Best AI tools for< Reduce Churn >

Echurn is an AI-driven tool designed to help SaaS businesses reduce churn and increase customer retention. It provides insights, suggestions, and customizable offers to prevent customer cancellations. With effortless integration and affordable pricing, Echurn aims to enhance SaaS MRR and CLV by leveraging advanced technology and data-driven decisions.

FLOWIT is an AI-powered coach designed for employee retention and growth. It offers a modern, holistic HR solution for people development in companies, focusing on frontline employees and desk workers. The tool provides personalized goal planning, seamless feedback integration, pulse surveys, and automatic translations in over 100 languages to enhance communication and professional development.

SupportLogic is a cloud-based support experience management platform that uses AI to help businesses improve their customer support operations. The platform provides a range of features, including sentiment analysis, case routing, and quality monitoring, that can help businesses to identify and resolve customer issues quickly and efficiently. SupportLogic also offers a number of integrations with popular CRM and ticketing systems, making it easy to implement and use.

SupportLogic is a Support Experience Management Platform that uses AI to help businesses improve their customer support operations. It offers a range of features, including sentiment analysis, backlog management, intelligent case routing, proactive alerts, swarming and collaboration, account health management, customer support analytics, text analytics, SLA/SLO management, quality monitoring and coaching, agent productivity, and translation. SupportLogic integrates with existing ticketing systems and apps, and can be implemented within 45 days.

SiteRecon is an AI-powered mapping tool designed for landscapers, offering features such as automated takeoffs, site condition reports, job plans, account management, and enhancement proposals. It helps landscapers streamline their sales process, improve productivity, and enhance client relations by providing accurate measurements, detailed site audits, and work reports. SiteRecon aims to revolutionize the landscaping industry by leveraging AI technology to simplify property mapping, estimate generation, and job management, ultimately leading to increased efficiency and profitability for landscaping businesses.

Hook is an AI-powered platform designed to help businesses grow their revenue by accurately predicting high-value customers, reducing churn, increasing expansion revenue, and actively managing accounts. The platform uses AI to analyze usage patterns and third-party data to prioritize customers for revenue growth and upsells. With a focus on data-driven decision-making, Hook empowers revenue teams to make informed choices and drive business growth.

Orango AI is an AI application that provides AI agents for support and onboarding, guiding users through product usage. It offers autonomous UX testing, AI data extraction, and RPA solutions. The application helps increase user activation, reduce churn, and improve user experience by providing real-time guidance and suggestions based on user context and expertise.

DesignRoasts is a web-based tool that provides personalized AI insights to help you optimize your website or app. Simply upload a screenshot of your product and select your goal (e.g., increase conversions, improve onboarding, etc.), and DesignRoasts will generate a list of actionable feedback tailored to your specific needs. The feedback focuses on improving the user experience, visual design, copywriting, and more.

YOMO is an AI-powered platform designed to help product teams build products that boost revenue and reduce churn. It offers a suite of tools for collecting feedback, conducting research, collaborating, and organizing information. YOMO leverages AI to provide automated insights, prioritize feedback, and uncover user needs and desires. The platform aims to streamline product development processes and empower teams to make data-driven decisions.

Slicker is an AI-powered tool designed to recover failed subscription payments and maximize subscription revenue for businesses. It uses a proprietary AI engine to process each failing payment individually, converting past due invoices into revenue. With features like payment recovery on auto-pilot, state-of-the-art machine learning model, lightning-fast setup, in-depth payment analytics, and enterprise-grade security, Slicker offers a comprehensive solution to reduce churn and boost revenue. The tool is fully transparent, allowing users to inspect and review every action taken by the AI engine. Slicker seamlessly integrates with popular billing and payment platforms, making it easy to implement and start seeing results quickly.

Deskflow is an employee experience platform powered by AI. It is designed to help companies improve employee productivity and reduce churn. Deskflow's AI is trained on a company's internal knowledge base and integrates with HRIS and ITSM systems to handle repetitive help desk tasks 10X faster. This frees up HR and IT teams to focus on more critical tasks, leading to cost savings. Deskflow also provides employees with a co-pilot that can answer questions, create tickets, and take other actions. This helps employees access the information they need quickly and easily, reducing the time it takes to resolve issues.

CMSWire is the world's leading community of customer experience professionals, providing the latest news, expert advice, and in-depth analysis on customer-first marketing, commerce, and digital experience design.

SMOC.AI is a digital sales agent that uses AI to help businesses capture leads, convert them into customers, and increase sales. It offers a variety of features, including automated chatbots, personalized flows, and gamification. SMOC.AI is designed to be easy to use and can be integrated with a variety of marketing and sales tools.

