Best AI tools for< amazon ppc manager >

ZonTools is an all-in-one Amazon PPC platform that helps you automate your campaigns, boost your sales, and increase your brand exposure. It is powered by proprietary A.I. technology that automatically and continuously optimizes your campaigns, allowing you to grow your sales profitably while freeing up hours of your time.

Trellis is an eCommerce merchandising software that offers a range of AI-powered tools to optimize various aspects of online selling. It provides real-time competitor data, automated pricing using machine learning, SEO-rich content generation, and cutting-edge software for promotions. The platform also offers market intelligence through game-changing data and dashboards, helping users make data-driven decisions. Trellis aims to help businesses maximize profitability by streamlining workflows and automating merchandising decisions.

Sellesta is an AI advertising agency that combines expert insights and AI to elevate brands on Amazon, Mercado Libre, and online stores. We offer a range of services to help businesses grow their sales, including ads management, market research, and product launch consulting. Our team of experts has deep experience in e-commerce and AI, and we are passionate about helping our clients succeed.

Autron is an AI-powered advertising tool designed to help businesses boost sales and optimize their advertising cost of sales (ACoS) on Amazon. The tool offers fully automated campaigns for Sponsored Products, Sponsored Brands, and Sponsored Display, leveraging AI technology to make data-driven decisions and optimize ad performance. Autron's technology simulates, predicts, and delivers decisions for users, acting as a virtual data scientist and machine learning specialist to help businesses grow on autopilot. With Autron, users can set simple goals based on their business objectives and let the tool handle keyword and ASIN research, providing deeper insights into sales growth and advertising performance.

TestMarket is an AI-powered sales optimization platform for online marketplace sellers. It offers a range of services to help sellers increase their visibility, boost sales, and improve their overall performance on marketplaces such as Amazon, Etsy, and Walmart. TestMarket's services include product promotion, keyword analysis, Google Ads and SEO optimization, and advertising optimization.

Amazon Web Services (AWS) is a comprehensive, evolving cloud computing platform from Amazon that provides a broad set of global compute, storage, database, analytics, application, and deployment services that help organizations move faster, lower IT costs, and scale applications. With AWS, you can use as much or as little of its services as you need, and scale up or down as required with only a few minutes notice. AWS has a global network of regions and availability zones, so you can deploy your applications and data in the locations that are optimal for you.

Amazon Science is a research and development organization within Amazon that focuses on developing new technologies and products in the fields of artificial intelligence, machine learning, and computer science. The organization is home to a team of world-renowned scientists and engineers who are working on a wide range of projects, including developing new algorithms for machine learning, building new computer vision systems, and creating new natural language processing tools. Amazon Science is also responsible for developing new products and services that use these technologies, such as the Amazon Echo and the Amazon Fire TV.

Amazon Bedrock is a cloud-based platform that enables developers to build, deploy, and manage serverless applications. It provides a fully managed environment that takes care of the infrastructure and operations, so developers can focus on writing code. Bedrock also offers a variety of tools and services to help developers build and deploy their applications, including a code editor, a debugger, and a deployment pipeline.

Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images.

Amazon Q in QuickSight is a generative BI assistant that makes it easy to build and consume insights. With Amazon Q, BI users can build, discover, and share actionable insights and narratives in seconds using intuitive natural language experiences. Analysts can quickly build visuals and calculations and refine visuals using natural language. Business users can self-serve data and insights using natural language. Amazon Q is built with security and privacy in mind. It can understand and respect your existing governance identities, roles, and permissions and use this information to personalize its interactions. If a user doesn't have permission to access certain data without Amazon Q, they can't access it using Amazon Q either. Amazon Q in QuickSight is designed to meet the most stringent enterprise requirements from day one—none of your data or Amazon Q inputs and outputs are used to improve underlying models of Amazon Q for anyone but you.

