For data engineers, data leaders and data managers
AIMO automatically analyzes your databases, suggests rich data quality monitors with AI, and tracks them over time – without sending raw data or credentials outside your network. One Docker image, minutes to value.
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Suggested monitors (AI)
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Anomaly detection
Product
Traditional data quality tools demand heavy configuration, ongoing rule maintenance and expensive licenses. AIMO turns database analysis, monitor suggestion and anomaly detection into a single, AI-assisted flow that you can run from one Docker image.
Designed for data engineers, analytics engineers and data leaders who need confidence in their warehouse and operational databases – without building a custom monitoring stack.
AIMO uses modern AI models to inspect your schemas, profile tables and propose monitors. You can inspect, accept and refine every suggestion.
AIMO’s agent runs inside your environment. Credentials are encrypted with a passphrase you control, and no raw data or PII is sent to AIMO’s servers – only carefully designed aggregates and monitor results.
How it works
AIMO compresses what used to be a full project into a single workflow: run the agent, analyze your schema, let AI propose monitors, and train models on your actual data history.
Deploy the prebuilt Docker image into your infrastructure. The agent runs in your network, close to your databases – no firewall gymnastics, no inbound holes, no external access to your DBs.
In the AIMO UI, create database connections. Credentials are encrypted locally with a passphrase that never leaves your environment; only the encrypted version is stored on the AIMO side, so we can never log in to your databases directly.
Choose tables you care about, and let the agent profile them locally. AIMO collects schema details and non-sensitive statistics, but never raw rows or PII. You can inspect exactly what leaves your environment.
AIMO uses AI to propose a comprehensive set of monitors for each table: null checks, uniqueness, ranges, categorical sets, text patterns, similarity and custom SQL monitors. Over time, a model learns what “normal” looks like and alerts when behavior drifts.
Security & privacy
AIMO is deliberately architected so that your most sensitive data never leaves your environment. The agent is open to inspection, and the data that flows to AIMO is minimized to what is required for monitoring.
Features
AIMO combines schema monitoring, rich data quality checks, AI-powered analysis and flexible alerting into one cohesive system that fits into almost any budget.
Automatically detect and track schema changes over time – new or removed tables and columns, type changes, constraints and more.
Use a rich library of monitor types to capture the shape, health and expectations of your data.
Let AI inspect your schemas and table statistics to suggest monitors and parameters that make sense for your business.
Connect a range of databases through SQLAlchemy and DuckDB, from warehouses to operational stores.
Route alerts to the channels your teams already monitor and tune sensitivity to what matters.
All configurations and results are stored in a local SQLite storage database, designed to be inspectable and easy to migrate.
Pricing
No tiers, no usage-based surprises. Pay for the tables you monitor. Start with one for free, then expand as you prove value.
Free
Everything you need to validate AIMO on a single critical table.
20€
per additional table / month
Scale AIMO across the datasets that drive your business.
Need a custom deployment model or have special compliance requirements? Get in touch and we’ll design a setup that works for your environment.
Examples
AIMO is versatile by design. Anywhere you have important tables and changing schemas, it can protect you from silent failures.
Monitor key warehouse tables like orders,
events or subscriptions for row-count,
null and range anomalies.
Run monitors against production OLTP databases without shipping data out of your environment.
Use the AIMO Python library to trigger monitors from your ETL and orchestration jobs exactly when new data lands.
FAQ
You run a single Docker image in your environment, connect your database using encrypted credentials, pick a table and run the analysis. Within minutes you’ll see suggested monitors and initial results in the UI.
AIMO builds on SQLAlchemy and DuckDB, so it works with PostgreSQL, MySQL, SQLite and other engines supported by those drivers. If you have a special case, you can extend support via AIMO’s Python library.
AIMO profiles your tables and feeds schema + summary statistics into AI models that propose monitor types and reasonable default thresholds. You can accept, modify or disable any monitor; the system refines parameters over time from actual data.
Yes. The agent is designed to avoid PII and raw values by default, and you can inspect both the code and the payloads leaving your network. If your policies require extra restrictions, you can tighten them further.
Many teams start with daily or hourly runs on key tables. Using the Python library, you can also trigger monitors from your existing pipelines, so checks run immediately after new data arrives.
Start with a single critical table using the free tier. Once you’re comfortable with how AIMO behaves in your environment, gradually add more tables until your key pipelines are fully covered.
Run the AIMO agent, connect a single table for free, and see how much more confident your team feels shipping changes.