Extract meaning from
via API.

Algoboost simplifies AI for everyone by eliminating the complexities of hosting models and managing vector stores. If you have data in any form, Algoboost can help you understand your data by clustering it and making it searchable.
- All with one easy-to-use API.

Transform your Data into AI-Ready Vectors

Choose an Embedding Model

Vector embeddings enable semantic search engines that can find more contextually relevant results, improving the search experience.

Streamlined Model Inference

Inference the model with your raw data through the Algoboost API.

Algoboost stores your vector embeddings

Algoboost provides secure management for your vector embeddings, offering accessibility and reliability for your vector storage needs.

Use your embeddings via API

Easily access your embeddings through our API for searching your data and clustering.

Algoboost in action

Semantic Search

Vector embeddings enable semantic search engines that can find more contextually relevant results, improving the search experience.

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Give ChatGPT the memory of an elephant

LLMs, not just ChatGPT, have a limited context length. In order for you to create a chatbot for your organisation, your LLM will require access to data greater than its context length - this is for querying your data and maintaining a chat history. 

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Give ChatGPT eyes and ears

LLMs can only speak words, we can make it speak all multimedia.

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We have the largest collection of the latest Embedding Models available.

Clip-vit-b-32

Image & Text model CLIP, which maps text and images to a shared vector space

multi-qa-minilm-l6-cos-v1

Sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space

gtr-t5-large

This is a sentence transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space.

all-mpnet-base-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space

all-minilm-l6-v2

Sentence transformers model that maps sentences & paragraphs to a 384 dimensional dense vector space

sentence-t5-large

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space

Bge-large-en

Maps any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.

Worried about data privacy?

Contact us to get your own deployment