Clarifai AI technology page Top Builders

Explore the top contributors showcasing the highest number of Clarifai AI technology page app submissions within our community.


Clarifai's groundbreaking technology aims to bring The World’s AI(™) to developers and data scientists around the globe. One of the first deep learning platforms since its establishment in 2013 by Matthew Zeiler, Ph.D, a distinguished expert in the realm of machine learning, the company has consistently set high benchmarks in the AI industry.

Prior to founding Clarifai, Dr. Zeiler took the top five places in image classification at the 2013 ImageNet Challenge. By 2019, Clarifai had made such a significant impact that it was recognized as a leader in Forrester’s New Wave Computer Vision Platforms report, making it the sole startup to attain a differentiated rating. In 2022 Clarifai launched a new free service – Clarifai Community – designed to empower everyone to create, share and use The World's AI. That same year Clarifai further solidified its reputation when IDC MarketScape acknowledged it as a leader in Computer Vision AI Platforms. In 2023, Gartner named Clarifai a niche player in the Magic Quadrant for Cloud AI Developer Services for the third year in a row.

Catering to the entire AI developmental life cycle, today Clarifai provides an AI platform for unstructured image, video, text, and audio data. The platform supports data exploration, data labeling, model training, evaluation and inference, and deployment. Clarifai enables users to implement AI technology into their products via API, Mobile SDK, and/or on-premise solutions.


  • Platform:
  • Release date: 2013
  • Founder: Matthew Zeiler, Ph.D.
  • Type: AI-powered software platform
  • Achievements: Winner of the top five places in image classification at the ImageNet 2013 competition, Recognized as a leader in Forrester’s New Wave Computer Vision Platforms in 2019, Computer Vision AI Platforms by IDC MarketScape in 2022, and Gartner CAIDS ‘21-’23.

Flexible Deployment

Clarifai is known for its flexible deployment options, allowing users to choose from an array of platforms, be it any cloud such as AWS, Azure, or Google Cloud, or on bare-metal with options for air-gapped setups and on-edge deployments.

AI Lake

Clarifai's AI Lake is designed to enhance AI adoption and its reusability across enterprises. It has features such as:

  • Storing and analyzing vast amounts of data objects.
  • Auto-indexing your data using AI.
  • Tracking the lineage and versioning of all AI assets.

Centralized and Secure AI

The platform offers centralized and secure AI functionalities that employ foundation models to simplify the machine learning model development and deployment process. It provides an efficient structure to organize, share, and reuse all AI resources.

Data Labeling with Scribe

Data labeling has never been more streamlined:

  • Automated data labeling upon data ingestion.
  • Scalability to cater to labeling projects of any magnitude.
  • Intuitive labeling and reviewer user interfaces to optimize the quality and quantity of annotations.

Open, Reusable Building Blocks

Clarifai’s fully-integrated AI platform brings forth a wide variety of machine learning products and services that seamlessly collaborate to produce AI on an impressive scale:

  • AI Lake: A central repository for all AI assets.
  • Spacetime: Enables vector and input searching.
  • Enlight: Fine-tune any open-source or third-party model with ease.
  • **Mesh: **Connect models to create more complex logic.
  • Armada: Instantaneously deploy your models for production.
  • Modules: Effortlessly deploy Streamlit-powered UIs in your apps or on top of Clarifai's Portal.

Start Building with Clarifai

Clarifai provides an extensive range of libraries and resources to assist developers in embarking on their AI journey. Dive into a vibrant ecosystem and see what others are creating using Clarifai’s cutting-edge technology. For a deeper understanding and tutorials, explore the dedicated sections on Clarifai's official website.

Documentation for developers

Clarifai AI technology page Hackathon projects

Discover innovative solutions crafted with Clarifai AI technology page, developed by our community members during our engaging hackathons.



Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.