.NET application delivery

Moёt Hennessy

About Moёt Hennessy USA

Moёt Hennessy USA is the leading importer and marketer of luxury wines, spirits and champagnes in the U.S. Moët Hennessy USA combines the expertise, brand portfolios, sales and marketing networks of all distributed brands in the US.

Global company, global needs

To support its global communication and collaboration goals, Moёt Hennessy USA wanted to implement additional technology solutions that were best of breed, mobile-ready, and worked in the cloud.

The Solution

Strategic technology choices

We evaluated a range of collaboration platforms with its three criteria in mind, we decided that Office 365 was one of the best choices for collaboration that also gave the flexibility to choose between cloud-based and onsite software deployments.

Simple sharing and seamless collaboration

Engage and inform

  • Feature of the Day/News
  • Employee Directory
  • Latest Scores
  • News Carousel
  • Bulletin Board

Harness collective knowledge

  • Help Desk
  • Training
  • Document portal
  • Executive Video Series
  • Career Development Center

Search Functions

  • Employee
  • Job
  • News
Moet Hennessy USA

Results

Increased efficiency through custom apps

The company has benefited from its transition to SharePoint Online, which is the basis for both the intranet and a range of custom applications designed to help employees contribute more directly to the company's success. Teams can now easily share files, data, news, and resources across PCs, Macs, and mobile devices.

 

Microsoft SharePoint and CRM 365 Cloud Implementation

Moёt Hennessy

Microsoft Dynamics® GP and CRM Business Solution, and Microsoft Office SharePoint® Server

Salesforce Implementation

Moёt Hennessy

Visual Force, Apex SOQL, SOAP, REST

With the goal to strengthen its global distribution network, Moёt reached out to Fair Pattern to build an integrated UI for Salesforce which brings together their multiple distributors across the US so that business can track data in one place. They needed to automate business processes and setup the ability to backtrack.

Our help was needed for IOS & Heroku App enhancements for various business workflows for both mobile and desktop usability.

The Challenge

Moёt Hennessy had a need to integrate all data points from their various distributors into one system. Prior to the solutions Fair Pattern proposed, data from the various distributors had to be aggregated for accurate ROI and reporting capabilities. The Salesforce app needed to be mobile ready.

The Solution

Fair Pattern built an integrated UI for both mobile and desktop including:

Journey Planning:

Developed new UI with changing functionality connecting to multiple objects

Account view showing different kinds of act ivies with photo capturing capability, affecting different objects

Integration with Distributors:

We integrated various distributors using dell boomi API.

Enable report generation in Salesforce including subscription rules

 

Hennessy USA Case Study
Hennessy USA Case Study

 

Objective C and J2

Export Bar Mobile App

Export Bar Mobile App

The Export Bar app allows sellers to connect with buyers in a unique social media platform. It is designed to maximize exposure of products and factories through our auto matchmaking and positioning algorithm, as well as through the relentless efforts of our in-house marketing team who connect "quality manufacturers with quality buyers".

The Challenge

The application was written using Objective C and J2 for iOS and Android platform respectively with a Yii, Node.js backend architecture and RESTFUL API's on Amazon AWS cloud infrastructure with node based elastic EC2 servers along with a load balancer bridge.

The Solution

The application was written using Objective C and J2 for iOS and Android platform respectively with a Yii, Node.js backend architecture and RESTFUL API's on Amazon AWS cloud infrastructure with node based elastic EC2 servers along with a load balancer bridge.

Export Bar

 

Magento 2.1 - Lansinoh

Including Durable Medical Equipment Sales

Lansinoh Laboratories, Inc. manufactures breastfeeding accessories. It offers care products and provides breast pumps. In addition, it offers storage solutions, including pumping sets, flanges, breastmilk storage bags, and breastmilk storage bottles; and feed solutions, such as bottles nipples and nipples. The company sells its products online, as well as through retail stores in the United States and internationally. The company was founded in 1984 and is based in Alexandria, Virginia.

