Unicsoft

AI driven credit risk assessment based on climate change factors

Our data science team works on integrating machine learning capabilities into the credit lifecycle and climate risk management system to substantially improve risk management for financial service providers. 

About the Client

YAPU Solutions is an innovative FinTech startup for farmers focusing on the populations most vulnerable to the climate and biodiversity crisis in developing countries. Due to various climate-changing events caused by human activity impacting weaker rural markets, YAPU recognizes the urgent need for resilient financial solutions that would help small land-holding farmers to streamline production by increasing working capital. With Unicsoft support, the company provides a digital platform for loan management and an operating system for resilience focused climate finance and increased impact reporting for financial institutions worldwide - as recognized and supported by multilateral development banks such as IDB Lab and UN IFAD or large commercial banks such as BNP Paribas.
Unicsoft has led the technological development of a YAPU designed credit lifecycle management system. Based on several years of triumphant product introduction, the client is looking to complement its data gathering capacities with AI-powered modules that further enhance risk management and improve operational efficiency for financial institutions.

Solution Highlights

YAPU’s cutting-edge system is increasing loan origination efficiency and capable of lowering credit default for financial institutions due to an increased transparency while delivering a better view on climate- and nature-related risks in credit assessment. The solution provides data insights on credit risks and cash flow projections, based on analyzing a spectrum of external factors, including climate threat exposure and sensitivity, production or asset characteristics (e.g. for housing, crops and animals) or price tendencies.

Business Goals

YAPU is powering up its existing system and analyzes the impact of applying ML to predict the probability of a borrower defaulting on his/her loan. Loans to vulnerable borrowers in developing countries are different to traditional business loans in highly formalized financial sectors - they are characterized by high levels of informality and market volatility. There are a number of different qualitative and quantitative risk factors to measure before financing higher risk loans for these populations in highly informal environments.

Project Scope. Step 1.

Unicsoft has charted a comprehensive roadmap, outlining the integration of AI functionality into the existing credit management solution from the ground up. With YAPU, we have conducted a thorough feasibility study, co-financed by the German government, defining four potential data use cases, identifying what data categories need to be gathered and processed, and designing the conceptual outlines  on algorithms for future model training.

Project Scope. Step 2.

Currently, Unicsoft works on covering the use cases 2 and 3 into a merged uses case of climate risk assessment and adaptation solutions recommendation system. First prototypes have been developed, while YAPU is aligning its data gathering routines and processes for existing and new clients across the entire platform. This development was and is following the established ML model development process:

• defining business requirements
• analyzing available data
• collecting & preparing data
• feature modelling
• model’s performance evaluation & optimization.

Expected Outcomes

Future ML modules will be seamlessly integrated into the existing YAPU system and resolve the challenges mentioned above. These modules are projected to enhance the credit risk assessment system, optimize credit scoring & management processes, and make the system more efficient to use. 

Mobile hyper-personalization empowered by AI technology on the edge device

Our data science experts contributed to the development of a Mobile engagement platform that applies AI algorithms to provide real-world user insights, advanced customer engagement and personalization.

The Customer

Our client is a fast-growing product company with offices in Tel Aviv and New York. The company pioneered on-device artificial intelligence solutions that help businesses provide hyper-personalized customer experience and leverage insightful user data for better decision-making.

Challenges

We started our long-term cooperation 7 years ago on a small project. But the proficiency of our Artificial Intelligence engineers and data scientists proved our client to take the partnership a step further by involving Unicsoft in the company's core product development with a strict demand of:

• A full-fledged approach to an artificial intelligence project covering all stages: data preparation, extracting business value from data, data clustering, feature modeling and evaluation, model optimization.
• High quality of artificial development services, together with transparent processes and effective team communication
• High-level engineering mindset to make reasoned decisions on matching business needs with applied technologies
• Solid project documentation for the product to be integrated by other development teams
• “Ninja” engineers who possess knowledge of over 40 programming languages and technologies.

