Schema to monitors · rows stay local

Fully AI-automated data quality monitoring for your tables.
Raw data never leaves.

Q: What does initial setup look like?

Sign in, register in the UI, run register once, add connections in the agent CLI, start the agent, then analyze and onboard tables in the UI. After accept, monitors are generated and history is backfilled—time scales with data size.

Q: Which databases are supported?

PostgreSQL, MySQL, SQLite, Snowflake, DuckDB, and more via SQLAlchemy drivers. See the docs for the current list.

Q: Who creates the monitors?

You pick which tables to onboard and accept. AIMO uses AI to generate the monitor types, columns, and bounds from its analysis of your schema and data.

Q: What actually leaves my network?

Routine monitoring does not export bulk raw rows. The agent sends metadata and aggregates—counts, grouped results, analysis payloads—for dashboards and models.

Q: How often do monitors run?

On your schedule, AIMO dispatches monitor jobs to the agent, using the same bounded queries as artifact validation.

Q: How do we scale beyond the trial?

After month one it is €10 per monitored table per month (excl. tax). Add or remove tables in the UI; billing updates next cycle.

AI-generated monitors AIMO reads each table’s schema and a profile of its data, then generates monitors built for that table—and learns what “normal” looks like to flag outliers. All automatically.
Raw data stays local The agent runs beside your databases. Only aggregates and metadata leave—never raw rows or PII. Outbound-only; your SQL port is never exposed to us.
Live in minutes Register the agent, add a connection in the CLI, onboard tables. 3 tables free your first month, then €10 / table / month (excl. tax). See pricing

Catch bad loads, silent drift, and schema surprises at the source—before they train your AI models or reach the dashboards, board decks, and customer-facing numbers built on the same tables. Caught early it’s a quiet fix; surfacing later as a wrong prediction or a blown report, it’s a scramble. Walk through setup ↓

View demo Start free Talk to us

PostgreSQL MySQL SQLite Snowflake DuckDB + connection URL

In active pilots with data teams EU-based · operated from Finland GDPR-ready · signed DPA available The agent’s database code is open to inspect

Why it matters

Bad data is a business problem first.

Bad numbers don’t stay in the warehouse. They train your AI models, feed your dashboards, and end up in the board deck, the invoice, and the decision. AIMO catches them while they’re still cheap to fix—before a long pipeline buries the error where no one thinks to look.

AI you can trust

Models learn whatever you feed them

Training a model—or feeding any AI pipeline—takes accurate data, and those pipelines run through many steps before a result comes out. A bad input rarely trips an alarm; it quietly skews what the model learns and resurfaces as a confidently wrong answer no one can trace back. AIMO checks the data at the source, so what you build on it holds up.

Confident decisions

Numbers leadership can trust

Every dashboard, forecast, and board report is only as reliable as the tables behind it. AIMO flags broken data before it shapes a decision—so you’re not steering on a figure that quietly went wrong last Tuesday.

Fewer fire drills

Catch it before customers do

A bad batch caught at the source is a quiet fix. The same error in a customer invoice or a quarterly report is an all-hands scramble—and a dent in trust. AIMO moves the catch upstream of your reputation.

Safe to adopt

No new exposure to sign off on

Most monitoring tools mean handing a vendor your data. AIMO doesn’t: raw rows never leave your environment, so there’s nothing new to leak and far less for security and legal to review before you say yes.

See it in action

Your data’s health, at a glance.

AIMO overview dashboard: a health score of 5 of 9 tables healthy, a health timeline, a per-table status list, and a detail panel of monitor results for the selected table. — Overview — see which tables are healthy and which need attention, with monitor detail one click away.

AIMO monitors view for a table: a list of generated monitors with their types and severities, and a selected monitor's time-series chart comparing actual values against a learned median band. — Every generated monitor for a table, each with its own outlier-aware time series.

Schema monitoring

Schema-aware from connect to monitors.

Discovery for onboarding; ongoing tracking as pipelines and migrations change your tables.

− user_score INT

+ loyalty_tier VARCHAR(16)

~ order_total NUMERIC → DECIMAL(12,4)

A generated monitor in AIMO tracking the average EURIBOR rate: a chart plots the actual value against a learned median band over time, alongside the monitor's plain-language reasoning. — A generated monitor, with the reasoning AIMO used and an outlier band learned from your history.

AI-assisted monitoring

Monitors generated to fit each table.

You choose which tables to onboard. AIMO generates the monitors—types, columns, and bounds—from its analysis of your schema and data, and explains its reasoning. Each one is a typed, validated definition that runs as a bounded query against your database.

Privacy by design

Aggregates cross the wire—not bulk rows.

Monitoring sends summaries and monitor outputs—counts, grouped results, schema fingerprints—not table copies. Outlier models run on those series in the cloud, while the rows themselves never move.

Architecture

Built so raw rows can’t leave.

One outbound-only agent beside your databases. AIMO dispatches typed jobs; the agent runs bounded queries locally and returns aggregates. There is no path from our cloud to your SQL port.

The full picture—agent identity, encryption, and job lifecycle—is in the architecture docs →

Capabilities

Trust the data your stack depends on.

Null

Unexpected nulls

Surprise nulls in critical columns—caught immediately.

