source analysis.

$340K
Audit savings
94%
Auto-reconcile
4hrs
Month-end close

How It Works

1

Ask

Natural language question

2

Generate

MiMo writes optimized SQL

3

Visualize

Auto-picks best chart

4

Share

Export, schedule, embed

# MiMo Analytics — Python SDK
from mimo_analytics import QueryEngine

engine = QueryEngine(api_key="mak-...")
result = engine.ask("Top 5 customers by ARR")

print(result.sql) # Generated SQL
print(result.chart) # Chart recommendation
print(result.insight) # AI summary