This machine Sends the report via email.

This machine fetches the guest list and in-house list, processes them with Gemini AI, and generates a summary report.

This machine will get the guest list and in house list from email and pass csv text and files on to the next machine.

This machine fetches the guest list and in-house list, processes them with OpenAI, and generates a summary report.

Annual Summary Overview machine is the fifth machine in workflow for          summarize the occupancy data in table. It’s provide Occupancy, RMS, ADR, Revenue and         same things from the last year.

Choice PMS Connection machine is the third machine in workflow for       collecting inputes from the user and getting reservation and occupancy reports.

Choice PMS Res Standardization machine is the forth machine in workflow for making reservation and occupancy data to standard table structure which are       used for all of the properties.

It seems you might be referring to Net Standard Last Year (LY), which is a common term in the hotel industry (and other industries) used to compare the financial performance or key metrics of the current year with the same period in the previous year. LY (Last Year) is typically used in reports and data analysis to assess growth, trends, and changes.

"On the books by month" data in the hotel industry, specifically about short and long details. In this context, "On the Books" refers to the current reservations or bookings that a hotel has made for upcoming periods. This is an important metric for hotel managers to forecast occupancy and revenue.

Let’s break this down in terms of short-term and long-term bookings and the details that might be provided in such reports:

This machine fetches the username and password of the hotel login system and securely encrypts the credentials.

This Machine send emails with user-defined recipients, subject, message, and attachments. Supports multiple recipients (To, CC, BCC) and securely delivers emails via SMTP with authentication, including XLSX,PDF and other file formats.

"Total Last Year" (LY) in the context of data or reporting. This phrase is often used in various industries to refer to a comparison between the current year's performance and last year's performance. It can be applied to financial metrics, sales, customer data, or any other key performance indicators (KPIs)

This machine retrieves pending requests, and gives available rooms and BAR (Best Available Rate) details for further processing.

The Generic Events Machine utilizes PredictHQ to discover events based on a saved hotel location. It fetches event data by leveraging latitude, longitude, and location ID, helping users stay informed about local happenings within a specified date range.

• Rateshop, developed by RateGain, is a pricing and revenue management platform designed for the hospitality industry. It helps hotels and other accommodation providers manage and optimize their pricing strategy by offering tools for:
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This machine fetches the hotel and their competitor rates from the rategain by using some filters.

This machine handles RateShop requests by retrieving room availability and Best Available Rate (BAR) information. It ensures accurate data processing through predefined workflows and outputs structured hotel and rate details. If not using external data, it accepts a JSON input with HotelName, RateShopURL, StartDate, and EndDate as required keys, making it a vital component of the ICRM RateShop system.

The ICRM RateShop Request machine serves as the initial component in the workflow, responsible for gathering and organizing all incoming RateShop requests. It ensures that each request is accurately collected, validated, and prepared for further analysis and processing.

The ICRM Rateshop Update Machine processes incoming RateShop data, stores it in the database, and automates the generation of summary emails. It streamlines data handling and ensures that stakeholders receive timely updates, contributing to a more efficient RateShop workflow.

This machine generates company names based on AI-processed text summaries.

This machine is responsible for uploading and storing all forecasted data into the database. It ensures that the generated forecast values are securely saved, well-structured, and made readily available for business analysis and reporting.

Processes company data, structures it for easy access, and provides key information.

Converts JSON data to CSV format and provides the output as base64-encoded string.

Converts JSON data to CSV format and provides the output as base64-encoded string.

This machine uses Selenium to extract hotel URLs from Google and then gathers detailed data, including reviews, information, and ratings from Google.

This machine uses a pre-trained model to analyze customer review sentiments based on extracted aspects, identifying positive, negative, or neutral feedback.

This machine retrieves sentiment-analyzed hotel data and inserts it into a database for further use and analysis.

Converts JSON data to Excel format and provides the output as base64-encoded string.

The Mongo DB Connection Machine serves as a critical component within the Quantum Datalytica platform, responsible for establishing, verifying, and managing connections to MongoDB databases. It simplifies backend integration by handling connection logistics, ensuring smooth data access for downstream processes.

The PostgreSQL Connection Machine serves as a critical component within the Quantum Datalytica platform, responsible for establishing, verifying, and managing connections to PostgreSQL databases. It simplifies backend integration by handling connection logistics, ensuring smooth data access for downstream processes.

This S3 Bucket machine performs two main tasks: reading files from an S3 bucket and uploading files to it, based on the user-provided configuration and file data. And if user in write mode then it returning the S3 object URL and file name, with optional folder placement within the bucket.

The MySQL Connection Machine serves as a critical component within the Quantum Datalytica platform, responsible for establishing, verifying, and managing connections to MySQL databases. It simplifies backend integration by handling connection logistics, ensuring smooth data access for downstream processes.

The MS SQL Connection Machine serves as a critical component within the Quantum Datalytica platform, responsible for establishing, verifying, and managing connections to MS SQL databases. It simplifies backend integration by handling connection logistics, ensuring smooth data access for downstream processes.

This machine fetches the hotel and their competitor rates from the agoda.

This machine uses APIs to fetch hotel reviews and ratings from Tripadvisor in the selected language.

This machine fetches the hotel and their competitor rates from the expedia.

This machine fetches the hotel and their competitor rates from the booking.com.

TOS POS Daily Closing Machine is responsible for processing daily closing reports from the Point of Sale (POS) system. It handles financial data related to various payment methods and ensures accurate reporting for business reconciliation.

TOS POS Daily Sales Machine is responsible for processing daily sales reports from the Point of Sale (POS) system. It handles financial data related to various payment methods and ensures accurate reporting for business reconciliation.

This machine uses Selenium to extract hotel URLs from Google and then gathers detailed data, including reviews, information, and ratings from Google.

This machine uses a pre-trained model to analyze customer review sentiments based on extracted aspects, identifying positive, negative, or neutral feedback.

This machine retrieves RateShop requests and provides available room details along with the Best Available Rate (BAR).

A domain extraction machine that classifies metadata into relevant business domains and subdomains, providing structured categorization for better data organization.

Metadata Extractor connects to a database using a connection string and extracts its metadata. It supports multiple database types.

This Rateshop Lighthouse connects to a database, retrieves property information, fetches Lighthouse reports from Outlook, processes rate shop data, and updates the database with the new rate information for further analysis.

This machine takes Google Service Account credentials to authenticate and access a restricted Google Sheet via a provided link. It then extracts the Google Sheet data, processes it, and converts it into JSON format.

This machine automates the process of delivering generated invoices to clients via Gmail. It retrieves invoice PDFs and client email addresses from the structured JSON data and securely sends the invoices as attachments.

This machine integrates with Google Sheets to convert sheet data into JSON format. It then validates whether all required keys are present in the JSON. If all necessary fields are available, the system proceeds to create an invoice. The invoice is formatted with essential details, including client information, itemized charges, and total amounts. Once generated, it is downloaded and stored in PDF format.