Best AI tools for< Improve Approach >

Salesably is an AI-driven sales practice platform that transforms sales training from static to dynamic through interactive playbooks, AI-powered practice, skills certification, and performance analytics. It offers sales trainers and managers a cutting-edge platform to enhance programs, drive better results, and create new revenue streams. With features like practice against diverse personas, learning paths & certification, customizable platform, measurable impact, objection handling mastery, discovery questioning excellence, pitch optimization workshops, and actionable insights for sales leaders and trainers, Salesably empowers sales teams to become masters of buyer-centric selling.

BenevolentAI is a leader in applying advanced AI to accelerate biopharma drug discovery blending science and technology with a focus on finding solutions for complex diseases. We empower both biopharmaceutical companies and our internal scientists to harness the full potential of data and AI to accelerate the next generation of scientific advances. We have built our AI-enabled drug discovery engine to drive a revolution in drug discovery. The Benevolent Platform™ unlocks the power of a vast biomedical data landscape to provide a multidimensional representation of human biology across all diseases. We believe this approach will improve the probability of clinical success, and help us deliver life-changing treatments to patients – because it matters.

Recursion is a techbio company that uses artificial intelligence to accelerate drug discovery. The company's platform combines hardware, software, and data to create a more efficient and effective drug discovery process. Recursion has a broad pipeline of drug candidates in development, and it has partnered with several leading pharmaceutical companies. The company is headquartered in Salt Lake City, Utah.

QOVES is a website that provides tools and advice to help people improve their looks. The website offers a variety of services, including facial analysis, hairline design, style advice, and Photoshop retouching. QOVES also has a blog with articles on a variety of topics related to beauty and aesthetics.

OranClick is an AI-powered tool designed to help users optimize their click messages and track link performance to increase revenue. The platform leverages AI to provide actionable insights, automate messaging optimization, and enhance marketing efforts. With features like AI-driven link optimization, personalized messaging, and revenue maximization recommendations, OranClick aims to streamline content management and monetization for content creators.

JobWizard is an all-in-one job application copilot that transforms your approach to job applications. It is integrated with ChatGPT and offers a range of AI-powered features to help you save time, improve your accuracy, and increase your chances of success. With JobWizard, you can autofill job applications, generate custom questions and answers, analyze job descriptions, find similar jobs, optimize your resume with keywords, generate tailored cover letters, get referrals, and chat with an AI about your resume, position, and career. JobWizard is not an app or a website; it's a Chrome extension that pops up when you are on a job description page, seamlessly incorporating its AI-powered functionality into your existing job application process.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

TheySaid is a Conversational AI Survey tool that helps businesses drive customer value and revenue through AI-powered surveys. It offers a unique approach of engaging audiences with one relevant question at a time, turning feedback into an ongoing dialogue. The AI tool scans websites to generate tailored survey questions, automates follow-ups with customers, and provides actionable insights based on industry standards and best practices. TheySaid's AI digs deeper by asking personalized follow-up questions in real-time, enhancing customer engagement and satisfaction.

Lune is a language learning application that leverages AI technology to provide a personalized and dynamic learning experience. The app tracks your learning progress to create tailored content and courses. With a multifaceted approach, Lune offers various ways to learn languages, including holding conversations, guided learning, and reading stories. It categorizes every word you encounter and helps you remember vocabulary effortlessly. Additionally, Lune allows users to share their progress with friends and family. The app is designed to make language learning engaging, efficient, and accessible to all.

AirMDR is an AI-powered Managed Detection and Response (MDR) application that revolutionizes cybersecurity by leveraging artificial intelligence to automate routine tasks, enhance alert triage, investigation, and response processes. The application offers faster, higher-quality, and more affordable cybersecurity solutions, supervised by human experts. AirMDR aims to deliver unprecedented speed, superior quality, and cost-effective outcomes to cater to the unique demands of security operations centers.

Prosodica is a contact center analytics platform that uses AI and machine learning to analyze conversational speech behaviors and non-verbal measures to provide a human-like perspective of conversational quality. It helps businesses optimize operations, improve agent performance, and increase customer loyalty.

Prosodica is a cloud-based contact center analytics platform that uses AI and machine learning to analyze 100% of customer interactions. It provides real-time insights into agent performance, customer satisfaction, and business trends. Prosodica helps contact centers improve their operations, increase agent productivity, and drive customer loyalty.

