Libraries tagged by contactus

Simple PHP SDK for sending SMS via BulkGate portal. For credentials contact us.

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Add Contact Us Page in sidenav

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Magento 2 Contact Us form response email

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Módulo de Fale Conosco

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A simple Contact Us form for customer queries. This package allows users to submit inquiries or feedback via a contact form

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simple form

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Allow customers to contact you using Whatsapp

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Magento 2. Contact Us Tracker. Contact Us History. Contact Us Grid

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OroCRM Magento Embedded Contact Us package

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Laravel - Messages [Contact us].

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Laravel contact us form package to send email and save to database

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Simple contact form to quickly add to your laravel project. Fully customisable, includes all views, routes and controllers. Also includes anti-spam (with recaptcha). You can also have multiple contact forms on one Laravel site.

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Extension will assist in adding company or store coordinates with maps directly to the contact us page.

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## Introduction Send messages using unifonic’s Conversations API. Message your target audience over social channels (WhatsApp for Business, Messenger, SMS, etc..). Unifonic conversations API Restful and HTTP **API's** uses The basic Authentication protocol. All request and response bodies are formatted in JSON. ## Get an account To start using the API you need to send an email [email protected] to create an account for you. ## Base URL All URLs referenced in the documentation have the following base: **apis.unifonic.com** ## Security To ensure privacy, we recommend you to use HTTPS for all unifonic API requests. ## Formats conversations API only supports JSON format. All requests must use the Content-type header set to application/json. ## Support We’re here to help! Get in touch with support at and we’ll get back to you as soon as we can or you can contact us throw live chat on our [website] (www.unifonic.com).

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