CopyMonkey is an AI-powered Amazon listing optimization tool that helps sellers create optimized listings in seconds. The tool uses AI to generate keyword-optimized bullet points and descriptions, analyze competitor listings, and suggest listing improvements based on sales results. CopyMonkey is used by over 1000 sellers and has been proven to help sellers increase their sales.

Push Lap Wholesale is a powerful Amazon wholesale sourcing tool that helps you find profitable products, save time, and hit your targets. With 97% matching accuracy, you can quickly scan your bulk price lists and never miss a potential profitable product. Our world-class platform provides you with detailed product information, including Keepa charts, historical averages, profit calculations, variation data, competition analysis, and multipack adjustments. You can also create and manage suppliers' purchase lists, export them as CSV files, and get the total cost and profit of each list. Additionally, we provide you with 500+ auto-ungate ASINs for free and access to a list of over 4,000 vetted and verified distributors with contact details.

ReviewGPT is an AI-powered tool that helps users analyze Amazon products and reviews to make informed purchasing decisions. It utilizes AI to identify counterfeit products, pirated books, fake reviews, unreliable third-party sellers, and products with potential safety or health risks. By leveraging ReviewGPT, users can save time and money while ensuring they purchase genuine and high-quality products from Amazon.

SmartScout is an Amazon FBA product research software designed to help businesses grow their wholesale, arbitrage, and private label operations. It provides users with access to millions of data points, including product sales data, seller information, and competitive insights. SmartScout's features include a product database, brand and category filters, a traffic graph, and a variety of tools for product research and analysis.

ProductListing.AI is an AI-powered tool that helps Amazon sellers create, optimize, and enhance their product listings. It uses advanced algorithms to analyze listings, identify target audiences, and optimize content for higher conversions and sales. With ProductListing.AI, sellers can generate product listing copies in seconds, ensuring that their listings are compelling and captivating, driving higher conversions and boosting sales.

This website offers a variety of AI-powered tools and resources to help users with a variety of tasks, including video generation, transcription, image upscaling, and resume writing. The website also provides access to AI-generated images and Midjourney prompts.

RankPress.io is a SaaS platform that utilizes ChatGPT/OpenAI artificial intelligence to power WordPress 'Google Snippets + PAA' and WooCommerce 'Amazon Affiliate Autoblog'. It offers a range of features for creating unique and engaging content, including the ability to scrape Google search result snippets, PAA Questions & Answers, and Amazon product data. With its advanced AI capabilities, RankPress.io helps users automate content creation, improve SEO rankings, and drive traffic to their websites.

Content Robot is an AI-powered content and image generator that helps users create high-quality, SEO-optimized content for their websites, blogs, and social media. The tool offers a wide range of templates and features to help users generate unique and engaging content quickly and easily. Content Robot is also affordable and easy to use, making it a great option for businesses of all sizes.

PlaylistAI is an AI-powered music playlist generator that allows users to create personalized playlists based on their preferences and descriptions. With its advanced AI algorithms, PlaylistAI can generate playlists for any occasion, mood, or activity. Users can simply enter a prompt describing the type of music they want to listen to, and PlaylistAI will create a custom playlist that matches their request. The app also offers a variety of features such as smart suggestions, the ability to turn any thought into a playlist, and the ability to revisit favorite music. Additionally, PlaylistAI provides unique music discovery experiences such as instant playlists for music festivals, the ability to identify songs in TikTok videos, and the ability to blend genres together.

Platoria is an AI-powered platform that helps users make informed shopping decisions by providing concise summaries of product reviews. It allows users to search for products or add their own, compare prices from different vendors, and access AI-generated review summaries. Platoria's mission is to save users time and effort in their shopping journey.

This directory contains samples for a QA chain using an AmazonKendraRetriever class. For more info see the samples README. Note : If you are using an older version of the repo which contains the aws_langchain package, please clone this repo in a new location to avoid any conflicts with the older environment. We are deprecating the aws_langchain package, since the kendra retriever class is available in LangChain starting v0.0.213.