Maintain-AI is an automated pavement assessment tool that utilizes cutting-edge computer vision and AI technologies to simplify and accelerate road assessments. It enables road asset professionals to make informed decisions by collecting and analyzing data from road network images. The tool aims to optimize the use of available budgets and improve the efficiency of pavement and infrastructure management through consistent and objective inspections.

The website offers a precision workflow tool for enterprises in the finance industry, combining AI technology with human oversight to empower financial decisions. It provides features such as accurate search citations, multilingual models, and complex human-in-loop automation. The tool integrates seamlessly with existing platforms, offers time savings, and self-improving models. It is backed by innovative generative AI solutions and neural search capabilities, with a focus on transforming data processes and decision-making in finance.

IMAGINaiTION is a company that uses artificial intelligence (AI) to create solutions for real-world problems. They believe that AI can be used to make the world a more inclusive and equitable place. One of their projects is a Mobile App Accessibility Audit Tool that uses AI to analyze mobile apps and identify areas where accessibility can be improved. This tool is designed to help developers create more accessible apps that can be used by everyone, regardless of their abilities.

Klipy is an AI-powered CRM platform that offers a comprehensive solution for managing all aspects of revenue growth. From automating manual tasks to providing insights through AI analytics, Klipy helps businesses streamline their sales processes and enhance customer relationships. The platform is designed to enrich, log, and organize deal flows, while also offering features like call analytics, playbooks, and pipeline orchestration. Klipy is trusted by various companies and is known for its security measures, ensuring data protection through encryption and anonymization. With a focus on growth operations management, Klipy aims to simplify the sales enablement process and improve customer loyalty.

Limbic is a clinical AI application designed for mental healthcare providers to save time, improve outcomes, and maximize impact. It offers a suite of tools developed by a team of therapists, physicians, and PhDs in computational psychiatry. Limbic is known for its evidence-based approach, safety focus, and commitment to patient care. The application leverages AI technology to enhance various aspects of the mental health pathway, from assessments to therapeutic content delivery. With a strong emphasis on patient safety and clinical accuracy, Limbic aims to support clinicians in meeting the rising demand for mental health services while improving patient outcomes and preventing burnout.

360Learning is a comprehensive learning platform that leverages AI and collaborative features to transform in-house experts into L&D collaborators. It enables organizations to upskill quickly and continuously within their own environment. The platform offers a range of features to facilitate collaborative learning, course creation, compliance training, employee onboarding, sales enablement, and frontline staff training. With a focus on data protection and security, 360Learning is trusted by over 2,300 customers for its innovative approach to corporate learning.

Comfy Org is an open-source AI tooling platform dedicated to advancing and democratizing AI technology. The platform offers tools like node manager, node registry, CLI, automated testing, and public documentation to support the ComfyUI ecosystem. Comfy Org aims to make state-of-the-art AI models accessible to a wider audience by fostering an open-source and community-driven approach. The team behind Comfy Org consists of individuals passionate about developing and maintaining various components of the platform, ensuring a reliable and secure environment for users to explore and contribute to AI tooling.

Cercle is an AI platform that advances healthcare for women by transforming healthcare data into real-time, high-quality insights. The platform caters to women's healthcare providers, payors, and pharma companies, helping them run more efficient businesses and provide personalized care. Cercle's Biomedical Graph unlocks insights at unprecedented speed and accuracy, optimizing patient care and improving outcomes in areas such as assisted reproduction and fertility processes.

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 contains a curated list of papers focusing on the self-correction of large language models (LLMs) during inference. It covers various frameworks for self-correction, including intrinsic self-correction, self-correction with external tools, self-correction with information retrieval, and self-correction with training designed specifically for self-correction. The list includes survey papers, negative results, and frameworks utilizing reinforcement learning and OpenAI o1-like approaches. Contributions are welcome through pull requests following a specific format.

Callytics is an advanced call analytics solution that leverages speech recognition and large language models (LLMs) technologies to analyze phone conversations from customer service and call centers. By processing both the audio and text of each call, it provides insights such as sentiment analysis, topic detection, conflict detection, profanity word detection, and summary. These cutting-edge techniques help businesses optimize customer interactions, identify areas for improvement, and enhance overall service quality. When an audio file is placed in the .data/input directory, the entire pipeline automatically starts running, and the resulting data is inserted into the database. This is only a v1.1.0 version; many new features will be added, models will be fine-tuned or trained from scratch, and various optimization efforts will be applied.

