Data Preparation & Labeling

High-quality training data is the foundation of every high-performing model. We build it right.

The Data Preparation Pipeline

From raw data to production-ready training sets — every step designed for quality and reproducibility.

01
Source
Identify and collect raw data from internal and external sources
02
Clean
Remove duplicates, fix errors, normalize formats
03
Annotate
Design schema and run labeling workflows with QA
04
Validate
Inter-annotator agreement and quality scoring
05
Augment
Synthetic data for edge cases and class balance
06
Version
Dataset versioning and lineage tracking

What we deliver

Model performance is bounded by data quality. We design and execute data preparation pipelines that produce clean, well-structured, and correctly labeled datasets — the foundation your training runs need to produce reliable, high-performing models.

We handle the full data lifecycle: sourcing, cleaning, deduplication, annotation design, labeling workflows, quality control, and versioning. For cases where real data is scarce, we design synthetic data generation strategies to fill the gaps.

Key deliverables

  • Data sourcing strategy and collection pipeline design
  • Data cleaning, deduplication, and normalization
  • Annotation schema design and labeling workflow setup
  • Quality control and inter-annotator agreement measurement
  • Synthetic data generation for edge cases and class imbalance
  • Dataset versioning and lineage tracking
95%+
Inter-annotator agreement target
10×
Model improvement from clean vs dirty data
100%
Dataset lineage tracked and reproducible
3×
Faster labeling with semi-automated workflows

Real-Life Use Cases

Data preparation that unlocked model performance across domains.

Medical Imaging

Radiology Dataset Curation

A medical AI company had 500K chest X-rays but inconsistent labels from 12 radiologists. We designed a consensus labeling protocol, re-annotated ambiguous cases, and built a quality scoring system. Model AUC improved from 0.81 to 0.94 on the same architecture.

Model AUC: 0.81 → 0.94 from data quality alone
Customer Service

Intent Classification Dataset

A telecom company needed a customer intent classifier but had only 200 labeled examples per class. We designed a synthetic data augmentation strategy using paraphrasing and back-translation, growing the dataset to 5,000 examples per class. Accuracy improved from 71% to 89%.

Classification accuracy: 71% → 89%
Legal

Contract Clause Annotation

A legal AI startup needed 50,000 annotated contract clauses across 15 clause types. We designed the annotation schema, trained annotators, and built a QA pipeline that maintained 96% inter-annotator agreement. The dataset became the foundation for their flagship product.

96% inter-annotator agreement at scale
Automotive

Defect Detection Training Data

An automotive manufacturer needed training data for a visual defect detection model but had very few defect examples. We generated 50,000 synthetic defect images using augmentation and GAN-based generation. The model achieved 98.5% defect detection accuracy.

98.5% defect detection with synthetic data

Build the dataset your model deserves

We'll design and execute the data preparation pipeline that gives your model the best possible foundation.

Build Your Training Dataset