Best AI tools for< Qualitative Researcher >

Speak Ai is an AI-powered software that helps businesses and individuals transcribe, analyze, and visualize unstructured language data. With Speak Ai, users can automatically transcribe audio and video recordings, analyze text data, and generate insights from qualitative research. Speak Ai also offers a range of features to help users manage and share their data, including embeddable recorders, integrations with popular applications, and secure data storage.

Raijin.ai is an AI-powered Customer Discovery and Intelligence Hub designed to help teams aggregate and extract key insights from customer conversations. It accelerates product development by prioritizing features based on customer feedback. The platform offers features like AI Thematic Analysis, Report Writing, Segmentation, and Tags to streamline qualitative research and analysis processes. Raijin.ai is ideal for user researchers, product analysts, and teams looking to integrate AI seamlessly into their workflow to create customer-centric products and data-driven marketing strategies.

ResearchGoat is a generative AI-powered research platform that enables clients to conduct in-depth qualitative research and interviews with unprecedented speed and genuine insights. The platform offers shockingly lower costs compared to traditional consumer research, AI moderators that can converse in any language, and the ability to recruit participants and conduct interviews efficiently. ResearchGoat empowers users to focus on understanding their customers by providing comprehensive insights and analysis from real people.

AILYZE is an AI tool designed for qualitative data collection and analysis. Users can upload various document formats in any language to generate codes, conduct thematic, frequency, content, and cross-group analysis, extract top quotes, and more. The tool also allows users to create surveys, utilize an AI voice interviewer, and recruit participants globally. AILYZE offers different plans with varying features and data security measures, including options for advanced analysis and AI interviewer add-ons. Additionally, users can tap into data scientists for detailed and customized analyses on a wide range of documents.

Viable is an AI-driven platform that provides actionable insights from qualitative data. It effortlessly transforms raw data into valuable information using AI technology. The platform offers integrations with various tech stacks and transparent pricing options to meet user requirements. Viable is designed to help businesses improve customer experience, boost employee engagement, enhance marketing strategies, prioritize product management actions, and conduct efficient research with the help of AI.

Groupt is an AI-powered data categorization and analytics application that simplifies the process of transforming complex arrays of qualitative data into clear, actionable insights for enhanced decision-making. Users can upload CSV files containing data such as user feedback, survey responses, or any qualitative data to receive visualizations of response groupings, categories, and more. The application offers high accuracy and reliability in categorizing data, with a user-friendly interface and transparent pricing options.

Outset is an AI-powered research platform that enables users to conduct and synthesize video, audio, and text conversations with hundreds of participants at once. It uses AI to moderate conversations, identify common themes, tag relevant conversations, and pull out powerful quotes. Outset is designed to help researchers understand the 'why' behind answers and gain deeper insights into the people they serve.

AmpUp.ai is an AI-powered feedback survey tool that helps businesses gain a deeper understanding of their audience. With AmpUp.ai, you can create surveys that ask relevant details and information, and get personalized follow-up questions that relate to what the user is talking about. AmpUp.ai also provides curated answers to surveys designed to understand trends in consumer feedback, personal coaching, and employee experience.

Marvin is an AI research software that serves as the perfect AI research assistant for qualitative research. It automates tedious parts of qualitative research, allowing users to analyze hours of research in minutes. Marvin helps in centralizing, searching, and sharing user research data, making it easily accessible for the whole team. With AI-powered enhancements, Marvin assists in consolidating user insights, finding patterns, and backing decisions with evidence. The tool streamlines the research journey by managing user interview panels, recruiting participants effectively, and providing features like automatic transcripts, video clips, and privacy filters for compliance. Marvin offers different pricing plans suitable for small teams, startups, companies with multiple teams, and large organizations.

Looppanel is an AI-powered research assistant that revolutionizes the way research data is managed. It automatically records calls, transcribes them, and centralizes all research data in one place. Looppanel's highly accurate transcripts support multiple languages and accents, enabling users to focus on interviews while AI takes notes. The platform simplifies analysis, allows for time-stamped note-taking, and facilitates collaboration among team members. Looppanel ensures data security and compliance with high standards, making it a valuable tool for researchers and professionals.

quantilope is an AI-driven Consumer Intelligence Platform that automates quantitative & qualitative research methodologies for actionable insights. It offers a wide range of research methods and tools for market research, consumer segmentation, brand health tracking, and more. Clients benefit from fast results, cost-effective solutions, and expert support from certified research consultants.

