Eto

About Eto

LanceDB is an open-source database designed for multimodal AI applications, enabling rapid vector search and advanced data retrieval from large-scale datasets. It addresses the challenges of managing and scaling AI data by providing a performant solution that integrates seamlessly with existing data pipelines and supports real-time analytics.

```xml <problem> Managing and scaling AI data for multimodal applications presents challenges in terms of performance, integration with existing data pipelines, and support for real-time analytics. Existing database solutions often struggle to efficiently handle the unique requirements of vector search and advanced data retrieval from large-scale datasets. </problem> <solution> LanceDB is an open-source database tailored for multimodal AI, providing a developer-friendly solution for managing AI data from experimentation to production. It functions as an embedded database with native object storage integration, enabling deployment anywhere and scaling to zero when not in use. LanceDB delivers high performance for search, analytics, and training, supporting real-time vector search and advanced retrieval for RAG applications. The database is built upon the Lance columnar format, optimized for multimodal AI training, analytics, and retrieval, offering significant speed improvements compared to traditional formats like Parquet. </solution> <features> - High-performance vector search capable of searching billions of vectors in real-time. - Cost-effective scalability for indexing billions of vectors and petabytes of multimodal data. - Multimodal training capabilities, allowing filtering, selection, and streaming of training data directly from object storage. - Advanced retrieval with hybrid vector and full-text search, rich metadata filters, and custom reranking. - Seamless integration with existing data and AI toolchains, including Spark and Ray. - Powered by the Lance columnar format, optimized for AI workloads. - Support for various data types, including text, images, and videos. - Integrations with Polars, DuckDB, Pyarrow, and PyTorch. </features> <target_audience> LanceDB targets AI developers, data scientists, and machine learning engineers building multimodal AI applications, including those in generative AI, autonomous vehicles, and AI-enabled e-commerce. </target_audience> ```

What does Eto do?

LanceDB is an open-source database designed for multimodal AI applications, enabling rapid vector search and advanced data retrieval from large-scale datasets. It addresses the challenges of managing and scaling AI data by providing a performant solution that integrates seamlessly with existing data pipelines and supports real-time analytics.

Where is Eto located?

Eto is based in San Francisco, United States.

When was Eto founded?

Eto was founded in 2022.

How much funding has Eto raised?

Eto has raised 11500000.

Location
San Francisco, United States
Founded
2022
Funding
11500000
Employees
17 employees
Major Investors
CRV
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Eto

Score: 100/100
AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

LanceDB is an open-source database designed for multimodal AI applications, enabling rapid vector search and advanced data retrieval from large-scale datasets. It addresses the challenges of managing and scaling AI data by providing a performant solution that integrates seamlessly with existing data pipelines and supports real-time analytics.

eto.ai5K+
cb
Crunchbase
Founded 2022San Francisco, United States

Funding

$

Estimated Funding

$11.5M+

Major Investors

CRV

Team (15+)

Jai Chopra

Building LanceDB

Ben Lees

Accelerating Mulitmodal AI Deployments

Company Description

Problem

Managing and scaling AI data for multimodal applications presents challenges in terms of performance, integration with existing data pipelines, and support for real-time analytics. Existing database solutions often struggle to efficiently handle the unique requirements of vector search and advanced data retrieval from large-scale datasets.

Solution

LanceDB is an open-source database tailored for multimodal AI, providing a developer-friendly solution for managing AI data from experimentation to production. It functions as an embedded database with native object storage integration, enabling deployment anywhere and scaling to zero when not in use. LanceDB delivers high performance for search, analytics, and training, supporting real-time vector search and advanced retrieval for RAG applications. The database is built upon the Lance columnar format, optimized for multimodal AI training, analytics, and retrieval, offering significant speed improvements compared to traditional formats like Parquet.

Features

High-performance vector search capable of searching billions of vectors in real-time.

Cost-effective scalability for indexing billions of vectors and petabytes of multimodal data.

Multimodal training capabilities, allowing filtering, selection, and streaming of training data directly from object storage.

Advanced retrieval with hybrid vector and full-text search, rich metadata filters, and custom reranking.

Seamless integration with existing data and AI toolchains, including Spark and Ray.

Powered by the Lance columnar format, optimized for AI workloads.

Support for various data types, including text, images, and videos.

Integrations with Polars, DuckDB, Pyarrow, and PyTorch.

Target Audience

LanceDB targets AI developers, data scientists, and machine learning engineers building multimodal AI applications, including those in generative AI, autonomous vehicles, and AI-enabled e-commerce.