Repository for training and deploying Generative AI models, including text-text, text-to-image generation, prompt engineering playground and chain of thought examples using SageMaker Studio. The tool provides a platform for users to experiment with generative AI techniques, enabling them to create text and image outputs based on input data. It offers a range of functionalities for training and deploying models, as well as exploring different generative AI applications.

The Amazon Transcribe Live Call Analytics (LCA) with Agent Assist Sample Solution is designed to help contact centers assess and optimize caller experiences in real time. It leverages Amazon machine learning services like Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker to transcribe and extract insights from contact center audio. The solution provides real-time supervisor and agent assist features, integrates with existing contact centers, and offers a scalable, cost-effective approach to improve customer interactions. The end-to-end architecture includes features like live call transcription, call summarization, AI-powered agent assistance, and real-time analytics. The solution is event-driven, ensuring low latency and seamless processing flow from ingested speech to live webpage updates.

This repository provides a sample solution for building generative AI agents using Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain. The solution creates a generative AI financial services agent capable of assisting users with account information, loan applications, and answering natural language questions. It serves as a launchpad for developers to create personalized conversational agents for applications like chatbots and virtual assistants.

This repository contains sample code demonstrating various use cases leveraging Amazon Bedrock and Generative AI. Each sample is a separate project with its own directory, and includes a basic Streamlit frontend to help users quickly set up a proof of concept.

The serverless-rag-demo repository showcases a solution for building a Retrieval Augmented Generation (RAG) system using Amazon Opensearch Serverless Vector DB, Amazon Bedrock, Llama2 LLM, and Falcon LLM. The solution leverages generative AI powered by large language models to generate domain-specific text outputs by incorporating external data sources. Users can augment prompts with relevant context from documents within a knowledge library, enabling the creation of AI applications without managing vector database infrastructure. The repository provides detailed instructions on deploying the RAG-based solution, including prerequisites, architecture, and step-by-step deployment process using AWS Cloudshell.

This repository provides sample applications and tutorials to showcase the power of Amazon Bedrock with Python. It helps Python developers understand how to harness Amazon Bedrock in building generative AI-enabled applications. The resources also demonstrate integration with vector databases using RAG (Retrieval-augmented generation) and services like Amazon Aurora, RDS, and OpenSearch. Additionally, it explores using langchain and streamlit to create effective experimental applications.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

This repository is a sample chatbot using the Anthropic company's LLM Claude, one of the foundational models provided by Amazon Bedrock for generative AI. It allows users to have basic conversations with the chatbot, personalize it with their own instructions and external knowledge, and analyze usage for each user/bot on the administrator dashboard. The chatbot supports various languages, including English, Japanese, Korean, Chinese, French, German, and Spanish. Deployment is straightforward and can be done via the command line or by using AWS CDK. The architecture is built on AWS managed services, eliminating the need for infrastructure management and ensuring scalability, reliability, and security.

GPT Home is a project that allows users to build their own home assistant using Raspberry Pi and OpenAI API. It serves as a guide for setting up a smart home assistant similar to Google Nest Hub or Amazon Alexa. The project integrates various components like OpenAI, Spotify, Philips Hue, and OpenWeatherMap to provide a personalized home assistant experience. Users can follow the detailed instructions provided to build their own version of the home assistant on Raspberry Pi, with optional components for customization. The project also includes system configurations, dependencies installation, and setup scripts for easy deployment. Overall, GPT Home offers a DIY solution for creating a smart home assistant using Raspberry Pi and OpenAI technology.

