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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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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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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Mu
On June 23, 2025, Microsoft unveiled Mu, an innovative 330-million-parameter encoder–decoder language model specifically crafted to enhance the agent experience within Windows environments by effectively translating natural language inquiries into function calls for Settings, all processed on-device via NPUs at a remarkable speed of over 100 tokens per second while ensuring impressive accuracy. By leveraging Phi Silica optimizations, Mu’s encoder–decoder design employs a fixed-length latent representation that significantly reduces both computational demands and memory usage, achieving a 47 percent reduction in first-token latency and a decoding speed that is 4.7 times greater on Qualcomm Hexagon NPUs when compared to other decoder-only models. Additionally, the model benefits from hardware-aware tuning techniques, which include a thoughtful 2/3–1/3 split of encoder and decoder parameters, shared weights for input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, allowing for swift inference rates exceeding 200 tokens per second on devices such as the Surface Laptop 7, along with sub-500 ms response times for settings-related queries. This combination of features positions Mu as a groundbreaking advancement in on-device language processing capabilities.
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