Tactful AI is an AI-powered customer engagement platform designed to boost customer retention, optimize operations, and maximize sales. It offers conversational analytics, AI help desk, AI chatbot, workflow automation, and content management functionalities. You can use Tactful AI to provide Comprehensive omnichannel customer support, ticketing, and CRM capabilities blended with advanced AI and automation technology for increased customer retention, optimized operations, and reduced costs. It can also be used to build AI powered chatbots for customer support.
Try Tactful.aiRemove.bg is an automated tool for removing backgrounds from images, with options to make backgrounds transparent or add a white background. It uses AI technology for a seamless experience, and can be used through a web interface or API. The tool has been integrated into popular design programs and e-commerce sites, making it a versatile addition to any workflow. There is also a blog with articles and ideas for using the tool.
Tara AI is a product delivery platform that helps engineering teams improve their efficiency and deliver better customer outcomes. It provides real-time insight and alerts on delivery progress and allows for project scope and task prioritization. The tool integrates with existing tools like GitHub, Asana, Slack, and Trello. Tara AI helps improve team communication and provides meaningful insights to uncover blockers and improve performance.
MiniGPT-4 is an AI model that focuses on enhancing vision-language understanding using advanced large language models.It is based on the idea that the advanced multi-modal generation capabilities of models like gpt-4 can be attributed to the utilization of a large language model (llm).minigpt-4 aligns a frozen visual encoder with a frozen llm called vicuna using one projection layer.It exhibits similar capabilities to gpt-4, such as generating detailed image descriptions and creating websites based on hand-written drafts.Additionally, minigpt-4 can write stories and poems inspired by given images, provide solutions to problems shown in images, and even teach users how to cook based on food photos.The architecture of minigpt-4 consists of a vision encoder pretrained with vit q-former, a single linear projection layer, and the advanced vicuna large language model.The training of the linear layer is necessary to align visual features with vicuna.The model is highly computationally efficient, requiring approximately 5 million aligned image-text pairs for training the projection layer.