HyperNorm normalizes unit mismatches and merges duplicate entities across multi-vendor data feeds before they ever reach your warehouse.
Vendor A input
Vendor B input
HyperNorm output
Three ways multi-vendor feeds silently break downstream models before HyperNorm gets involved.
Your aggregation query joins on null. The model trains on silence.
Three rows, one company. Your segmentation model learns the wrong size distribution.
Numeric range is meaningless. The regression model quietly learns phantom correlations.
HyperNorm runs normalize then resolve in a single pipeline pass. No custom scripts. No manual mapping spreadsheets.
import hypernorm
client = hypernorm.Client(api_key="hn_...")
result = client.normalize(
source="vendor_a.csv",
schema="canonical_schema.yml"
){
"canonical_id": "ent_9f2a",
"canonical_name": "Acme Corporation",
"confidence": 0.97,
"sources": ["vendor_a", "vendor_b"],
"duplicates_merged": 3
}Different sales reps, different entry conventions. HyperNorm resolves the same company entered 14 different ways into one canonical customer entity with a provenance trail.
See the walkthrough Data EngineeringVendor A sends snake_case USD. Vendor B sends camelCase EUR. Vendor C uses ISO dates. One normalization config handles all three, with schema version tracking.
See the walkthrough ML TeamsSilent entity duplicates in your training set teach the model the wrong distribution. HyperNorm runs pre-training deduplication and flags low-confidence merges for review before you commit to a training run.
Start with the quickstartWe were spending 3 days per sprint manually reconciling vendor schemas. HyperNorm cut that to a 20-minute config review.
Entity duplicates were quietly contaminating our model training set for six months. HyperNorm caught 11,000 unresolved duplicates on the first run.
Data teams have solved ingestion, transformation, and orchestration. The one step they still do by hand is resolving whether 'Acme Corp' and 'ACME Corporation' are the same entity.
Street, St., Strasse, RUA: address formats across global datasets are a normalization minefield. Here is how to handle them systematically.
Downstream data quality fixes cost 10-100x more than catching problems at ingestion. We quantified this across pipeline runs to show where the leverage actually is.
Connect your first vendor feed in under 10 minutes. Free tier available, no credit card required.