Data Engineering
Clean, structure, automate, and connect data so AI projects can actually work - across pipelines, databases, and multimodal annotation.
What we do
Data is the foundation of every AI system. We build the pipelines, cleaning processes, annotation workflows, and integrations that make your data usable for machine learning, analytics, and automation. Whether you need structured databases, automated ETL flows, or large-scale annotation for training models, we handle it.
ETL/ELT Pipelines
Automated data extraction, transformation, and loading from multiple sources into clean, queryable formats.
Data Cleaning
Handle missing values, duplicates, inconsistencies, and outliers. Prepare data that models can actually learn from.
API Integrations
Connect your data sources, third-party services, and internal systems into unified data flows.
Data Collection
Scrape, gather, and aggregate data from web sources, APIs, documents, and sensors for AI training and analysis.
Data Annotation
Label and annotate data for AI model training: text classification, image segmentation & object detection, voice synthesis, and video annotation.
Databases & Dashboards
SQL database design, data modeling, monitoring dashboards, and automated reporting systems.
What you need to get started
Raw data sources
Databases, files, APIs, documents, or systems where your data currently lives. Even unstructured or messy data works as a starting point.
Target format or goal
What should the cleaned/annotated data look like? What models or systems will consume it?
Volume and frequency
How much data? One-time batch or ongoing streaming? This determines the architecture we build.
Quality requirements
Annotation guidelines, accuracy thresholds, or schema requirements for the output data.
What we've built
Golden Gauss AI - Data Pipeline
Built a complete data engineering pipeline for financial time-series: automated data collection from market feeds, 239 engineered features, cleaning and normalization, and structured storage for model training and live inference.
Startup Failures Dataset
Curated and published dataset on Kaggle tracking startup failures across industries, funding stages, and regions. Includes data collection, cleaning, and structured formatting for analysis and ML training.
Need your data ready for AI?
Tell us about your data challenge and we'll propose a solution.