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Overview›Automation Overview
automation overview
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Rules
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aito..
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Manual
_search_evaluate
POST /api/v1/_evaluate
{
"testSource": { "from": "purchases", "limit": 200 },
"evaluate": {
"from": "purchases",
"where": {
"supplier": { "$get": "supplier" },
"description": { "$get": "description" },
"amount_eur": { "$get": "amount_eur" }
},
"predict": "cost_center"
},
"select": ["accuracy", "baseAccuracy", "cases"]
}
{
"testSource": { "from": "purchases", "limit": 200 },
"evaluate": {
"from": "purchases",
"where": {
"supplier": { "$get": "supplier" },
"description": { "$get": "description" },
"amount_eur": { "$get": "amount_eur" }
},
"predict": "cost_center"
},
"select": ["accuracy", "baseAccuracy", "cases"]
}
How it works
Per-field accuracy on this page is real — it comes from Aito's _evaluate with
Unlike traditional ML, aito.. needs no feature engineering, no model selection, no deployment — predictions come directly from the database, and so does this evaluation.
select: ["cases"]. We hold out each row, predict the field from supplier + description + amount, compare to ground truth, and bucket by confidence band. Predictions ≥ 0.85 are the auto-approve zone; lower bands flag review work.Unlike traditional ML, aito.. needs no feature engineering, no model selection, no deployment — predictions come directly from the database, and so does this evaluation.
Learn more