Superlinked provides AI search and matching capabilities specifically designed for semi-structured data sources. The platform utilizes a Mixture of Encoders approach to encode diverse data types, including text and numerical features, for high-relevance retrieval. This enables advanced use cases like conversational search, real-time recommendations, and complex data organization for enterprise applications.
Funding
$10.8M 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 to efficiently transform complex, multi-modal data into vector embeddings for use in information retrieval and feature engineering. Existing solutions often lack the flexibility to handle diverse data types and require significant engineering effort to deploy and maintain. This complexity hinders the adoption of vector-based applications for semantic search, recommendations, and analytics.
Solution
Superlinked provides a compute framework designed to streamline the creation of vector embeddings from complex data. The platform allows users to combine text, images, and structured metadata into multi-modal vectors, capturing the full context of their entities. It offers a Python SDK for managing the compute layer between data infrastructure and vector databases, enabling users to experiment, deploy, and scale their vector-based applications. Superlinked supports multi-objective queries, allowing users to optimize for relevance, freshness, and other competing objectives.
Target Audience
The primary target audience includes data scientists, machine learning engineers, and software developers building information retrieval and feature engineering systems within enterprises.
Features
- Python SDK for defining data transformations and constructing vector indices.
- Support for multi-modal vectors, combining text, images, and structured data.
- Infrastructure-as-code approach for managing the compute layer.
- Multi-objective query optimization for balancing competing objectives.
- Auto-generated ingestion and query APIs for simplified data access.
- Integration with vector databases such as MongoDB and Redis.
- Example use cases and notebooks for RAG, semantic search, recommendations, and analytics.