Lansinoh Laboratories, Inc., reached out to Fair Pattern with a vision for an entirely new website. While the previous website included the core information they needed, Lansinoh needed to extend the unparalleled in-store experience to web and mobile devices. Our strategy, design, and development teams crafted a new website to better represent the premium brand they had built - from the exclusive Breast Pumps, to the first-class service provided to both consumers and healthcare professionals.

The Challenge

Lansinoh used two separate servers for their content and shopping cart. They didn't have access to the Magento servers and couldn't upgrade nor configure settings.

Lansinoh is the manufacturer and authorized dealer of the DME equipment but were not able to sell directly without going through a third party insurance lookup company.

Lansinoh needed to automate their healthcare provider authentication for their discount and giveaway program.

Lansinoh Case Study

The Solution

  • Discovery & Strategy Planning
  • User Experience Optimization
  • Interactive Web Design
  • Custom Web Development & Programming
  • Third-Party System Integration
  • Turn-Key CMS and Web Hosting
  • Fair Pattern received the raw data from their current build and updated the current version of Magento.
  • App creation seamlessly connecting insurance companies to check customer's insurance eligibility, co-pay and upgrade cost to enable Lansinoh cart experience for their DME products.
  • Automatic lookup tool to authenticate healthcare provider status via NPPES API

The technology used:

  • Created separate views for healthcare providers vs. customers
  • NPPES public data API to authenticate a viable healthcare provider based on NPI.
  • Partnered with third party healthcare payer lookup through API to determine patient's eligibility for DME product
Lansinoh Case Study
Lansinoh Case Study

 

UI - UX Project

NYC Department

NYC Department of City Planning reached out to Fair Pattern to reduce inconsistencies across three different platforms in use for their Geographic Online Address Translator.

Nyc Department

The Challenge

The Geographic Online Address Translator services are built in 3 different platforms; C#, VB NET and html, the challenge was to achieve internal consistency within accepted UI design patterns.

The Solution

Fair Pattern accomplished a responsive design for access anywhere and similarity across all platforms.

  • Design and Style guide idea on the above 3 applications and landing pages
  • HTML and .NET conversion of the approved UI designs
  • Setting up the code base and running them in our environment
  • Integration of the HTML / .NET designed pages into the application front end
  • Deliverables will be in Style Guide PDF, PSD, HTML and ASP.NET, VB.NET codes

 

Python and Machine Learning, Consulting and Delivery

Fair Pattern Customer Relationship Management

CRM

Fair Pattern's CRM is used by marketing teams to grow business faster than ever before by building stronger customer relationship over time, and exceeding expectations every step of the way. We help create Email, Content and Social Media Campaigns to nurture qualified leads and help you convert leads into sales & lasting customer relationships

Our platform helps you track, sustain and improve your sales funnel processes. We can help you excel with establishing positive and timely client relationships.

Fair Pattern's CRM
  • Contacts
  • Lead Generation
  • Email Campaign
  • Marketing Workflow
  • Email Analytics
  • Social Analytics
  • Events
  • Reports
  • Advertisement
Fair Pattern's CRM
Fair Pattern's CRM
Fair Pattern's CRM
Fair Pattern's CRM

 

AI based Chat-bot

Fair Pattern's Chat-bot is used by marketing, sales & service teams to grow business faster than ever before by building stronger customer relationship over time, and exceeding expectations every step of the way.

Our AI based chat-bot interpret the user intent, process their requests, and give prompt relevant answers. It is designed to understand and respond to a conversation in a natural, human-like manner. Once it is trained, it can pick up variations in a customer’s question/query/response and give relevant answers in a more human-like way.

Our bot helps you track, sustain and improve your sales funnel processes. We can help you excel with establishing positive and timely client relationships.

