Use case · AI & ML

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

Agents and RAG systems amplify whatever they sit on, including the silent failures and undocumented assumptions in your warehouse. Limen fixes the layer underneath: contracts define what each table means, and certificates prove every batch met that definition before a model sees it. When the model answers, you can show why the answer holds.

Book a demo → See the stack
L4 · Consumer

Models, agents, RAG

All your agents: analysts, copilots, retrieval, and autonomous workflows.

OpenAI OpenAI Claude Claude Gemini Gemini
↓ consumes
L3 · Proof

Certificates

Every batch is verified against its contract before any model reads it. The certificate is the receipt.

pass cert_3a4f7e customers · 08:42 UTC
pass cert_b0c218 orders_fact · 08:43 UTC
pass cert_8e1d05 ledger · 08:43 UTC
↓ proven against
L2 · Meaning

Contracts

A machine-readable definition of what each table is supposed to mean. Schema, freshness, ownership, semantics.

contract customers {
  pk           customer_id
  freshness    <= 4h
  email        unique & rfc5322
  tier         in [free, pro, ent]
}
↓ describes
L1 · Substrate

Warehouse data

Whatever you've got: Snowflake, BigQuery, Databricks, Postgres. The bytes underneath everything else.

Snowflake Snowflake Databricks Databricks BigQuery BigQuery Postgres Postgres
Why this matters

You can't go AI-native on a stack you don't trust.

Four failure modes get amplified the moment a model reads the warehouse and none of them show up in a dashboard until something downstream is already wrong.

Silent failures

A nightly job double-ran. Half the rows in orders_fact are duplicates. The model dutifully reports revenue up 84%.

Amplified by AI

Schema drift

A vendor renamed customer_id to cust_id. The retrieval index still queries by the old name. The agent answers with stale joins.

Amplified by AI

Undocumented assumptions

The employee who built churn_signal left in 2024. The definition lives in someone's head. The agent has no way to know what it actually measures.

Amplified by AI

Seven revenue tables, one truth

The warehouse has seven tables with revenue in the name. Finance trusts exactly one. Nothing is broken; the agent just answers from the wrong table.

Amplified by AI
How Limen helps

Tell your AI which data to trust.

  1. Encode meaning

    Contracts capture what a table is supposed to mean: schema, freshness, allowed values, ownership, semantic descriptions agents and humans both read.

  2. Certify every batch

    Every materialization is validated against its contract before the data is exposed. A passing run emits a signed, timestamped certificate.

  3. Serve certified data

    Models and retrieval read through Limen, which only returns batches with a valid certificate. Stale or failed batches are quarantined automatically.

  4. Defend the output

    When an answer is questioned, the certificate chain is the answer's defense: this run, this contract, this dataset, this verdict. Auditable on demand.

Example

An agent asks. The substrate answers, with receipts.

finance-agent · session 0x82af
finance-agent → limen
What was Q3 revenue by region, fulfilled-orders basis?
limen substrate response
$184,402,118
across 7 regions · fulfilled basis · Q3 2025
Receipts
pass orders_fact cert_b0c218 · 08:43 UTC
pass fx_rates_daily cert_71fa0e · 06:00 UTC
pass regions_dim cert_29ab44 · 04:12 UTC
Defensible. Reproducible. Three contracts enforced before the model saw a row.
Get started

Make your AI's data layer defensible.

We'll instrument the three or four tables your most-used model reads from, generate baseline contracts, and run them for a week. You'll see exactly which batches your agents would have been served bad data on.

Book a demo →