Vy Labs provides Cephea, an AI-powered recruitment software designed to streamline talent acquisition processes. This platform offers unified search, contextual job matching, and pipeline visibility to help organizations hire top talent faster and more efficiently. The system focuses on merit-based sourcing and automation to reduce administrative burden while maintaining recruiter control.
Funding
Funding not disclosed
Founders
Product
Problem
Many organizations struggle with the challenges posed by disorganized and unstructured data, making it difficult to extract meaningful insights and utilize the information effectively. Traditional methods of data processing are often unreliable and expensive, hindering decision-making and operational efficiency.
Solution
Vy Labs offers a platform that leverages large language models (LLMs) and knowledge graphs to provide reliable document cleaning and entity extraction, transforming unstructured data into structured knowledge. The platform employs cutting-edge LLM APIs for at-scale document cleaning, knowledge graph creation for high-accuracy requirements, and custom supervised deep learning models tailored to domain-specific problems. By extracting meaningful data points from documents and other unstructured sources, Vy Labs ensures that the foundational data is reliable and ready for deep learning models, enabling users to interact with complex information seamlessly and gain actionable insights. The platform's hybrid AI approach combines data cleaning and extraction with custom deep learning models trained on real-world data to understand and learn tasks in depth, making them uniquely modeled to bespoke needs.
Target Audience
The platform is designed for organizations across diverse industries, including healthcare (medical knowledge modeling and retrieval) and recruiting (resume parsing, job matching, and application tracking).
Features
- Document cleaning using large-language model (LLM) APIs
- Knowledge graph creation and search
- Custom supervised deep learning models tailored to domain-specific problems
- Entity extraction from unstructured sources
- Hybrid AI approach combining data cleaning and custom deep learning models
- Intuitive knowledge graphs and structured knowledge maps