Audience
Companies in need of a platform to build and deploy AI apps
About Relevance AI
No more file restrictions and complicated templates. Easily integrate LLMs like ChatGPT with vector databases, PDF OCR, and more. Chain prompts and transformations to build tailor-made AI experiences, from templates to adaptive chains. Prevent hallucinations and save money through our unique LLM features such as quality control, semantic cache, and more. We take care of your infrastructure management, hosting, and scaling. Relevance AI does the heavy lifting for you, in minutes. It can flexibly extract from all sorts of unstructured data out of the box. With Relevance AI, the team can extract with over 90% accuracy in under an hour. Add the ability to automatically group data by similarity with vector-based clustering.
Company Information
Relevance AI
Founded: 2020
Australia
relevanceai.com
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Union Cloud
Union.ai is an award-winning, Flyte-based data and ML orchestrator for scalable, reproducible ML pipelines. With Union.ai, you can write your code locally and easily deploy pipelines to remote Kubernetes clusters.
“Flyte’s scalability, data lineage, and caching capabilities enable us to train hundreds of models on petabytes of geospatial data, giving us an edge in our business.”
— Arno, CTO at Blackshark.ai
“With Flyte, we want to give the power back to biologists. We want to stand up something that they can play around with different parameters for their models because not every … parameter is fixed. We want to make sure we are giving them the power to run the analyses.”
— Krishna Yeramsetty, Principal Data Scientist at Infinome
“Flyte plays a vital role as a key component of Gojek's ML Platform by providing exactly that."
— Pradithya Aria Pura, Principal Engineer at Goj
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SciPhi
Intuitively build your RAG system with fewer abstractions compared to solutions like LangChain. Choose from a wide range of hosted and remote providers for vector databases, datasets, Large Language Models (LLMs), application integrations, and more. Use SciPhi to version control your system with Git and deploy from anywhere. The platform provided by SciPhi is used internally to manage and deploy a semantic search engine with over 1 billion embedded passages. The team at SciPhi will assist in embedding and indexing your initial dataset in a vector database. The vector database is then integrated into your SciPhi workspace, along with your selected LLM provider.
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UpTrain
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users.
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Ragie
Ragie streamlines data ingestion, chunking, and multimodal indexing of structured and unstructured data. Connect directly to your own data sources, ensuring your data pipeline is always up-to-date. Built-in advanced features like LLM re-ranking, summary index, entity extraction, flexible filtering, and hybrid semantic and keyword search help you deliver state-of-the-art generative AI. Connect directly to popular data sources like Google Drive, Notion, Confluence, and more. Automatic syncing keeps your data up-to-date, ensuring your application delivers accurate and reliable information. With Ragie connectors, getting your data into your AI application has never been simpler. With just a few clicks, you can access your data where it already lives. Automatic syncing keeps your data up-to-date ensuring your application delivers accurate and reliable information. The first step in a RAG pipeline is to ingest the relevant data. Use Ragie’s simple APIs to upload files directly.
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Product Details
Platforms Supported
SaaS
Support
Online