Client Success Stories
See how we've partnered with businesses to overcome data challenges and achieve significant outcomes.
Problem: Needed a scalable analytics platform to handle rapidly growing user data from their construction management software.
Solution: We designed and implemented a data lakehouse architecture on Databricks, enabling advanced analytics and ML capabilities.
Impact: Improved query performance by 10x and unlocked new product insights, driving a 20% increase in user engagement.
Problem: Needed to modernize their data infrastructure to support next-generation AI and machine learning initiatives.
Solution: We built a scalable, AI-ready data platform on Azure, leveraging Databricks for unified analytics and Azure ML for model deployment.
Impact: Accelerated AI project deployment by 40% and established a future-proof foundation for advanced analytics.
Problem: Faced challenges with processing large volumes of data for fraud detection, requiring a more scalable and efficient solution.
Solution: We engineered a data lakehouse using Apache Iceberg on AWS, enabling efficient data versioning and schema evolution for advanced fraud analytics.
Impact: Improved fraud detection accuracy by 25% and reduced data processing costs by 40%.
Problem: Required a robust data lake to manage and analyze vast amounts of carbon emission data from various sources.
Solution: We implemented a scalable data lake on AWS, with automated data ingestion and processing pipelines for efficient data management.
Impact: Provided a single source of truth for carbon data, enabling faster reporting and more accurate emissions tracking.
Problem: Struggled with a data platform locked into a single cloud provider, hindering real-time analysis and scalability.
Solution: Revamped their platform to be AWS-agnostic, enabling real-time pattern matching with Elasticsearch, and optimized the system for better scalability.
Impact: Achieved cloud-agnostic data operations, enabled real-time insights, and improved system performance and scalability.
Problem: Faced significant delays in generating reports, which hindered timely decision-making and business agility.
Solution: Implemented a lightweight, serverless data platform using AWS S3, Lambda (Python), and Snowflake to automate data processing and reporting.
Impact: Reduced report generation time by over 95%, enabling business users with on-demand access to critical insights.