Fini is an AI application that turns your knowledge base into an AI chat in just 2 minutes. It helps businesses supercharge their customer support by resolving 70% of customer questions with AI agents, saving costs, and keeping customers happy. Fini securely integrates with private data and provides AI agents ready 24/7 to solve customer queries on platforms like Zendesk, Slack, and Discord. It also helps growth teams at PLG companies identify reasons for churn and deliver personalized experiences to retain existing customers.

Saara Inc is an AI tool for eCommerce that focuses on maximizing profits by leveraging AI-powered automation and smart agents. The platform helps online stores increase profitability by addressing challenges such as high return rates, operational costs, and customer churn. By enhancing loyalty, reducing expenses, and streamlining processes through automation and AI, Saara enables businesses to achieve sustainable growth and long-term profitability.

RetentionX is a customer retention platform designed for consumer brands aiming to excel in the digital era. It helps businesses prevent churn, increase retention, optimize acquisition, maximize sell-through, automate workflows, and reduce costs by centralizing customer data and decision-making processes. The platform leverages AI to provide actionable insights, analytics, and segmentation capabilities to enhance customer relationships and drive revenue growth.

Tomato.ai is an AI accent softening and neutralization software designed to improve customer service and sales metrics in call centers. The software uses AI-powered voice filters to clarify offshore agent voices, making them more intelligible and reducing customer frustration. Tomato.ai offers benefits such as improving CSAT, reducing agent churn, boosting savings and sales, and enabling the hiring of more offshore agents. The software works in real-time to soften accents, enhance voice quality, cancel noise, and preserve the natural rhythm of the speaker.

Xound.io is an AI-powered voice cleaner and background noise removal tool designed for content creators, podcasters, YouTubers, TikTokers, and anyone who wants to improve the audio quality of their content. It uses advanced algorithms to remove background noise, enhance vocals, and improve the overall listening experience. Xound.io is easy to use, with a simple drag-and-drop interface and no need for any technical expertise. It also offers a variety of features, including natural pitch correction, AI background noise removal, and high-frequency presence.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

DailyBot is an AI-powered toolkit for teams that want automation, better reporting, and customization. It offers a range of features to enhance team visibility, reduce meetings, and improve collaboration. With DailyBot, teams can run asynchronous standups, retros, and other meetings, send kudos and recognition, create surveys and collect data, and access a variety of add-ons like watercoolers and random coffees. DailyBot also integrates with popular tools like Zapier, Jira, and Trello, making it easy to connect with the tools teams already use. Trusted by leading companies and backed by Y Combinator, DailyBot is a valuable tool for teams looking to improve their collaboration and productivity.

This Python application, 'Youtube-playlist-to-formatted-text', utilizes the Google Gemini API to extract and refine transcripts from YouTube playlists. It offers various refinement styles such as Balanced and Detailed, Summary, Educational, Narrative Rewriting, and Q&A Generation. Users can control the chunk size for API calls, select Gemini models, and output the refined transcript as a formatted markdown file. The tool is designed to convert lengthy YouTube playlists into organized text files for easy readability and further processing, suitable for tasks like summarizing videos, creating study guides, and enhancing content comprehension.

The Medical_Image_Analysis repository focuses on X-ray image-based medical report generation using large language models. It provides pre-trained models and benchmarks for CheXpert Plus dataset, context sample retrieval for X-ray report generation, and pre-training on high-definition X-ray images. The goal is to enhance diagnostic accuracy and reduce patient wait times by improving X-ray report generation through advanced AI techniques.

This repository contains an implementation of Ring Attention, a technique for processing large sequences in transformers. Ring Attention splits the data across the sequence dimension and applies ring reduce to the processing of the tiles of the attention matrix, similar to flash attention. It also includes support for Striped Attention, a follow-up paper that permutes the sequence for better workload balancing for autoregressive transformers, and grouped query attention, which saves on communication costs during the ring reduce. The repository includes a CUDA version of the flash attention kernel, which is used for the forward and backward passes of the ring attention. It also includes logic for splitting the sequence evenly among ranks, either within the attention function or in the external ring transformer wrapper, and basic test cases with two processes to check for equivalent output and gradients.

HaE is a framework project in the field of network security (data security) that combines artificial intelligence (AI) large models to achieve highlighting and information extraction of HTTP messages (including WebSocket). It aims to reduce testing time, focus on valuable and meaningful messages, and improve vulnerability discovery efficiency. The project provides a clear and visual interface design, simple interface interaction, and centralized data panel for querying and extracting information. It also features built-in color upgrade algorithm, one-click export/import of data, and integration of AI large models API for optimized data processing.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

Cortex is a tool that simplifies and accelerates the process of creating applications utilizing modern AI models like chatGPT and GPT-4. It provides a structured interface (GraphQL or REST) to a prompt execution environment, enabling complex augmented prompting and abstracting away model connection complexities like input chunking, rate limiting, output formatting, caching, and error handling. Cortex offers a solution to challenges faced when using AI models, providing a simple package for interacting with NL AI models.