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.

Personno.ai is an AI-driven market research platform that provides businesses with access to a pool of AI-generated respondents. These respondents are designed to mimic the behavior and characteristics of real human respondents, allowing businesses to conduct research studies quickly and cost-effectively. Personno.ai's platform includes a range of features that make it easy to create and manage research studies, including the ability to create custom audiences, conduct real-time interviews, and collect both qualitative and quantitative data.

Ivie is an AI-powered user research tool that automates the collection and analysis of qualitative user insights to help product teams build better products. It offers features such as AI-powered insights, processed user insights, in-depth analysis, automated follow-up questions, multilingual support, and more. Ivie provides advantages like human-like conversations, scalable surveys, customizable AI researchers, quick research setup, and multiple question types. However, it has disadvantages such as limited customization options, potential language barriers, and the need for user training. The frequently asked questions cover topics like supported research types, data security, multilingual research, and research findings presentation. Ivie is suitable for jobs related to user research, product development, customer satisfaction analysis, market research, and concept testing. The application can be used for tasks like conducting customer interviews, analyzing user feedback, creating surveys, synthesizing research findings, and building user personas.

Viable is an AI-powered platform that helps businesses analyze and understand their qualitative data. It uses a combination of proprietary AI models and GPT-4 to deliver insights that are accurate, nuanced, and actionable. Viable integrates with popular tools like Zendesk and Intercom, and offers unlimited seats for your entire team.

Essense is an AI-powered platform that specializes in transforming qualitative customer feedback and competitor reviews into actionable insights. The platform helps businesses focus their product roadmap on critical customer needs, improve customer adoption and engagement, and understand their market position compared to competitors. By leveraging AI technology, Essense provides valuable data-driven recommendations to enhance product and customer success strategies.

WhyHive is an AI-powered data analysis tool that helps users find and code key themes in their data, then visualize their findings with beautiful, shareable charts. It is designed to be easy to use, even for those with no prior experience with data analysis. WhyHive can analyze thousands of rows of data in minutes, saving users hours of manual coding time. It can also be used to analyze both quantitative and qualitative data, making it a versatile tool for a variety of research projects.

Multytude is an AI-driven influencer-led prompted listening tool designed for brands and agencies. It combines the scale and speed of social listening with the prompting ability of surveys and focus groups. The platform enables brands to uncover qualitative consumer insights in a short time, facilitated by influencers and analyzed by AI. Multytude aims to revolutionize traditional social listening methods by proactively harnessing strategic insights through prompted listening.

Probz is an AI-powered end-to-end Insights Platform that helps brands gather detailed insights on their ideas through Quant, Qual, and Video Research. With a panel of over 10 million users worldwide, Probz enables brands to supercharge their decision-making process, understand customer behavior, collect feedback for product development, optimize marketing strategies, explore new initiatives, and obtain feedback on technology preferences and designs. The platform offers AI-generated hypotheses, customizable surveys, and real-user tests to enhance sales, customer satisfaction, and brand loyalty.

UserCall is an AI-powered platform that enables users to conduct high-quality user interviews using custom AI interviewers. The platform offers deep insights through 1:1 voice calls with users, providing smart follow-up questions to uncover customer needs. UserCall saves time and effort by extracting key actionable insights from conversations, offering branded interview links, and facilitating continuous feedback, market research, participant screening, usability testing, and customer satisfaction analysis.

noScribe is an AI-based software designed for automated audio transcription, specifically tailored for transcribing interviews for qualitative social research or journalistic purposes. It is a free and open-source tool that runs locally on the user's computer, ensuring data privacy. The software can differentiate between speakers and supports transcription in 99 languages. It includes a user-friendly editor for reviewing and correcting transcripts. Developed by Kai Dröge, a PhD in sociology with a background in computer science, noScribe aims to streamline the transcription process and enhance the efficiency of qualitative analysis.

