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Use case · AI Infrastructure

Your AI is only as good as the data under it.

Limen checks each table against what it is supposed to mean and records a certificate, so when a model’s answer is questioned you can show why it holds.

Producer guarantees are checked once at Raw data and carried through Cleaned and Combined to the AI model, where the consumer requirements are met. A certificate recorded at Combined shows whether those checks still hold.AI modelProducer guaranteesConsumer requirementsCertificatechecks still hold
Before a model reads a table, its certificate shows whether the checks still hold.

What goes wrong today

A model repeats what the warehouse gets wrong.

  • Silent failures.A nightly job ran twice, half the rows in orders_fact are duplicates, and the model reports revenue up 84%.
  • Renamed columns.A vendor renamed customer_id to cust_id, and the agent still looks it up by the old name.
  • Unwritten definitions.The person who built churn_signal left in 2024, and nothing records what it measures.
  • Seven revenue tables.Seven tables have revenue in the name, finance trusts one, and the agent answers from another.

How it works

Know which data your AI can rely on.

  1. 01

    State what each table means.

    Schema, freshness, allowed values, and owner, in descriptions people and models can both read.

  2. 02

    Check every batch.

    Each run is checked against that meaning. A passing run gets a timestamped certificate.

  3. 03

    Answer for the output.

    When an answer is questioned, the certificates show which run, which rules, which data, and the result.

A small first step

The tables your main model reads. One week.

We take the three or four tables your most-used model reads, set starting rules, and run them for a week. You see which batches would have given it bad data.

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