AIMO Docs

Appearance

Getting started ​

From an empty account to monitored tables and the first analysis pass. Each section links to deeper pages where needed.

1. Sign up and sign in ​

Create an account and sign in to the web UI. You need an active session before you can register agents and work with connections.

2. Register an agent ​

In the web UI, open Add agent, choose Register via agent, and follow the steps. You receive a registration token and commands to run the agent CLI in your environment so the agent can authenticate to AIMO.

For keys, tokens, and what is written on disk, see Registration.

3. Add database credentials with the agent CLI ​

Credentials are added through the agent, not in the browser. After registration, the register command usually writes a helper script aimo_agent.sh next to your agent data (for example the directory you mount as /app/data in Docker). The script sets AIMO_AGENT_UUID, AIMO_AGENT_PRIVATE_KEY_B64, AIMO_AGENT_PASSPHRASE, AIMO_AGENT_HOST, and AIMO_AGENT_USE_TLS, runs the agent image, and forwards CLI arguments—you do not need to set those variables by hand for routine use.

From the same directory as aimo_agent.sh:

bash
chmod +x aimo_agent.sh   # once
./aimo_agent.sh add      # interactive: name, database type, secrets; tests, then stores via the API

Use the same wrapper for list, test, edit, and delete.

If you install the agent package directly (not Docker), use python -m aimo.agent.cli with the same subcommands and environment—see Agent CLI.

Keep the agent process running when jobs should run against your data (for example ./aimo_agent.sh agent). See Operations and Registration.

4. Analyze the connection in the web UI ​

Open the connection you added via the CLI and run analyze so AIMO can inspect metadata and structure before you choose tables.

5. Choose tables and accept ​

Select tables to onboard, then accept. AIMO assigns monitor definitions for those tables; you do not pick individual monitor types in the UI. See Monitors.

After you accept ​

AIMO runs an automated pipeline on the selected tables, typically in this order:

  1. Deeper table analysis — columns, types, relationships, and patterns relevant to monitoring
  2. Time blocks and dimensions — expressions so monitors aggregate consistently over time
  3. Monitor definitions — generated, typed monitors for your tables
  4. Monitor values — historical values for the new monitors over the product’s window
  5. Outlier model — trained on monitor series; likelihood scores for new points
  6. Past alerts — materialized on historical data where the product supports it

Timing and progress depend on deployment and data size. See Monitors and Outlier detection.

See the product site for pricing and positioning.