Best AI tools for< make data-driven product decisions >

Sprig is a user insights platform that helps businesses capture user feedback and session clips directly in their product. This data is then transformed into AI-powered product opportunities. Sprig's features include targeted surveys, session replays, and AI analysis. The platform is used by top tech companies such as Coinbase, Square, and ClassPass to boost product adoption, optimize user experience, and reduce churn.

Leanbe is a product management platform designed for data-driven teams. It helps product managers capture insights from customers, competitors, and team members to make better product decisions. Leanbe uses AI to continuously update idea scores based on customer behavior, insights, and feedback from the team. This helps product managers prioritize the right ideas and build products that meet the needs of their customers.

PromptBetter is a tool that helps you optimize your OpenAI API prompts with advanced A/B testing and insights. It provides a simple interface to create, test, and change prompts, and it tracks conversions and user feedback so you can see how your prompts are performing. PromptBetter also offers in-depth insights into your OpenAI API usage, so you can understand how and by whom it is being used.

Athenic AI is an AI-enabled analytics platform that empowers business teams to access and analyze data without technical knowledge. It offers a range of solutions tailored to specific industries, including product growth for startups, growth analytics for marketers, WooCommerce analytics for e-commerce, and ERP analytics for planning and operations. Athenic AI's key features include search-driven analytics, natural language AI training, auditor role, suggested questions, dashboards, email reports, query generation, and collaboration tools. By leveraging AI, Athenic AI provides fast and reliable insights, enabling teams to make data-driven decisions and improve efficiency.

Prelaunch.com is a product validation platform that helps creators test market demand before launching a product. It provides tools for concept validation, price validation, positioning testing, and idea validation. Users can gather early customer feedback, define pricing, identify target audiences, and make data-driven decisions. The platform offers AI market research assistance, audience insights, and analytics to help creators launch with confidence or refine their product ideas. With a focus on co-creation and community engagement, Prelaunch.com aims to empower creators to succeed in the competitive market landscape.

DataGems is a data storytelling platform that helps businesses transform scattered data into compelling narratives. With DataGems, users can explore their data like never before, discover insights that matter most to their business, and share compelling stories, backed with data. DataGems integrates with a variety of data sources, including product analytics, social media, and e-commerce platforms, and offers a variety of features to help users create and share data stories, including customizable templates, push notifications, and voice notes.

Graphy is a data visualization and reporting tool that helps marketers create beautiful, interactive reports in minutes. It is powered by AI to increase productivity and make data more accessible and understandable. With Graphy, you can unify your data from all your tools into a single, shareable view. You can also explore data in the tools you've already mastered, then save it in Graphy to tell your data story with AI Insights, comments, annotations, goals, trend lines, and even emojis.

Betafi is a cloud-based user research and product feedback platform that helps businesses capture, organize, and share customer feedback from various sources, including user interviews, usability testing, and product demos. It offers features such as timestamped note-taking, automatic transcription and translation, video clipping, and integrations with popular collaboration tools like Miro, Figma, and Notion. Betafi enables teams to gather qualitative and quantitative feedback from users, synthesize insights, and make data-driven decisions to improve their products and services.

Kraftful is an AI-powered tool that helps product teams collect, analyze, and prioritize user feedback. It integrates with various feedback sources, such as surveys, app store reviews, support tickets, and user interviews, to provide a comprehensive view of user needs and pain points. Kraftful uses natural language processing and machine learning to automatically categorize and summarize feedback, making it easy for product teams to identify trends and make data-driven decisions.

ChatGPT for Shop is a browser extension that utilizes the capabilities of ChatGPT to analyze and summarize user reviews for products on major e-commerce websites. It assists e-commerce professionals in understanding user preferences and making informed product selections.

Cardinal is an AI-powered product backlog tool that helps product managers prioritize features and make data-driven decisions. It integrates with your CRM and customer support tools to collect customer feedback and revenue data, which it then uses to identify the most valuable features to build. Cardinal also provides a clear view of your product roadmap and progress, so you can always see what's coming up and how it's aligned with your business goals.

Mather Economics is a global leader in providing technology, analytics, and advisory services to enable publishers to monetize content and grow subscriber revenue. Our Sophi platform uses industry-leading AI technology and automation to monetize content more effectively and efficiently, driving both revenue growth and cost savings. Our approach to dynamic pricing has proven to drive significant revenue increases over the past 20 years and continues to lead the industry. Overall, our value proposition enables fast, economical, and verifiable return on investment through our efficient application of predictive analytics, technology, A/B testing, and data science.

EyePop.ai is an AI application that offers effortless computer vision solutions for self-service perception AI. It streamlines visual data analysis to provide easy and accessible AI-powered insights across various industries, catering to users with different levels of technical knowledge. Users can build tailored AI applications with EyePop.ai's intuitive no/low code platform, enabling them to unlock the potential of their visual content and make data-driven decisions.