How to Use

  • User can give chat-bot an image, welcome message and can set language for chatting
  • Improve the knowledge base of our chat-bot with intents and response. Chat-bot can add or reject them based on their relevance
  • Easily create, edit, and train complex conversations in the Question/Answer mode
  • Automatically extract question-answer pairs from semi-structured contents
  • User can get analytics data of conversation length, total message and response accuracy
  • Our chat-bot auto-detects the primary language and sets the analyzer accordingly, the service supports more than 50 languages
chatbot features

How it Works

A conversational chatbot is an intelligent piece of AI-powered software that makes machines capable of understanding, processing, and responding to human language based on sophisticated deep learning and natural language understanding (NLU). Our chatbot uses natural language processing (NLP) to map user input to an intent, with the aim of classifying the message for an appropriate predefined possible response.

How To Work ChatBot

Technology

Python, Pandas, Numpy, Sklearn, Matplotlib , Tensorflow 2, NLTK, Sequential Model, NLP, Google Translator, Angular JS, Google Maps, Flask.

We used python library Pandas for data manipulation and analysis to manipulate our intents and responses. Another library NumPy is used for multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. TensorFlow is used for training and implication of deep neural networks. We used Natural Language Toolkit (NLTK) a text processing libraries for tokenization, parsing, classification, stemming, tagging and semantic reasoning on our datasets.

Methodology

Our chatbot uses natural language processing (NLP) to map user input to an intent, with the aim of classifying the message for an appropriate predefined possible response with the help of Deep Neural Network model.

Phases

  • Text Pre-processing:tokenization, lemmatization, removing stop words, vocabulary building
  • Data Encoding & Decoding: dataset encoded into numerical features for modeling
  • Data Splitting:For testing and training purpose
  • Model Architecture:Neural Network, Embedding Layer, Fully Connected Layer, Hidden Layer, Activation Function
  • Model History and Evaluation
Deep Learning

Text Pre-Processing:

  • We converted the .json file of intents and responses into Pandas dataframe
  • Then we tokenized the intents by using stemming and lemmatization methods
  • After that we removed all stop words and save the token. Words like the, in, at, that, which, and on are called stop words.
  • Our next step was to build a vocabulary, which is a set of words in a given dataset after the removal of stop words.

Data Encoding and Decoding:

Now that we have a vocabulary of words in the dataset, each of the patterns can be encoded into numerical features for modeling, using any of the common text encoding techniques—count vectorizer, term frequency-inverse document frequency (TF-IDF), hashing, etc.

Data Splitting:

With the data encoded, we can now split it into training and testing sets. The training set will be used to train the model while the testing set will be used for evaluating its performance on unseen data.

Model Architecture:

The most common approach to building a model on sequence input is to use a reinforcement learning model, for that we used the “Sequential” model class of Tensorflow. Along with that we also used embedded layer, fully connected layer, hidden layer and activation functions.

Model Evaluation:

At the end we evaluated the model on the test set and observed model performance was increased. We kept on training and tweaking the model for better performance and accuracy.

Benefits:

Benefits to Customers

  • 24-hour availability
  • Instant answers
  • Consistent answers
  • Recorded answers
  • Multi language
  • Endless patience
  • Instant transactions
  • Programmability
  • Personalization

Benefits to Companies

  • Cost savings
  • Increased sales
  • Increased customer interaction
  • Reaching new customers
  • Gaining a deeper understanding of customers

Uses of Chat-bot

Retail and e-commerce

  • Product and price notifications
  • Purchase assistance
  • Post-purchase customer service

Banking, finance, and fin-tech

  • Personal financial information
  • Loan services
  • Banking services

Travel and hospitality

  • Vacation planning
  • Reservations
  • Bookings
  • Refund or reschedule assistance
  • Ordering and delivery

Healthcare

  • Billing and insurance processes
  • Appointment bookings
  • Conversational self-service

Media and entertainment

  • On-demand content
  • Subscription management

Education

  • Student assistance
  • Announcements and updates
  • International student assistance

Installation and maintenance

  • Step-by-step instructions
  • Troubleshooting sessions

Want More Information? Download Our Brochure!Click Here