With a high demand for AI solutions on the market, it is often difficult for companies to instantly find an AI solution development partner that can meet their needs and not just speculate on the latest trends.

Solution

For years, our cooperation with the customer has been based around different AI products and proofs of concept (PoCs), all of which constituted the client's core product - Mobile Engagement platform that pioneers on-device artificial intelligence (Edge-AI).

• Mobile Engagement Platform

The product turns mobile phones into personal data engines, all while carefully protecting each user’s privacy. It’s an on-handset artificial intelligence engine that collects and uses data from a handheld device’s sensors to build an on-device user profile.

By gathering and analyzing user behavior collected by the smartphone’s built-in sensors, the technology understands the user’s location, activity and more, enabling prediction of future movements, and offering up contextual services that improve the user experience. The platform allows to engage with specific segments of customers in the moments that are the most appropriate for them to complete transaction or make some action.

• Mobile SDK

A key element of the developed platform is a Mobile Engagement SDK (software development toolkit), that integrates easily with any iOS or Android app. It applies artificial intelligence to analyze first party data from one’s mobile, build a person's profile and predict his future actions.

SDK helps not only to create personalized and well-targeted engagement campaigns, but also trigger them in the most relevant moments.

The solution is based upon Edge AI technology that is capable of turning 87 device signals into hundreds of first party insights. Edge AI provides personalized customer experience without infringing on users’ privacy or breaking regulations on safe data practices. With Edge AI, all personal data stays on the device.

Our Expertise

• Machine learning
• Deep learning
• Natural language processing
• Big data
• AWS
• SalesForce consulting

The Result

• We have made a big effort to contribute to the company's core product - a revolutionary solution that changes the paradigm of mobile engagement, from a model that creates tension between personalization and privacy to one that maximizes both.
• One of the main elements that led to the mutual success of our cooperation was the fact that we treated each other as partners that hear and trust each other.
• The best reward for our work is our ongoing cooperation. We keep receiving new requests for our expertise and deliver results on a regular basis.

About the Project

Bitcoin has garnered significant attention, but its market value is often volatile. However, under the watchful eye of professionals, any risk can be turned into an opportunity. Our client, Ternary Intelligence, has done just that: Drawing on their Ph.D. degrees and passion for computational models, the company’s founders built a mathematical algorithm to detect cryptocurrency market instabilities and reduce investment risks.

Our client sought a trusted software development vendor to create a cryptocurrency investment solution integrated with an affiliate platform and accompanying iOS and Android applications. The key challenge was to develop these products within tight deadlines while maintaining high quality.

Challenges

Proficiency in AI & blockchain technology, software engineering, security, and UX design was essential to address the following challenges:

• The marketing campaign supporting the platform had already been scheduled, and the platform was supposed to be launched within a month.
• The platform had to operate seamlessly across multiple devices and operating systems.
• All solutions had to include reliable security and privacy features to prevent data breaches.
• It was necessary to fix several discrepancies with affiliate platform integration.

Solution

Unicsoft played a pivotal role in developing a leading-edge solution that perfectly aligns with the client's vision of a Bitcoin hedging strategy. Harnessing advanced analytics and automation, our primary focus was to mitigate potential risks tied to Bitcoin investments. We aimed to provide the client with a user-friendly digital platform, empowering end-users to navigate the volatile cryptocurrency market with confidence and minimal risk exposure.

We achieved the project goals entirely and delivered a solution encompassing the following capabilities:

• Instability Detection: An instability detection technology anticipates major market corrections via investor herding pattern analysis.
• Dynamic Portfolio: Our algorithm adapts Bitcoin exposure dynamically based on market analysis, optimizing growth potential and minimizing drawdowns.
• Preserved Confidence: The platform's investment strategy offers reassurance across various cryptocurrency market conditions – from bearish phases and bubble cycles to corrections and risk reduction initiatives.

