Lu'lu Naturals brand offers all-skin skin care & supplements product line. They were looking to expand their business by creating an E-commerce website which would showcase their products and branding. They approached us to develop this project.
Develop an SEO friendly E-commerce platform with a set of custom functionalities such as custom referrals, custom shipping management and a responsive design.
Customized email notification system with full integration for Amazon, Paypal with payment gateway, integration for Facebook, Twitter, Youtube and Google.
Fully customized account dashboard with various capabilities for management and overview of: orders, personal information, subscription stats, referrals, cart, wishlist, recently ordered items and order history.
The Cape Ann Solution
The first step before starting to work on any project would be investigation. Cape Ann team will identify the project scope, arrange a communication plan with the client, establish how involved the client will be during the project development and then choose the right strategy for development. Considering the number of features client requested to be finished over the six month development duration period, we chose SCRUM Agile to ensure team worked collaboratively with the client and stayed in sync. Along with standard features most webpages would have (login, account management, basic pages like Home, Contact Us, News, Faq, Help, About Us), we developed a custom dashboard, and provided client with fully custom CMS backend system packed with different functionalities like CMS for Information Page and for individual content, ability to view, edit, create and fulfill orders from admin panel; complete invoice management, shipment management, ability to create one or multiple shipments and credit memos per order to allow for split fulfillment, and reporting with ability to print invoices and packing slips. Stack used was ASP.NET 4.5, C#, MS SQL Server 2008 R2, Paypay Rest Api and Amazon payments with Bootstrap, jQuery and Knockout Js on frontend side. The initial production release was completed after 5 months, and the succeeding 2 months were spent refining the product and releasing subsequent iterations.
What Lu'lu Naturals said...Cape Ann delivered an innovative solution for our website to address an issue we had been grappling with for a long time, how to easily and effectively make available the numerous documents we hold on the site to our multiple groups of users. Job done - an excellent result!
RevTwo is a pioneer company offering support for mobile or Internet of Things applications. Their brand name is layered and comes from two words, revision - which is another word for GENERATION and two - which in this case symbolizes a new iteration of something, the NEXT version. This alludes to RevTwo providing the next generation of support in a world where Internet of Things is becoming an increasingly growing topic. IoT is affecting our lives and the way we operate on a daily basis. Revtwo combines remote access and support which scales to IoT levels.
Develop a service which would provide communication and remote access to Linux OS or container
Make SDK for adding data and custom logic for the application running on Linux
Make a web application which would serve as a workspace
The Cape Ann Solution
We used C++ to develop a complex agent service with an SDK allowing the application to collect data. We ensured communication would happen in realtime and agent was always online and ready to respond by using PubNub's realtime push/subscribe messaging API and their network currently serving over 300 million devices and streaming more than 750 billion messages per month. We used WebRTC to handle the session and data transfer, which ensures a fast peer-to-peer connection. This approach was very elegant and it secured key product strengths: reliability, high speed and responsiveness.
What RevTwo said...The team has done a tremendous job and created a product that will be a linchpin of our product line...
RevTwo is my third startup, and I have had engaged many teams from companies of all size to do work for us, Cape Ann is at the top of that list!
In 1968 a group of parents established The Wirral Society for Autistic Children, which was the forerunner for our organization today. Autism Together has grown from providing support to people with autism, to providing a wide range of living options, day services and community support to people all over the UK. Autism Together campaigns on behalf of all individuals affected by autism, they encourage all supporters to get involved with fundraising and volunteering. They offer comprehensive training and job development for all their employees.
Autism Together hired us along with another researcher abroad to develop a system which would track and comprehend all the data they record while monitoring individuals with autism. We had to investigate all the data they capture, video feeds, and data collected by third party applications.
We were tasked to develop a tool where all of this data can be presented together in a comprehensible manner and intuitive user interface. This is why the second challenge was designing a tool which would display all this data in a simple and effective manner, where user could easily navigate around the data to discern significant from insignificant or invalid data.
Personnel could then observe and obtain statistics from this data, as well as draw some higher level conclusions supported by this data. Challenge here was to make sure selecting a time slice of data would synchronize between all the recorded data and filter it to that selected time range.
The Cape Ann Solution
We have built a complete application using Angular on frontend and Python on backend side. We developed a full API to handle file upload (video and various data spreadsheets), and also integrated S3 into the app so that files can be downloaded directly from S3 bucket. We gave the client ability to create presentations of data, where they can select which data they want to display on the presentation from all available data. A post-processing filter was added to the tool to validate and differentiate between usable and non-usable data. Video data was displayed in a video player while sensory data was shown on a chart next to the video. Chart was linked to the video so that selecting a point on chart would forward video to that same point in time.
NuVasive strives to develop novel surface and structural technologies to enhance the osseointegration and biomechanical properties of surgical materials. They develop proprietary design and manufacturing methods behind Advanced Materials Science that create implants intelligently designed for fusion. As the leader in spine technology innovation, they maintain focus on transforming spine surgery to deliver clinically-proven surgical outcomes. LessRay powered by Surgical Intelligence, is an image enhancement platform designed to take low-quality, low-dose images and improve them to look like conventional full-dose images. X-ray or fluoroscopic radiation has been identified as a potential cause for a host of medical problems. LessRay offers the physician and hospital system the opportunity to use significantly reduced radiation imaging in the O.R.
Nuvasive wanted us to collaborate with them on a proof of concept project which would completely change the way their LessRay platform operates. Our first challenge was to quickly grasp the way their current system operates, and the logic behind LessRay X-ray imaging.
Instead of just enhancing low-dose images that x-ray device records, it would have an embedded neural network which would isolate specific areas on the low-dose image. Challenge was to grasp how the neural network works and how it ties together with Mask R-CNN framework, and to validate and assess the quality of Mask R-CNN resulting images.
Final challenge was to design an algorithm would then locate the same areas on a full-dose image and replace low-quality, grainy areas with appropriate high resolution areas from full-dose image using geometric transformations.
The Cape Ann Solution
Tensorflow was used to manipulate the neural network which has a state of the art Mask R-CNN framework for object segmentation built on top of it. This neural network (NN) was trained using a vast amount of images Nuvasive provided for NN calibration, and then used on fresh images to test how well the recognition algorithm works in practice. After Mask R-CNN locates and marks the areas of interest (zones) on low-dose image, these same zones would be located on the full-dose image and replaced accordingly. Due to complex and sensitive nature of project, only scalar transformations are allowed on the image. We successfully set up the network and Mask R-CNN framework running in the environment and used Python for zone extraction. Big emphasis was placed on speed of development because this was only a prototype project. This is why Python and all the sci libraries like Tensorflow, scipy, opencv, keras, and others available in it were put in use.