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

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

DL3DV-10K is a large-scale dataset of real-world scene-level videos with annotations, covering diverse scenes with different levels of reflection, transparency, and lighting. It includes 10,510 multi-view scenes with 51.2 million frames at 4k resolution, and offers benchmark videos for novel view synthesis (NVS) methods. The dataset is designed to facilitate research in deep learning-based 3D vision and provides valuable insights for future research in NVS and 3D representation learning.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

This repository contains code for building cooperative embodied agents modularly with large language models. The agents are trained to perform tasks in two different environments: ThreeDWorld Multi-Agent Transport (TDW-MAT) and Communicative Watch-And-Help (C-WAH). TDW-MAT is a multi-agent environment where agents must transport objects to a goal position using containers. C-WAH is an extension of the Watch-And-Help challenge, which enables agents to send messages to each other. The code in this repository can be used to train agents to perform tasks in both of these environments.

This repository contains code for the paper 'Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models'. It focuses on fine-tuning CLIP in an unsupervised manner to enhance its robustness against visual adversarial attacks. By replacing the vision encoder of large vision-language models with the fine-tuned CLIP models, it achieves state-of-the-art adversarial robustness on various vision-language tasks. The repository provides adversarially fine-tuned ViT-L/14 CLIP models and offers insights into zero-shot classification settings and clean accuracy improvements.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

2025-AI-College-Jobs is a repository containing a comprehensive list of AI/ML & Data Science jobs suitable for college students seeking internships or new graduate positions. The repository is regularly updated with positions posted within the last 120 days, featuring opportunities from various companies in the USA and internationally. The list includes positions in areas such as research scientist internships, quantitative research analyst roles, and other data science-related positions. The repository aims to provide a valuable resource for students looking to kickstart their careers in the field of artificial intelligence and machine learning.

FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.

This repository contains the Python source code for FINMEM, a Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design. It introduces FinMem, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, Memory with layered processing, and Decision-making. FinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. The framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. It presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.

finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML. It offers documentation, installation instructions, and basic usage examples for exploring various financial APIs and features. Users can install recommended datasets from 3rd party APIs into a local SQL database, access Bureau of Economic Analysis (BEA) data, Federal Reserve Economic Data (FRED), Securities and Exchange Commission (SEC) filings, and more. The package also allows users to explore raw data features, install refined data features, and perform refined aggregations of raw data. Configuration options for API keys, user agents, and data locations are provided, along with information on dependencies and related projects.

This repository focuses on the application of Large Language Models (LLMs) in the field of finance. It provides insights and knowledge about how LLMs can be utilized in various scenarios within the finance industry, particularly in generating AI agents. The repository aims to explore the potential of LLMs to enhance financial processes and decision-making through the use of advanced natural language processing techniques.

PIXIU is a project designed to support the development, fine-tuning, and evaluation of Large Language Models (LLMs) in the financial domain. It includes components like FinBen, a Financial Language Understanding and Prediction Evaluation Benchmark, FIT, a Financial Instruction Dataset, and FinMA, a Financial Large Language Model. The project provides open resources, multi-task and multi-modal financial data, and diverse financial tasks for training and evaluation. It aims to encourage open research and transparency in the financial NLP field.

The gpt-bitcoin repository is focused on creating an automated trading system for Bitcoin using GPT AI technology. It provides different versions of trading strategies utilizing various data sources such as OHLCV, Moving Averages, RSI, Stochastic Oscillator, MACD, Bollinger Bands, Orderbook Data, news data, fear/greed index, and chart images. Users can set up the system by creating a .env file with necessary API keys and installing required dependencies. The repository also includes instructions for setting up the environment on local machines and AWS EC2 Ubuntu servers. The future plan includes expanding the system to support other cryptocurrency exchanges like Bithumb, Binance, Coinbase, OKX, and Bybit.

Solana AI Trade Bot is an advanced trading tool specifically designed for meme token trading on the Solana blockchain. It leverages AI technology powered by GPT-4.0 to automate trades, identify low-risk/high-potential tokens, and assist in token creation and management. The bot offers cross-platform compatibility and a range of configurable settings for buying, selling, and filtering tokens. Users can benefit from real-time AI support and enhance their trading experience with features like automatic selling, slippage management, and profit/loss calculations. To optimize performance, it is recommended to connect the bot to a private light node for efficient trading execution.

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