Results

Overall, we successfully completed the project on time, upholding quality standards, and reached the following results:

• Filled the gap in the virtual currency investment space by limiting downside risk while capturing long-term market growth.
• Safer investment space thanks to the incorporated risk-adjusted characteristics.
• Benefits for virtual currency investors thanks to dynamic hedging that solves the entry timing problem.
• Opting for React Native app development reduced costs by 30%, enabling budget savings and efficient post-launch support for the client.
• The users can safely relocate funds they didn’t invest in cryptocurrencies directly due to market volatility in the crypto trading market.

About the project

The European startup sought to develop an AI solution for early diagnosis of skin disorders, utilizing a cloud-based, computer-aided service. In order to attract major healthcare players and promote the use of AI in healthcare products, the company aimed to build a prototype of an AI application core specifically designed for early diagnosis of the base types of skin disorders.

Business Challenges

The main objective of this project was to build a prototype of an AI application, with the goal of improving patient outcomes within the diagnostics process and enhancing the potential of AI technology in healthcare.

• Analyzing complex skin disorders that vary greatly in symptoms, severity, and underlying causes.
• Providing value to both patients and specialists with the AI solution, including allowing patients to make decisions in time to get consultations from doctors, and enabling specialists to receive primary diagnostics and recommendations from the AI system.
• Ensuring compliance with medical industry regulations and standards.
• Creating a cost-effective solution that saves time and money for both the client and end-users.
• Demonstrating the capabilities of the solution to specialists in AI, healthcare, and potential investors.

Technical Challenges

The Unicsoft team faced several technical challenges while developing the prototype for skin disorder diagnostic solution, including:

• Selecting the optimal algorithm to analyze four types of skin diseases, with the potential for scaling to more types in future iterations.
• Collecting free medical images from open source Dermnet.com, a skin diseases library, and processing the images to comply with the model's needs, including separating the skin and other elements in the photo, highlighting areas with the highest density of problem areas, and scaling to improve the quality of image recognition.
• Deploying the prototype in Jupyter notebook, a web-based interactive development environment for creating, sharing, and collaborating on code, and providing all documentation and prototype materials to the client.
• Ensuring the solution works in real-time for individual users, with the processes of image recognition and classification model constantly recomputed by an AI Engine.

The Solution

Unicsoft's R&D team was in charge of developing a prototype for the Skin Diagnostic Model based on Computer Vision algorithms, which required advanced knowledge and expertise in AI and machine learning.

• Conducted thorough research and analysis to select the most optimal solution to diagnose early skin problems within the prototype.
• Created documentation that estimates the resources and time required to launch this solution into production.

The Results

The team created a fully functional prototype of the AI solution, which was delivered to the customer on time and according to the initial requirements.

Built a solution that could handle and process thousands of images with disease examples within seconds, and provide primary diagnostics and recommendations to healthcare specialists.

Ensured the accuracy and effectiveness of the machine learning model for providing personalized and targeted predictions on skin disorders.

Technology & Tools

• CNN
• Python
• Jupyter
• ML
• Keras
• AI
• OpenCV
• Computer Vision

With dozens of successful product launches but no digital apps, the Latin American healthcare company turned to Unicsoft to create an unprecedented app that combines AI and product management functionality.

The company wanted to develop an application that would radically change the way merchandisers work by delivering a higher quality of pharmaceutical retail data for better decision making.

Business challenges to solve

Panama has only recently begun to implement high-tech business solutions into the operations of its major industries. Most of the data that pharmaceutical managers and retail chain owners receive on the status of products in their brick and mortars is still  collected manually by merchandisers.

Merchandisers visit dozens of retail outlets to track:

• Specific product inventories and shelf quantities
• Product placements
• Brand exposure at particular retail chains

Merchandisers have to spend time manually counting goods, analyzing their demand, referencing their in-store presence with shelf placing model guidelines, and so on. These processes place a high demand on employee resources and cause a number of bottlenecks in the operational processes of an entire business:

• Inventory gaps
• Inability to monitor inventory rotation in each store
• Inability to know about inventory decay in advance
• Low productivity of sales representatives visiting the stores

The solution

To address these shortcomings, the client wanted to arm pharmaceutical merchandisers with a mobile application that could:

• Analyze and count the number of products and brands on shelves using cameras on mobile devices.
• Group the collected information by brand and specific product, then display it to the manager on a separate dashboard for analysis.

