The startup offers a data intelligence and automation platform that enables organizations to manage unstructured data effectively by identifying and eliminating redundant and outdated information. This approach significantly reduces infrastructure costs while providing insights into data state and availability, thereby mitigating data risks and enhancing operational efficiency.
Funding
$13M 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
Enterprises struggle with the increasing volume of unstructured data, leading to rising storage costs, security vulnerabilities, and missed opportunities for data-driven insights. The lack of visibility and control over this data hinders effective data management and AI initiatives.
Solution
Aparavi offers a data intelligence and automation platform designed to provide organizations with comprehensive visibility, understanding, and control over their unstructured data. The platform enables users to discover, classify, and optimize unstructured data across various data sources, including cloud, on-premises, and hybrid environments. By automating data lifecycle processes and providing actionable intelligence, Aparavi helps organizations reduce storage costs, mitigate data risks, and prepare data for AI applications. The platform's no-code visual workflow allows users to build and streamline data processes, while its AI-powered capabilities facilitate sensitive data detection, AI-OCR, and multimodal embedding.
Target Audience
Aparavi targets enterprises across various industries that are struggling with unstructured data management, including IT departments, legal teams, and data scientists.
Features
- Visual workflow: A no-code interface for building and automating data processes.
- Sensitive data detection: Automated identification and classification of sensitive information to meet compliance requirements (GDPR, HIPAA, CCPA).
- AI-OCR: Conversion of image-based documents into searchable text.
- Universal LLM Integration: Connects with various AI models, including OpenAI, xAI, Anthropic, and Amazon Bedrock.
- Multimodal embedding: Processes text, images, audio, and video in unified AI workflows.
- Advanced table handling: Extracts tables from complex documents automatically.
- Unstructured-to-SQL pipeline: Transforms unstructured content into SQL databases.
- Data lifecycle automation: Automates data processing with intelligent scheduling.
- Connectors & Targets: Integrates with existing storage systems and cloud providers.
- REST Hook for Pipelines: Triggers data processing workflows via API.
- Data Toolchain SDK: Build custom nodes, connectors, and extensions.