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.

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.

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.

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.

Nametag is an identity verification solution designed specifically for IT helpdesks. It helps businesses prevent social engineering attacks, account takeovers, and data breaches by verifying the identity of users at critical moments, such as password resets, MFA resets, and high-risk transactions. Nametag's unique approach to identity verification combines mobile cryptography, device telemetry, and proprietary AI models to provide unmatched security and better user experiences.

SentinelOne is an advanced enterprise cybersecurity AI platform that offers a comprehensive suite of AI-powered security solutions for endpoint, cloud, and identity protection. The platform leverages AI technology to anticipate threats, manage vulnerabilities, and protect resources across the enterprise ecosystem. SentinelOne provides real-time threat hunting, managed services, and actionable insights through its unified data lake, empowering security teams to respond effectively to cyber threats. With a focus on automation, efficiency, and value maximization, SentinelOne is a trusted cybersecurity solution for leading enterprises worldwide.

Codimite is an AI-assisted offshore development company that provides a range of services to help businesses accelerate their software development, reduce costs, and drive innovation. Codimite's team of experienced engineers and project managers use AI-powered tools and technologies to deliver exceptional results for their clients. The company's services include AI-assisted software development, cloud modernization, and data and artificial intelligence solutions.

SentinelOne is an advanced enterprise cybersecurity AI platform that offers a comprehensive suite of AI-powered security solutions for endpoint, cloud, and identity protection. The platform leverages artificial intelligence to anticipate threats, manage vulnerabilities, and protect resources across the entire enterprise ecosystem. With features such as Singularity XDR, Purple AI, and AI-SIEM, SentinelOne empowers security teams to detect and respond to cyber threats in real-time. The platform is trusted by leading enterprises worldwide and has received industry recognition for its innovative approach to cybersecurity.

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 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.

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.

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.

This repository aims to build a low-parameter LLM model through pretraining, fine-tuning, model rewarding, and reinforcement learning stages to create a chat model capable of simple conversation tasks. It features using the bert4torch training framework, seamless integration with transformers package for inference, optimized file reading during training to reduce memory usage, providing complete training logs for reproducibility, and the ability to customize robot attributes. The chat model supports multi-turn conversations. The trained model currently only supports basic chat functionality due to limitations in corpus size, model scale, SFT corpus size, and quality.

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.

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.

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.

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.

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.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

InfLLM is a training-free memory-based method that unveils the intrinsic ability of LLMs to process streaming long sequences. It stores distant contexts into additional memory units and employs an efficient mechanism to lookup token-relevant units for attention computation. Thereby, InfLLM allows LLMs to efficiently process long sequences while maintaining the ability to capture long-distance dependencies. Without any training, InfLLM enables LLMs pre-trained on sequences of a few thousand tokens to achieve superior performance than competitive baselines continually training these LLMs on long sequences. Even when the sequence length is scaled to 1, 024K, InfLLM still effectively captures long-distance dependencies.

last_layer is a security library designed to protect LLM applications from prompt injection attacks, jailbreaks, and exploits. It acts as a robust filtering layer to scrutinize prompts before they are processed by LLMs, ensuring that only safe and appropriate content is allowed through. The tool offers ultra-fast scanning with low latency, privacy-focused operation without tracking or network calls, compatibility with serverless platforms, advanced threat detection mechanisms, and regular updates to adapt to evolving security challenges. It significantly reduces the risk of prompt-based attacks and exploits but cannot guarantee complete protection against all possible threats.

This repository contains a vulnerability mining engine based on GPT technology. The engine is designed to identify logic vulnerabilities in code by utilizing task-driven prompts. It does not require prior knowledge or fine-tuning and focuses on prompt design rather than model design. The tool is effective in real-world projects and should not be used for academic vulnerability testing. It supports scanning projects in various languages, with current support for Solidity. The engine is configured through prompts and environment settings, enabling users to scan for vulnerabilities in their codebase. Future updates aim to optimize code structure, add more language support, and enhance usability through command line mode. The tool has received a significant audit bounty of $50,000+ as of May 2024.

This is a vulnerability mining engine purely based on GPT, requiring no prior knowledge base, no fine-tuning, yet its effectiveness can overwhelmingly surpass most of the current related research. The core idea revolves around being task-driven, not question-driven, driven by prompts, not by code, and focused on prompt design, not model design. The essence is encapsulated in one word: deception. It is a type of code understanding logic vulnerability mining that fully stimulates the capabilities of GPT, suitable for real actual projects.

Enhances customer loyalty and reduces churn rates.

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