The biggest challenge was to implement a machine learning system. This system had to recognize products and then analyze the pictures taken by merchandisers in real-time. It would then identify brands, titles, and accurately count the number of products on store shelves.

A highly-skilled team of Unicsoft data scientists and engineers followed the sophisticated process to create an ML model for the product:

• Created a dataset of images
• Annotated training dataset
• Trained computer vision model for object detection
• Benchmarked model accuracy
• Finetuned and optimized model after going live

Development process

Unicsoft started development from the discovery phase, which lasted about four months. The main goal of the discovery was to define a straightforward app workflow, features, and interdependencies. Within the MVP scope, Unicsoft trained the unique ML model to recognize the titles of drugs and medical products using a test dataset of five products.

Unicsoft’s UX designers were proposing design outlines that prompted the client to immediately adjust their expectations. It took multiple iterations to create a design with usability and features that satisfied the client.

The quality of the team's communication, adherence to the predefined plan and estimates, and sticking to the project’s budget was crucial for our client. He appreciated that we provided consistent communication and a well-organized development process.

How system works

1. The Admin sets up a company in the app. Each company can have multiple products in its portfolio. An Admin can add those products and descriptions manually or by importing data from a CSV or CRM. 
2. The Admin invites users under the Manager role. These users can set product KPIs and access product analytics. 
3. The app then generates an unlimited number of QR codes. Each is assigned to a specific shelf with a particular address in the store. Merchandisers can scan those codes from the shelves. That's how the app identifies the locations of products it analyzes. 
4. The merchandiser is equipped with a mobile app that can scan a QR code, take a photo of a shelf, and then automatically analyze the number of products located on the shelf along with their brands.
5. All information is instantly available for the manager, who can track any product inventory on the shelf and confirm its location.
6. Using the app’s dashboard that shows inventory for dozens of stores, the manager can quickly ship any number of goods to the needed store locations based on the data displayed in the app.

The result

• Team of 9 people released the PoC on time, providing the client with all the basic functionality they had initially requested.
• The system generates results with 90% model accuracy, perfectly acceptable for the client.
• The project grew from an idea to a functional mobile and web application that allows the client to test their business hypotheses.
• The ML model is ready to be tested and proven in the field, and together with the primary application, scale for any volume of goods, retail outlets, and large number of users.

The Customer

Our client is a product company developing on-device artificial intelligence (Edge-AI) solutions for various industries: advertising, banking, automotive, retail, and insurance. Their clients are world-famous companies in these areas. The company consists of progressive team members who are in love with the product and are eager to find the best ways possible to develop and enhance it.

Business Challenges

From the first days of working together, it became clear that in order to make their product successful on the market they needed a highly communicative and stable team of developers, data scientists, and cloud specialists.

That’s why we made it our top priority to select and retain the best candidates to build a strong and highly motivated team.

In fact, the main challenges for us when building this team were:

• Complicated and lengthy selection process comprising test tasks and trial period. Unicsoft has completed a thorough selection & validation of a great number of potential candidates, and then only 1 in 15 engineers were able to complete the test task.
• Difficulties in finding candidates, not only with proficient hard skills, but also with highly-developed soft skills.
• Much attention was paid to the candidates with relevant engineering or mathematics education and high quality of their code. The code should be written quite “cleanly”, without extra lines and signs, and should comply with the code style.
• The requirements for the specialists were quite complex. For example, we had to find a specialist who would have been a full stack developer with knowledge of about forty different technologies - we called it “ninja in IT”.

The client’s key focus was not just to find skilled employees, but specialists able to develop a comprehensive approach to solving customer problems. Our client hired a number of different people having distinctive, but at the same time efficient approaches to problem solving.

