Simple Phones
|
Tags
|
Pricing model
Upvote
0
Simple Phones offers an AI-driven answering service for companies, delivering round-the-clock customer assistance. It removes the necessity for expensive human labor, guaranteeing that calls are managed consistently and professionally. The service includes automated call responses, call routing, call logging, competitive pricing, mobile-friendly account management, and customer support.
Similar neural networks:
Upvote
0
Limeline is a meeting assistant driven by AI, aiming to simplify and automate the meeting process. It enables users to generate automated AI agents for meetings and calls, distribute call links, and obtain real-time transcripts and summaries. Limeline is adaptable, catering to various scenarios such as sales, customer success, research, recruitment, marketing, IT, and education. The platform allows users to hold meetings without being present, revisit calls at any time, and respond with flexibility. It also features a customized call experience, integrates with mobile browsers, and offers extensive API support, facilitating effortless creation and management of AI-curated meetings.
Upvote
0
The Pin tool is a platform that enhances team collaboration within Slack, making it both more effective and efficient. Users can pin messages using an emoji, which then automatically produces a summary and highlights action items. Additionally, it includes a portal for assigning owners and monitoring progress, and it stores relevant documents for easy retrieval. The tool is also affordably priced for various team sizes.
Upvote
0
LM Studio is a software app enabling users to execute large language models (LLMs) on their personal devices without requiring an internet connection, thus providing improved privacy and data security. It accommodates various models like LLaMa, Falcon, MPT, Gemma, and others from Hugging Face repositories. Users can engage with models via an in-app Chat UI or a local server that is OpenAI's API compatible. LM Studio is perfect for individuals aiming to utilize LLMs for personal projects, research, or development while keeping full control of their data. This approach to locally running machine learning models is particularly appealing to those who prioritize privacy or handle sensitive information.