Consolidate
Data in unison.
Data, just like any resource, must be processed to be valuable. Combine disparate data sources that may often be fragmented, inconsistent, and inaccessible into an asset that can be leveraged to unlock value.
Unified Data Integration: Consolidate different data sources and formats such as text, tabular data, images, JSON, SQL, and others into a single data vertical, providing comprehensive visibility.
Rapid Prototyping in Raw Data Ingestion: Employ Extract, Transform, and Load (ETL) techniques, including Optical Character Recognition (OCR), to digitize paper files, images, PDFs, and other formats into a usable data stream for analysis and machine learning applications.
Data Lake Management: Manage and support data lake infrastructure, including migrations across both cloud and on-premises environments.
Control
Data at command.
Once your data has been ingested, better manage your consolidated data lake and put it to use.
Data Governance: Establish data cleanliness, security, and compliance, ensuring these critical elements are upheld in all data analysis efforts.
Data Analysis: Leverage expert analysis and advanced machine learning technologies to uncover inefficiencies, trends, and patterns, unlocking insights from previously siloed data sources.
Data Visualization: Efficiently locate and visualize your now uniform and clean data, enabling informed decision-making and actionable insights.
Automate
Data to action.
Increase efficiency by leveraging your data to enable valuable AI end-cases.
AI Model Predictions: Harness pre-built AI solutions, such as predictive models, to automate labor-intensive and high-friction business processes, driving efficiency.
Model Fine-Tuning: Develop machine learning models tailored to your data, enabling custom solutions like chatbots, cost optimizations, and accelerated organizational operations.
Model Management: Deploy and manage the infrastructure for machine learning models, with ongoing enhancements and the option to prepare for future monetization of anonymized datasets