End-to-end data engineering solutions that accelerate time-to-value and reduce cost-of-quality.
We build a robust data & analytics foundation with a focus on accelerating AI adoption across the business value chain through our digital accelerators, frameworks, and solutions.
The cloud is getting complex
Enterprises are rapidly moving to the cloud, based on the promise of scalability and cost savings. But multi-cloud environments can be complex to integrate and ingest data from.
Data is stuck in legacy silos
Most enterprises have their data in legacy data platforms, which require them to re-engineer the codebase quickly and effectively.
ML engineering is time-consuming
Today, enterprises spend weeks in experimentation, feature selection, productizing ML models etc., delaying time-to-insights significantly.
How we help you overcome data engineering challenges?
Enterprises need thoughtful data engineering to sustain AI & analytics at scale. Our digital accelerators are designed to accelerate the full life cycle of data management covering data ingestion, data quality, catalog, data provisioning with focus on improving time to value and self-service analytics for different user personas. In order to scale analytics and AI, you need Tredence to deliver flexible, easy to extend and scalable data foundation.
Cloud data engineering advisory and strategy
Our data engineers help you clarify your AI vision; build a robust strategy across cloud, tech and data governance pillars; choose the right technology; and modernize your data platforms.
Platform modernization for accelerated AI adoption
We enable rapid AI adoption by modernizing your data platforms with our use case-driven accelerators.
Operationalized ML engineering with MLWorks
Our MLWorks accelerator is built to manage thousands of models across petabytes of data with ease.