Extract meaning from
via API.
Algoboost simplifies AI for everyone. Make sense of your raw data by making it searchable and eliminating the complexities of hosting models and managing vector stores. Extract more value with one easy-to-use API.
Try Algoboost AITransform 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 provides secure management for your vector embeddings, offering accessibility and reliability for your vector storage needs.
Algoboost stores your vector embeddings
Easily access your embeddings through our API for searching your data and clustering.
Use your embeddings via API
Easily access your embeddings through our API for searching your data and clustering.
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.
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.
Give ChatGPT eyes and ears
LLMs can only speak words, we can make it speak all multimedia.
Our collection boasts the latest Embedding Models, with an additional inclusion of OpenAI models.
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.
