Clarifai

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.

General

Platform: https://www.clarifai.com/
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

ryzen ai

i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

x402 Bazaar Agent

Today's AI agents hit a wall the moment they need data behind a paywall. The x402 Bazaar Agent tears that wall down. The agent takes a plain-English question, searches a catalog of 42 premium endpoints across 17 domains using Gemini semantic embeddings, selects the cheapest APIs that can answer, pays for each call via x402 micropayments on Base Sepolia, and composes a cited final answer — no human in the loop. DISCOVER — Gemini text-embedding-005 indexes the catalog at startup. Queries rank by cosine similarity; entries below a relevance floor return zero results, giving the agent a clean stop signal rather than hallucinated answers. PAY — The x402 HTTP client intercepts the 402 response, signs a USDC micropayment with an EVM wallet, and retries. The LLM never sees the payment handshake. VERIFY — After every paid call, an independent Gemini Flash model judges whether the response served the stated purpose. Off-topic data is flagged before the reasoning model builds on it. COMPOSE — Gemini 2.5 Pro weaves results across endpoints and domains into a cited answer. Demo: "Will container ALPHA-99 arrive at Genoa on time?" triggers three paid calls — container ETA, port congestion, marine weather — producing a synthesized delay forecast. Guardrails prevent runaway behavior: hard budget cap, empty-search streak, no-progress streak, duplicate-call detection, and an iteration backstop. The catalog mirrors the real Coinbase Bazaar's discovery schema exactly — switching to the live Bazaar is one function change. All 42 endpoints have mock fallbacks so the demo works with zero mandatory signups. The browser UI streams every agent action live.

Eco friendly shopping assistant

The Eco-Friendly Shopping Assistant is a tool designed to help consumers make more sustainable and environmentally-conscious purchasing decisions. By providing access to a wide variety of eco-friendly products, the assistant allows users to search for items based on eco-friendly attributes like organic, fair trade, or eco-certified labels. Additionally, it offers carbon footprint calculations for products, allowing users to understand the environmental impact of their purchases. The project uses APIs to gather data on various products and their sustainability features, promoting a greener lifestyle by empowering shoppers to choose products that align with their environmental values.