Sellesta.ai is an AI-powered platform that leverages advanced technologies to provide efficient and effective solutions for businesses. It offers a range of services such as data analysis, customer engagement, and automation to streamline operations and enhance productivity. With a user-friendly interface and cutting-edge algorithms, Sellesta.ai empowers organizations to make data-driven decisions and optimize their processes.

Upshot.ai is an award-winning omnichannel customer engagement, user engagement, and gamification platform that helps businesses improve product adoption and conversions using principles of gamification, behavioral psychology, and learning theory. The platform enables product, digital, and marketing teams to capture user data, analyze it with over 50 analytical reports, create target user segments, improve product and feature adoption through dynamic in-app guidance, achieve highly personalized communication at scale, gamify user experience, build loyal users, upsell and cross-sell, provide in-the-moment experiences, retain users, deliver omni-channel experiences, create automated workflows, and make data-driven decisions using machine learning algorithms.

Valyr is a tool that helps you track usage, costs, and latency metrics for your GPT-3 logs with just one line of code. It's easy to get started and can be up and running in less than 3 minutes.

Arro is an AI-powered research assistant that helps product teams collect customer insights at scale. It uses automated conversations to conduct user interviews with thousands of customers simultaneously, generating product opportunities that can be directly integrated into the product roadmap. Arro's innovative AI-led methodology combines the depth of user interviews with the speed and scale of surveys, enabling product teams to gain a comprehensive understanding of their customers' needs and preferences.

Gastrograph AI is a cutting-edge artificial intelligence platform that empowers food and beverage companies to optimize their products for consistent market success. Leveraging the world's largest sensory database, Gastrograph AI provides deep insights into consumer preferences, enabling companies to develop new products, enter new markets, and optimize existing products with confidence. With Gastrograph AI, companies can reduce time to market costs, simplify product development, and gain access to trustworthy insights, leading to measurable results and a competitive edge in the global marketplace.

Smarter Sales is a sales call data management and automation tool that helps businesses streamline their sales processes, improve performance metrics, and save time. It integrates with popular video conferencing platforms like Zoom, Teams, and Meets to automatically pull call recordings for analysis. The tool also automates CRM data entry, providing instant, personalized feedback post-call. Managers can access detailed performance dashboards and summarized email reports to make data-driven coaching decisions. Smarter Sales is fully customizable, allowing businesses to set their own CRM data preferences and extract specific data from each call. The tool also offers personalized AI learning materials and stunning chart creation capabilities to help businesses better understand their sales data and improve their sales strategies.

LoomFlows is a user feedback collection tool that allows businesses to collect high-quality feedback from their users through Loom screen recordings and annotated screenshots. It helps businesses streamline feedback collection, identify impactful opportunities, and scale faster by building the right features.

DeepBI is an AI-native data analysis platform that leverages the power of large language models to explore, query, visualize, and share data from any data source. Users can use DeepBI to gain data insight and make data-driven decisions.

This solution accelerator is built on Azure Cognitive Search Service and Azure OpenAI Service to synthesize post-contact center transcripts for intelligent contact center scenarios. It converts raw transcripts into customer call summaries to extract insights around product and service performance. Key features include conversation summarization, key phrase extraction, speech-to-text transcription, sensitive information extraction, sentiment analysis, and opinion mining. The tool enables data professionals to quickly analyze call logs for improvement in contact center operations.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

A curated list of Generative AI projects, tools, artworks, and models

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Guides B2B startups in a structured customer segment evaluation process. Stop guessing! Ideate, Evaluate & Make data-driven decision.

Calculate the results of your A/B test and check whether the result is statistically significant or due to chance.

Expert on 'Lean Analytics' book content

Nerdy, interactive advisor for digital product optimization.

I predict DAU based on growth and retention.

Creates Reforge framework feature maps in tabular format.

Guiding business strategy with historical and modern insights

Informal, insightful expert in digital analytics for business improvement.

Expert in current gaming growth analysis

A strategic SaaS analyst for CXOs, with a focus on market trends and benchmarks.

协助规划公司运营方向的助手

Enhances cannabis delivery customer experience with data analysis and recommendations.

Data-driven predictions on company funding and potential

A Data Engineer assistant offering advice on data pipelines and data-related tasks.

Upload an image of a statistical analysis and we'll interpret the results: linear regression, logistic regression, ANOVA, cluster analysis, MDS, factor analysis, and many more

Predicts gender for first names using data

Providing web data insights from Google and Adobe Analytics.

Analyzes and organizes SERP data.

Your assistant for understanding G Analytics data.

Analyzing startup trends with a focus on 2024 data, or 2023 if needed.