Provides a low-code platform for AI-powered data collection, transformation, and validation, enabling users to integrate data from sources like PDFs, spreadsheets, and ERP systems without coding. Streamlines data workflows by consolidating, standardizing, and validating scattered data into audit-ready metrics, improving accuracy and efficiency for businesses.
Funding
$100K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations struggle to efficiently collect, transform, and validate data from disparate sources like PDFs, spreadsheets, and ERP systems, leading to data silos and hindering accurate reporting and analysis. Manual data handling is time-consuming, error-prone, and lacks the scalability needed to meet evolving business requirements.
Solution
Dataring offers a low-code, AI-powered platform that streamlines data workflows by automating data collection, transformation, and validation. The platform enables users to build and automate data collection workflows using a drag-and-drop interface, eliminating the need for extensive coding. It consolidates and standardizes data from various sources into audit-ready metrics, improving data accuracy and operational efficiency. By incorporating human validation and triggering automated agents, Dataring ensures seamless data operations and reliable data insights.
Target Audience
Dataring is designed for businesses, data analysts, and ESG professionals who need to streamline data collection, improve data quality, and automate data workflows for reporting and analysis.
Features
- Drag-and-drop interface for building custom data collection workflows without coding
- AI-powered data extraction, transformation, and validation from various data sources
- Pre-built integrations for Excel spreadsheets, PDFs, ERP systems, and other third-party sources
- Option to create custom data components using Python
- Automated agents for triggering seamless data operations
- Workflow scheduling via UI or APIs for periodic or event-driven data processing
- Data standardization and anomaly detection for improved data quality
- Integration with major data systems and cloud providers