Entity Resolution & Normalization for Model-Ready Datasets
Raw web data is chaotic. We transform it into structured, model-ready assets — resolving entities, deduplicating records, enforcing formatting standards, and automatically masking PII so your datasets are clean, consistent, and compliant before they reach your models.
Before & After
{
// Before normalization
"provider": "dr. jane smith md phd",
"dob": "01/15/1978",
"phone": "555.867.5309",
"ssn": "XXX-XX-XXXX" // auto-redacted
// After normalization
"provider": "Dr. Jane Smith, MD, PhD",
"dob": "1978-01-15T00:00:00Z",
"phone": "+15558675309",
"ssn": "[REDACTED]",
"entity_id": "ent_8a3f92c1",
"confidence": 0.98
}The Problem
LLMs and vector databases are highly sensitive to data quality. Duplicate records inflate your embedding index and degrade retrieval precision. Inconsistent date formats cause downstream parsing failures. Unredacted PII in training data creates legal exposure. Most teams lack the NLP pipeline infrastructure to handle this at scale before ingestion.
Our Solution
We deploy a multi-stage normalization pipeline on every dataset we deliver. Entity resolution runs across all records. Schema contracts are enforced field-by-field. PII detection covers 15+ entity types in 40+ languages. Outputs arrive as clean JSON or Markdown with a per-record confidence score and audit trail.
Core Capabilities
Entity Resolution & Deduplication
We identify and merge records that refer to the same real-world entity across different sources using both exact matching (IDs, canonical names) and fuzzy matching (edit distance, phonetic similarity, ML-based embeddings). The result is a single, authoritative record per entity in your dataset.
Format Standardization & Schema Enforcement
Dates become ISO 8601. Phone numbers become E.164. Addresses get geocoded and normalized to a standard hierarchy. Currency values get explicit codes and decimals. Every field in your output schema is validated against a defined contract before delivery — no silent schema drift.
Automated PII Detection & Redaction
Our NLP pipeline detects and masks names, email addresses, phone numbers, national IDs, financial account numbers, and medical record identifiers using a combination of regex, named entity recognition, and contextual ML classifiers. Outputs are GDPR and CCPA-safe by default.
Ready for clean, compliant data?
Share a sample of your raw data. We'll return a normalized proof-of-concept output within 48 hours, no commitment required.
Request a Free PoC