FIT Framework is a Java enterprise AI development framework that provides a multi-language function engine (FIT), a flow orchestration engine (WaterFlow), and a Java ecosystem alternative solution (FEL). It runs in native/Spring dual mode, supports plug-and-play and intelligent deployment, seamlessly unifying large models and business systems. FIT Core offers language-agnostic computation base with plugin hot-swapping and intelligent deployment. WaterFlow Engine breaks the dimensional barrier of BPM and reactive programming, enabling graphical orchestration and declarative API-driven logic composition. FEL revolutionizes LangChain for the Java ecosystem, encapsulating large models, knowledge bases, and toolchains to integrate AI capabilities into Java technology stack seamlessly. The framework emphasizes engineering practices with intelligent conventions to reduce boilerplate code and offers flexibility for deep customization in complex scenarios.

Simple-OpenAI is a Java library that provides a simple way to interact with the OpenAI API. It offers consistent interfaces for various OpenAI services like Audio, Chat Completion, Image Generation, and more. The library uses CleverClient for HTTP communication, Jackson for JSON parsing, and Lombok to reduce boilerplate code. It supports asynchronous requests and provides methods for synchronous calls as well. Users can easily create objects to communicate with the OpenAI API and perform tasks like text-to-speech, transcription, image generation, and chat completions.

The GPT Server project leverages the basic capabilities of FastChat to provide the capabilities of an openai server. It perfectly adapts more models, optimizes models with poor compatibility in FastChat, and supports loading vllm, LMDeploy, and hf in various ways. It also supports all sentence_transformers compatible semantic vector models, including Chat templates with function roles, Function Calling (Tools) capability, and multi-modal large models. The project aims to reduce the difficulty of model adaptation and project usage, making it easier to deploy the latest models with minimal code changes.

LLMInterviewQuestions is a repository containing over 100+ interview questions for Large Language Models (LLM) used by top companies like Google, NVIDIA, Meta, Microsoft, and Fortune 500 companies. The questions cover various topics related to LLMs, including prompt engineering, retrieval augmented generation, chunking, embedding models, internal working of vector databases, advanced search algorithms, language models internal working, supervised fine-tuning of LLM, preference alignment, evaluation of LLM system, hallucination control techniques, deployment of LLM, agent-based system, prompt hacking, and miscellaneous topics. The questions are organized into 15 categories to facilitate learning and preparation.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

AWQ (Activation-aware Weight Quantization) is a tool designed for efficient and accurate low-bit weight quantization (INT3/4) for Large Language Models (LLMs). It supports instruction-tuned models and multi-modal LMs, providing features such as AWQ search for accurate quantization, pre-computed AWQ model zoo for various LLMs, memory-efficient 4-bit linear in PyTorch, and efficient CUDA kernel implementation for fast inference. The tool enables users to run large models on resource-constrained edge platforms, delivering more efficient responses with LLM/VLM chatbots through 4-bit inference.

Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

DuoAttention is a framework designed to optimize long-context large language models (LLMs) by reducing memory and latency during inference without compromising their long-context abilities. It introduces a concept of Retrieval Heads and Streaming Heads to efficiently manage attention across tokens. By applying a full Key and Value (KV) cache to retrieval heads and a lightweight, constant-length KV cache to streaming heads, DuoAttention achieves significant reductions in memory usage and decoding time for LLMs. The framework uses an optimization-based algorithm with synthetic data to accurately identify retrieval heads, enabling efficient inference with minimal accuracy loss compared to full attention. DuoAttention also supports quantization techniques for further memory optimization, allowing for decoding of up to 3.3 million tokens on a single GPU.

Awesome Green AI is a curated list of resources and tools aimed at reducing the environmental impacts of using and deploying AI. It addresses the carbon footprint of the ICT sector, emphasizing the importance of AI in reducing environmental impacts beyond GHG emissions and electricity consumption. The tools listed cover code-based tools for measuring environmental impacts, monitoring tools for power consumption, optimization tools for energy efficiency, and calculation tools for estimating environmental impacts of algorithms and models. The repository also includes leaderboards, papers, survey papers, and reports related to green AI and environmental sustainability in the AI sector.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

This repository focuses on unraveling the sources that large language models tap into for attribution or citation. It delves into the origins of facts, their utilization by the models, the efficacy of attribution methodologies, and challenges tied to ambiguous knowledge reservoirs, biases, and pitfalls of excessive attribution.

Enhances customer loyalty and reduces churn rates.

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Improves operational efficiency by optimizing processes and reducing waste.

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