The Process of Cooperation

For May 2022, we formed several teams for the client:

The Data Science team was the team formed by the client with the help of the Data Science challenge workshop. We found various data scientists to interview and then organized a workshop for them. During this workshop, the client introduced them to a problem to solve and looked at the different approaches they proposed to solve it. In the end, the client chose a few people to join the team. Now, a Data Scientist and a Data Analyst/Business Intelligence engineer joined the core team.

The Mobile team consists of 4 Android developers and 3 iOS developers.

The product team consists of two designers who work in tandem with a product manager to prepare product designs for developers and create marketing materials for sales objectives.

The CTO team consists of engineers who are engaged in validating hypotheses through quickly building apps and PoCs.

The Cloud team is the main team that works on the client’s Mobile Engagement Platform. It consists of a DevOps/Solution Architect, three Front-end developers, two Back-end developers, and QA. This product is being developed 100% by the Unicsoft team.

We believe that a stable and knowledgeable team is one of the main success factors of the project. Selecting highly-skilled and motivated employees for the client was a consistent and well-deliberated process. And we are more than happy that the result met our client’s expectations.

The result

On top of our long-term cooperation, 18 of our engineers have been working on the client’s product. 15 of them are members of the core team.

The client treats the remote team as “their own team” and C-levels, especially those responsible for product management, have been actively communicating with our team on the common product vision.

We provided an efficient communication process, openly discussing the expectations, risks, and giving and receiving feedback from each other.

Thanks to mutual transparency and scrupulousness, we were able to develop a strong and productive team of professionals, while mitigating risks.

French startup helps customers avoid scams while delivering a seamless online shopping experience.

Business context

Today, e-commerce is grappling with an influx of scammers posing as online stores. While the popularity of online shopping has skyrocketed due to the COVID-19 pandemic, fraudsters disguised as e-stores are collecting more and more credit card information from unsuspecting customers.

Solution

The extension helps buyers detect e-commerce scams and locate higher quality goods at a more competitive price point

The extension works in the background and is able to recognize when a user finds a product on an e-commerce site or page of an online store. Next, the extension analyzes the website's reputation using unique algorithms and determines a user trust ranking. As a result, the user sees the ranking marked with one of three colors: red, orange, or green.

Our challenges

Tightest deadlines. The product working at full capacity was set to hit production before Black Friday (26th of November) 2020. The client initially reached out to Unicsoft in August 2020.

The client didn't have a significant technical base. Since the customer had only a business background, Unicsoft managed all the prototyping, decomposition, and development work. The client chose Unicsoft because of their confidence in Unicsoft’s ability to provide the necessary engineering, analytical resources, and sufficient expertise.

Critical performance requirements coupled with sophisticated data analysis algorithms. End users shouldn’t have to wait long for the results of the web page analysis. The extension had to generate results in seconds. But achieving the required speed was not a trivial task for all the complexity and variety of calculations carried out under the hood.

Multiple proofs of concept were required. Since the customers had a very broad view of the possible functionality, the team had to develop and quickly test several feature concepts. These included AI-powered image recognition (for analyzing product images and matching them with other e-commerce platforms) and advanced mobile traffic analytics.

Project stages

During three months of development, the product passed the following stages:

• Discovery
• Development
• Testing
• User acceptance testing
• Release

Since Unicsoft started with a high-level product idea, the team had to work on both the development and business levels.

To develop the concept for future solution, business analysts researched all the possible related solutions and competition, and worked on defining potential customer portraits. The project manager set the tasks, and the core team deconstructed them for development.

Technology Stack

• PostgreSQL
• Docker
• Angular
• Redis
• MongoDB
• Kubernetes
• node.js
• Google cloud

The result

After six months in production, the most significant result was the successful and timely release of the extension:

• completed in a relatively short amount of time
• was to be presented to journalists and a wide range of users
• with full responsibility for maintaining the customer's reputation through presenting high-quality working product

Unicsoft utilized artificial intelligence to build Wellskin solution for accurate skin cancer diagnosis.