HuixiangDou2 is a robustly optimized GraphRAG approach that integrates multiple open-source projects to improve performance in graph-based augmented generation. It conducts comparative experiments and achieves a significant score increase, leading to a GraphRAG implementation with recognized performance. The repository provides code improvements, dense retrieval for querying entities and relationships, real domain knowledge testing, and impact analysis on accuracy.

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.

LLM-as-HH is a codebase that accompanies the paper ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution. It introduces Language Hyper-Heuristics (LHHs) that leverage LLMs for heuristic generation with minimal manual intervention and open-ended heuristic spaces. Reflective Evolution (ReEvo) is presented as a searching framework that emulates the reflective design approach of human experts while surpassing human capabilities with scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. The tool can improve various algorithms on problems like Traveling Salesman Problem, Capacitated Vehicle Routing Problem, Orienteering Problem, Multiple Knapsack Problems, Bin Packing Problem, and Decap Placement Problem in both black-box and white-box settings.

Co-LLM (Collaborative Language Models) is a tool for learning to decode collaboratively with multiple language models. It provides a method for data processing, training, and inference using a collaborative approach. The tool involves steps such as formatting/tokenization, scoring logits, initializing Z vector, deferral training, and generating results using multiple models. Co-LLM supports training with different collaboration pairs and provides baseline training scripts for various models. In inference, it uses 'vllm' services to orchestrate models and generate results through API-like services. The tool is inspired by allenai/open-instruct and aims to improve decoding performance through collaborative learning.

PrefixQuant is an official PyTorch implementation for static quantization that outperforms dynamic quantization in Large Language Models (LLMs) by utilizing prefixed outliers. The tool provides functionalities for quantization, inference, and visualization of activation distributions. Users can fine-tune quantization settings and evaluate pre-quantized models for tasks like PIQA, ARC, Hellaswag, and Winogrande. The approach aims to improve performance and efficiency in LLMs through innovative quantization techniques.

New AI Drug Discovery is a repository focused on the applications of Large Language Models (LLM) in drug discovery. It provides resources, tools, and examples for leveraging LLM technology in the pharmaceutical industry. The repository aims to showcase the potential of using AI-driven approaches to accelerate the drug discovery process, improve target identification, and optimize molecular design. By exploring the intersection of artificial intelligence and drug development, this repository offers insights into the latest advancements in computational biology and cheminformatics.

This repository provides a hands-on approach to Retrieval-Augmented Generation (RAG) techniques, simplifying advanced concepts into understandable implementations using Python libraries like openai, numpy, and matplotlib. It offers a collection of Jupyter Notebooks with concise explanations, step-by-step implementations, code examples, evaluations, and visualizations for various RAG techniques. The goal is to make RAG more accessible and demystify its workings for educational purposes.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

Mutahunter is an open-source language-agnostic mutation testing tool maintained by CodeIntegrity. It leverages LLM models to inject context-aware faults into codebase, ensuring comprehensive testing. The tool aims to empower companies and developers to enhance test suites and improve software quality by verifying the effectiveness of test cases through creating mutants in the code and checking if the test cases can catch these changes. Mutahunter provides detailed reports on mutation coverage, killed mutants, and survived mutants, enabling users to identify potential weaknesses in their test suites.

KnowAgent is a tool designed for Knowledge-Augmented Planning for LLM-Based Agents. It involves creating an action knowledge base, converting action knowledge into text for model understanding, and a knowledgeable self-learning phase to continually improve the model's planning abilities. The tool aims to enhance agents' potential for application in complex situations by leveraging external reservoirs of information and iterative processes.

The Next-Generation LLM-based Recommender Systems Survey is a comprehensive overview of the latest advancements in recommender systems leveraging Large Language Models (LLMs). The survey covers various paradigms, approaches, and applications of LLMs in recommendation tasks, including generative and non-generative models, multimodal recommendations, personalized explanations, and industrial deployment. It discusses the comparison with existing surveys, different paradigms, and specific works in the field. The survey also addresses challenges and future directions in the domain of LLM-based recommender systems.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

RAG-FiT is a library designed to improve Language Models' ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library assists in creating training data, training models using parameter-efficient finetuning (PEFT), and evaluating performance using RAG-specific metrics. It is modular, customizable via configuration files, and facilitates fast prototyping and experimentation with various RAG settings and configurations.

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