A safety latchfor your data.
AI products rarely fail on the model. They fail on the catalog underneath. Tag yours against values you set, and your agent gets a tool that leaves out what it can’t vouch for, rather than guessing.
Run · in progress
2Catalogs shipped
1,324Records processed
20Safety values enforced
2,000+Automated tests
What we do.
Get data inA file, a paste, a table read from Postgres, MySQL, MongoDB, Airtable or Shopify, or records a model drafts. It all arrives the same way: untagged, in a heap under the crane's jib.
Where we sit.
Sources · raw
SpreadsheetsCSV, TSV, Excel, JSON
Pasted rowsStraight from a sheet
Your databasePostgres, MySQL, MongoDB, Airtable, Shopify
GeneratedDrafted by a model
Rules first
Match your contract's words · skip the model
AI enrichment
Allowed values
Safety tagging
Facts with quotes
Your PDFs as source
Curate
Ratchet blocks
Abstains
Low confidence
Withheld
Serve
Checked on your answer key · a numbered release · MCP or REST
Outcomes · served
Your agent
MCP, on one tool key
Your app
REST, the same release
Every answer
Ranked, with receipts
Never returned
Excluded values, withheld rows