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Tabular foundation model · Integrations

Workflows and data stack

Call the model from any HTTP node, schedule syncs and refreshes from your orchestrator, and land bundles in the warehouse.

Informational documentation. This section explains how a tabular foundation model of the Data Language Model class works: what goes in, what comes out, and how results are evaluated. It is based on the paper Data Language Models: A New Foundation Model Class for Tabular Data (arXiv:2605.06290). It is reference material, not a hosted service: hosts such as api.example.com, keys, and ids are placeholders.

Any tool with an HTTP Request node can call the model, and any orchestrator can keep an endpoint current. The contract is the same everywhere: POST /v2/run for a stateless quick run, POST /v2/serve/{endpoint_id} for a live endpoint, a bearer key, JSON in, the bundle out.

Workflow and no-code tools

Setting Value
Method POST
URL https://api.example.com/v2/serve/{endpoint_id} (or /v2/run)
Headers Authorization: Bearer {{ $secrets.API_KEY }}, Content-Type: application/json
Body { "tables": [ { "id": "...", "columns": [...], "rows": [...] } ] }

Map the incoming item (a form submission, a CRM row, a CSV chunk) into rows. Use a serve-scoped key for the serve call; the sync, refresh, and job-polling steps below need manage and read. Then branch on the bundle:

  • prediction.results[0].confidence above your threshold → automate; below → human review.
  • tables[0].column_profile[*].pii == true → mask before the next step.
  • cross_table_map.entity_matches[*].confidence in your review band → review queue.
  • imputation.filled[*] non-empty → write filled values back with their row confidence.

Endpoint creation, refresh, and batch runs return 202 with a job_id. Add a wait-and-poll loop on GET /v2/jobs/{job_id} (2 s, 4 s, 8 s, then every 15 s) and continue on status == "done". Every failure is one JSON envelope with error.type; branch on it, and honor Retry-After on 429. See the error envelope.

Keep the layer current

A DAG or workflow that runs after each load:

text
 load source  →  POST /v2/data/:id/sync  →  POST /v2/endpoints/:id/refresh
                                         →  poll job  →  read report

Use an Idempotency-Key per scheduled run so retries never duplicate work.

Land the bundle in the warehouse

For large tables, run in batch and write the output to a warehouse table:

http
POST /v2/run
{ "data": ["snowflake://example/warehouse/events_2025"],
  "target": { "column": "converted" },
  "options": { "processing": "batch", "out": "warehouse://analytics.outputs" } }

The bundle (per-table profile, imputation, predictions, unified rows) lands as rows you can join and model on. See Batch and large runs.

dbt

Model the bundle tables like any source: unified_rows becomes a golden-record model, imputation.filled a repair log, prediction.results a scored table with the endpoint's report_id as lineage.

Other channels

For a channel you need, contact us.

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