This repository provides code to deploy a chatbot powered by Multi-Model and Multi-RAG using AWS CDK on AWS. Users can experiment with various Large Language Models and Multimodal Language Models from different providers. The solution supports Amazon Bedrock, Amazon SageMaker self-hosted models, and third-party providers via API. It also offers additional resources like AWS Generative AI CDK Constructs and Project Lakechain for building generative AI solutions and document processing. The roadmap and authors are listed, along with contributors. The library is licensed under the MIT-0 License with information on changelog, code of conduct, and contributing guidelines. A legal disclaimer advises users to conduct their own assessment before using the content for production purposes.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

AIAC is a library and command line tool to generate Infrastructure as Code (IaC) templates, configurations, utilities, queries, and more via LLM providers such as OpenAI, Amazon Bedrock, and Ollama. Users can define multiple 'backends' targeting different LLM providers and environments using a simple configuration file. The tool allows users to ask a model to generate templates for different scenarios and composes an appropriate request to the selected provider, storing the resulting code to a file and/or printing it to standard output.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

This leaderboard evaluates the hallucination rate of various Large Language Models (LLMs) when summarizing documents. It uses a model trained by Vectara to detect hallucinations in LLM outputs. The leaderboard includes models from OpenAI, Anthropic, Google, Microsoft, Amazon, and others. The evaluation is based on 831 documents that were summarized by all the models. The leaderboard shows the hallucination rate, factual consistency rate, answer rate, and average summary length for each model.

Eden AI aims to simplify the use and deployment of AI technologies by providing a unique API that connects to all the best AI engines. With the rise of **AI as a Service** , a lot of companies provide off-the-shelf trained models that you can access directly through an API. These companies are either the tech giants (Google, Microsoft , Amazon) or other smaller, more specialized companies, and there are hundreds of them. Some of the most known are : DeepL (translation), OpenAI (text and image analysis), AssemblyAI (speech analysis). There are **hundreds of companies** doing that. We're regrouping the best ones **in one place** !

Lego AI Parser is an open-source application that uses OpenAI to parse visible text of HTML elements. It is built on top of FastAPI, ready to set up as a server, and make calls from any language. It supports preset parsers for Google Local Results, Amazon Listings, Etsy Listings, Wayfair Listings, BestBuy Listings, Costco Listings, Macy's Listings, and Nordstrom Listings. Users can also design custom parsers by providing prompts, examples, and details about the OpenAI model under the classifier key.

The AWS Healthcare and Life Sciences AI/ML Immersion Day workshops provide hands-on experience for customers to learn about AI/ML services, gain a deep understanding of AWS AI/ML services, and understand best practices for using AI/ML in the context of HCLS applications. The workshops cater to individuals at all levels, from machine learning experts to developers and managers, and cover topics such as training, testing, MLOps, deployment practices, and software development life cycle in the context of AI/ML. The repository contains notebooks that can be used in AWS Instructure-Led Labs or self-paced labs, offering a comprehensive learning experience for integrating AI/ML into applications.

AWS AI/ML Workshop & example collection in Korean. The example codes in this repository are divided into 4 categories: AI services, Applied AI, SageMaker, Integration, Generative AI, and AWS Neuron. Each directory has its own Readme file. This repository also provides useful information for self-studying SageMaker.

Expert in Amazon selling, providing precise guidance on various Amazon-related issues.

A role-play enabled Amazon/AWS interview coach specializing in STAR format and Leadership Principles.

Asesor experto en Amazon FBA, al estilo de Jeff Bezos

Expert in Amazon PRFAQs, providing strategic advice and document editing.

Guides on Amazon Bedrock setup and use

I assist with creating optimized listings for Merch by Amazon.

Amazon product shopping search, conveniently query products, get discounts and discounted products more quickly.

Bilingual translator for Amazon, Chinese to 50 languages.

A no-nonsense gift recommender with direct Amazon links.

Adds seller and brand info to Amazon ASIN data tables.

A guide for sellers on Amazon.

Teaching and applying Bezos' insights through his letters to shareholders at Amazon

I write detailed, engaging Amazon product reviews based on your points.

Expert in various online reselling platforms. Reseller Buddy is trained on tons of data about reselling. eBay, Amazon, Etsy, Poshmark and more. Please consider rating Reseller Buddy if you find any value in it.

Connect your Spotify, Apple Music, Amazon Music, or Deezer account to create music playlists in your library.

A powerful and multilingual music identifier for Spotify Wrapped, Amazon Music, YouTube, TikTok by listening to your songs or scanning playlists from screenshots.