Transformation - Datalier

What It Does

Transform converts unstructured, ingestion-stage data into production-ready intelligence assets. A fully configurable processing pipeline executes automated data cleansing, deduplication, PII redaction, schema normalization, and multimodal feature extraction across all records rectifying structural anomalies without data loss. Every operational step maintains complete auditability through delta metrics, generating a unified data health score that persists across downstream workflows and auto-populates the lineage report the moment the dataset reaches readiness.

Operations

Core Cleaning

Validate

Checks every record against the expected schema missing fields, null values, malformed rows, and type mismatches across every column.

Deduplicate

Removes exact-match duplicates via content hashing. Semantic near-duplicate clustering catches the rest, below.

PII Detection

Scans every text field for personal data names, emails, phone numbers, credit cards, addresses, national IDs before it reaches a labeler.

AI Enrichment

LLM Schema Normalize

An LLM reconciles messy or inconsistent field names, date formats, and free-text values into one clean schema.

Semantic Clustering

Embeds every record with a sentence model and clusters near-duplicates, removing what hashing alone would miss.

Confidence Routing

A zero-shot classifier scores every row. High-confidence rows auto-label, uncertain rows route to human review.

Multimodal Extraction

OCR Extraction

Runs OCR across scanned PDFs and images, extracting structured text before labeling begins.

Speech-to-Text Cleanup

Normalises transcripts for punctuation, capitalisation, speaker diarisation, and confidence filtering.

Cross-Modal Alignment

Links transcript segments to image frames and metadata into one aligned record, ready for multimodal training.

Transform

Operations

Get data infrastructure for training AI models