AI-first GCC

Data Engineering.

Data platform design, pipeline architecture, quality frameworks, and governance the foundation that makes AI possible.

AI is only as good as the data underneath it. Data engineering is the most underinvested layer in most AI programs. NeoIntelli helps you get it right.

Deliverables

What we deliver

Data platform architecture

Design a scalable, cost-efficient data platform on cloud infrastructure tailored for AI workloads.

Pipeline engineering

Build reliable, tested data pipelines for ingestion, transformation, and serving.

Data quality framework

Implement automated data quality checks, profiling, and anomaly detection.

Data governance

Establish cataloging, lineage tracking, access controls, and compliance-ready classification.

Analytics enablement

Enable self-serve analytics and reporting for business teams on top of governed data.

Frequently asked questions

What cloud platforms do you support?

We work across AWS, Azure, and GCP and recommend based on your existing investments and requirements.

How do you handle data quality?

Through automated profiling, validation rules, anomaly detection, and integration with CI/CD pipelines.

Do you build data platforms from scratch?

We can. We also modernise existing platforms and migrate legacy systems to cloud-native architectures.

How does data engineering relate to AI?

Data engineering provides the reliable, governed data foundation that AI models need for training, evaluation, and serving.

What tools do you typically use?

We are tool-agnostic but frequently work with Spark, dbt, Airflow, Delta Lake, Snowflake, and cloud-native services.