Data Engineering

Clean, reliable data is the foundation of every successful AI and analytics initiative. At Correlion AI, we bring years of experience designing and implementing enterprise-grade data systems for multi-billion-dollar companies, ensuring scalability, security, and performance at every level.

Some examples in action:‍

AI-Enriched Data Pipelines - We design and deploy robust, automated data pipelines that seamlessly extract raw data from your databases, transform it, and make it readily available for detailed analytics. When necessary, we enrich your data using advanced Large Language Models—for instance, translating customer reviews or identifying fraudulent transactions.‍

Scalable Data Warehousing Solutions - We set up professional-grade data warehouses tailored to your needs, utilizing industry-leading platforms like Google BigQuery, Amazon Redshift, and others. Our solution includes structured data marts customized for different teams, along with secure, robust access controls. Say goodbye to unreliable data logic and start making confident, data-driven decisions.

‍Discover our capabilities—see our recent project: on ETL Pipeline with LLM-driven Data Enrichment.

Here is the full list of our services:

Data Integration & Warehousing

• ‍Data Extraction, Transformation, and Loading (ETL/ELT) – Designing efficient pipelines to ingest, transform, and load raw data from diverse sources (databases, APIs, cloud services) into a centralized system.
• ‍Data Warehousing – Building scalable, high-performance warehouses (Snowflake, BigQuery, Redshift) to store and analyze vast datasets.
• ‍Data Integration – Unifying disparate data sources for a single, consistent view across the organization.
• ‍Pipeline Orchestration – Automating data workflows using Apache Airflow, Prefect, or Dagster to ensure reliability, scalability, and monitoring.

Data Architecture & Access Control

• ‍Data Marts & Schema Design – Structuring domain-specific data marts for optimized performance, governance, and user access.
• ‍Dimensional Modeling & Schema Optimization – Designing fact and dimension tables (star/snowflake schema) to enable fast analytics and reporting.
• ‍Data Governance & Access Control – Implementing RBAC (Role-Based Access Control) and fine-grained permissions to enforce security and compliance.

Data Preparation & Preprocessing

• ‍Data Cleaning – Handling missing values, inconsistencies, and errors to improve data quality.
• ‍Feature Engineering – Creating domain-specific transformations to enhance machine learning performance.
• ‍Data Transformation & Enrichment – Applying aggregations, joins, and encodings to structure data for downstream analytics and AI applications.

Machine Learning Model Deployment

• ‍Model Serving & APIs – Deploying models as scalable APIs using tools like TensorFlow Serving, FastAPI, or Vertex AI.
• ‍MLOps & Model Lifecycle Management – Automating versioning, retraining, and monitoring to ensure models remain accurate and effective.
• ‍Real-Time & Batch Inference – Optimizing model execution for both real-time predictions and large-scale batch processing.
• ‍AI Model Governance – Ensuring model fairness, explainability, and regulatory compliance.