Range

Numeric bounds

Flags values outside assigned min/max; outlier detection spots unusual count behavior over time.

SQL aggregate

KPIs & cross-column checks

When built-ins aren’t enough, SQLQueryMonitor runs one custom aggregate in the same grouped shell—bounded, not a free SQL session.

Alerting

Alerts where you’ll see them

Email alerts today, routed by severity—with more destinations added regularly.

Email More channels soon

Multi-database

Wherever data lives

SQLAlchemy-backed—broad engine support; extend with drivers.

PostgreSQL MySQL SQLite + more

Jobs

Known operations only

Fixed job types only—analyze, monitors, validate, test connections. No arbitrary cloud code beside your data.

3 tables free for 1 month · then €10 / table / month (excl. tax)

See pricing →

Security & privacy

Security-first from day one.

Credentials via agent CLI, encrypted before upload. DB access decrypts on the agent—not in browser or plaintext in our API.

Data stays in your environment

The agent queries your databases from inside your own network. Only aggregates and monitor results leave—never raw rows or PII.

Ciphertext at rest

Secrets encrypted on the agent before upload. We store ciphertext; agent decrypts only for jobs beside your data.

Agent identity (Ed25519 + JWT)

Per-agent keypair; cloud holds the public key. Agent proves possession for short-lived JWTs on REST and job socket—no static API password.

Typed jobs, typed monitors

Jobs deserialize to known models. SQL on your side comes from validated monitor definitions—not free-form strings in the envelope.

Passwordless sign-in

Human access to the web UI uses passkeys (WebAuthn) only—there is no password to phish, reuse, or leak.

EU-based & GDPR-ready

Operated from Finland. A signed Data Processing Agreement, named sub-processors, and your GDPR rights are documented in Legal.

Where it fits

Where your critical tables live.

Warehouse fact tables

Track orders, events, subscriptions—drift, null spikes, range issues. Catch bad loads before dashboards fail.

Production OLTP databases

Watch keys, status distributions, and schema changes from deploys—and catch them before they corrupt downstream reports. All without exporting rows.

After pipelines land data

Run checks right after each load, so a bad batch is caught at the source—not three dashboards later.

Why AIMO

What you get.

AI-generated monitors per table — built from each table’s schema and a profile of its data
Raw data stays local — only aggregates & metadata leave; the agent is outbound-only
Outliers & alerts on monitor series — behavior shifts, not just one-off bad rows
Minutes to live — register, add a connection in the CLI, onboard; bounded jobs only
3 tables free your first month, then €10 / table / month (excl. tax) — pricing

Getting started

`register` → live monitors on your tables.

Run the agent in your network

Register in the UI, run register once, then keep start running. All traffic is outbound—no inbound access to your databases.

$ chmod +x aimo_agent.sh
$ ./aimo_agent.sh start

Connect databases securely

Add connections through the agent CLI, not the browser. Credentials are encrypted on the agent before upload—we only ever store ciphertext.

Ed25519 agent identity; connection payloads encrypted for storage. Cloud never sees your DB password in plaintext.

Analyse the database and choose tables to monitor

Run analysis from the UI and pick the tables that matter. Profiling runs on the agent; only metadata and aggregates reach AIMO—never raw rows.

Stays in your env Raw rows PII Business values

→

Sent to AIMO Column names & types Aggregate stats Monitor results

Accept — AIMO does the rest

Accept, and AIMO generates the monitors, backfills history, and trains outlier detection on each series. Alerts fire on aggregate shifts, not one-off rows.

Null Row count Uniqueness Range Categorical Pattern SQL aggregate

Alerts arrive by email today, with more destinations added regularly.

Pricing

Pay per table. Nothing else.

No tiers or seat tax. First month: up to 3 tables free—then €10/table/month (excl. tax).

FAQ

Common questions.

What does initial setup look like?

Sign in, register in UI, run register once, add connections in the agent CLI, run start, then analyze and onboard in the UI. After accept, monitors assign and backfill—time scales with data size.

Which databases are supported?

PostgreSQL, MySQL, SQLite, Snowflake, DuckDB, and more—see docs for the list.

Who creates the monitors?

You pick which tables to onboard and accept. AIMO then uses AI to generate the monitor types, columns, and bounds from its analysis of your schema and data.

What actually leaves my network?

Routine monitoring does not export bulk raw rows. The agent sends metadata and aggregates—counts, grouped results, analysis payloads—for dashboards and models. Details: security, architecture.

How often do monitors run?

On your schedule, AIMO dispatches monitor jobs to the agent. Same bounded queries as artifact validation.

How do we scale beyond the trial?

After month one: €10/table/month (excl. tax). Add or remove in UI—billing updates next cycle.

More on data quality monitoring: read the complete guide, the glossary, and the full FAQ.

From the founder

“For years I watched data teams say ‘we should really have data quality monitoring’—and never get to it, because setup took months and someone had to hand-write every check. AIMO is the tool I wished existed: it reads your tables, generates the monitors, learns what normal looks like, and never copies your data out.”

Sami Hanhijärvi · PhD · Founder, AIMO (Motify Data Mining)

Get started

Three tables.
One month.
Free.

Register, CLI connection, onboard—monitors and outliers on up to three tables free month one. Aggregates cross the wire, not bulk raw rows.