The Receptionist for iPad
The Receptionist iPad software allows visitors to manage their visits and calm down the chaos in the front office. Our digital check in solution can be customized to meet your needs. You can choose to use configurable buttons or drag-and-drop badge printing. You can effectively manage and track all visitors to your workspace, and securely store the information in the cloud. No more paper visitor logs!
Ask your guests for key information at check-in. This is whether you need it to comply with ITAR, C-TPAT, FSMA or PCI compliance or to build a human connection with them. Your employees can communicate with their guests via our unique two-way communication feature before they even reach the lobby.
The Receptionist will make a profound impression on your guests.
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Visual Visitor
AI Sales Rep - Your Next Salesperson Might Not Be A Person at All
Identify and Influence Your Engaged Website Visitors into Sales-Ready Leads – Before You Commit a Single Working Hour.
Lower Funnel, Higher Value Leads: Using our advanced WebID +Person identification technology, we uncover and identify the most engaged visitors to your site. These are the prospects we focus on, ensuring maximum impact for your sales efforts.
- Detailed Prospect Data: We gather 40 points of data about each prospect, including first name, last name, email address, and more.
- Engaged, But Anonymous: These prospects are conducting online research but haven’t met with your sales team yet.
- Crucial Sales Funnel Position: These visitors are deep in your sales funnel, spending time on your key ‘buying pages’ but remaining unknown to you. They are the ones most likely to convert into appointments.
- AI-Driven Engagement: Our AI Sales Rep identifies and gently engages with these visitors, influencing them to express interest. The process is fully automated, so your sales team only needs to engage with the interested leads—your low-hanging fruit.
Leverage the power of AI to turn your website visitors into meeting-ready leads effortlessly.
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GLM-OCR
GLM-OCR is an advanced multimodal optical character recognition system and an open-source framework that excels in delivering precise, efficient, and thorough document comprehension by integrating textual and visual elements within a cohesive encoder-decoder design inspired by the GLM-V series. This model features a visual encoder that has been pre-trained on extensive image-text datasets alongside a streamlined cross-modal connector that channels information into a GLM-0.5B language decoder. It offers capabilities for layout detection, simultaneous recognition of various regions, and structured outputs for diverse content types, including text, tables, formulas, and intricate real-world document formats. Furthermore, it employs Multi-Token Prediction (MTP) loss and robust full-task reinforcement learning techniques to enhance training efficiency, boost recognition accuracy, and improve generalization across various tasks, leading to remarkable performance on significant document understanding challenges. This innovative approach not only sets new benchmarks but also opens up possibilities for further advancements in the field of document analysis.
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CodeT5
CodeT5 is an innovative pre-trained encoder-decoder model specifically designed for understanding and generating code. This model is identifier-aware and serves as a unified framework for various coding tasks. The official PyTorch implementation originates from a research paper presented at EMNLP 2021 by Salesforce Research. A notable variant, CodeT5-large-ntp-py, has been fine-tuned to excel in Python code generation, forming the core of our CodeRL approach and achieving groundbreaking results in the APPS Python competition-level program synthesis benchmark. This repository includes the necessary code for replicating the experiments conducted with CodeT5. Pre-trained on an extensive dataset of 8.35 million functions across eight programming languages—namely Python, Java, JavaScript, PHP, Ruby, Go, C, and C#—CodeT5 has demonstrated exceptional performance, attaining state-of-the-art results across 14 different sub-tasks in the code intelligence benchmark known as CodeXGLUE. Furthermore, it is capable of generating code directly from natural language descriptions, showcasing its versatility and effectiveness in coding applications.
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