Challenges

Streamlining the process of melanoma diagnosis for improved efficiency

Ensuring accurate and reliable detection of skin cancer using AI technology

Establishing seamless communication between the app and clinics for analysis

Developing a user-friendly interface for easy image capture and comparison

The solution:

• iPhone/iPod application capturing detailed skin images for analysis
• Database storage enabling easy tracking and analysis of skin dynamics over time
• Integration of advanced image analysis algorithms for precise diagnosis
• Seamless transmission of patient's skin images to clinics for analysis

The Result

Enhanced efficiency in melanoma diagnosis through automated image analysis

Improved tracking and analysis of skin dynamics for effective monitoring

Streamlined communication between patients and clinics for prompt diagnosis

User-friendly interface enabling easy image capture and comparison for patients and healthcare professionals

Technology & Tools

Android

Android SDK

iOS SDK

Objective-C

iOS

Mobile development

Unicsoft's expert team collaborated to build an AI-integrated cloud-based platform - predictive remote patient monitoring software that connects doctors, clinicians, patients, and caregivers.

Challenges

Ensuring patient safety by predicting and preventing adverse events

Integrating AI technology into existing healthcare systems

Gathering and analyzing patient data for accurate predictions

The solution

• Unicsoft's team of tech experts joining Wanda's ML Engineers and Data Scientists
• Our Senior Python/Django developer improving the back-end structure
• Integration of newly trained models into the platform
• Effective communication options (video, IVR, text messages, app) facilitated by Unicsoft to integrate into the platform
• Utilization of machine learning algorithms, prediction, and risk analytics

A system that analyzes blockchain data repositories and turns inert data into workable and valuable financial assets

Challenge: The inefficient process of collecting and processing financial data hampers crypto provider’s decision-making

The client required numerous reports and analytics to process market data like cryptocurrency rates and interest rates showing fluctuations in cryptocurrencies rates. Armed with this analytical data, the client would get actionable insights to allow more rational decision making.

Data sourced from blockchain is highly technical and detailed, which makes gathering it manually a daunting task, prone to data loss. The Maker Foundation needed to set up an ETL (Extract, Transform, and Load) pipeline to gather data and keep sourced data records, but since they didn’t have enough data science expertise in-house, the client turned to Unicsoft.

Results

Complete and relevant DeFi analytics. We helped our client develop an automated process for gathering data from blockchain through an ETL pipeline—a solution that helped extract data from different sources, transformed it into the required format, and loaded it into the client’s database. Today, the entire process has become automated, requiring minimal support from the client’s side. They can now get complete and relevant DeFi analytics faster than ever before.

AlphaWallet is an Ethereum based application tailored to help individuals manage their tokenized assets: fungible and non-fungible tokens (NFTs). It is the most popular fully open sourced Ethereum wallet on iOS and Android platforms with more than 600 forks used by enterprises and small projects alike. For businesses, AlphaWallet offers a white-label solution that can be easily customized by them and would support different tokens (including ones that do not follow ERC standard)on demand:

1. AlphaWallet enables peer to peer network, where users could easily execute token transactions among each other.

2. AlphaWallet supports the TokenScript standard, which helps to create customized and intuitive interfaces for various tokens.

3. The wallet supports a Web 3.0 browser, that empowers users to access different DApps (e.g.: for buying or exchanging tokens) through AphaWallet interface.

4. It helps users to manage their tokenized assets in a simple way: with your car ownership token in the wallet, you can control, sell, transfer, collateralise or share it.

5. The wallet helps users manage their NFTs effectively: Users can see their NFT across multiple chaines, with images in the wallet and load NFT assets directly from marketplaces.

6. AlphaWallet supports all public and private EVM (Ethereum Virtual Machine) networks, thus enabling a variety of tokens in the wallet.

Results of our cooperation with AlphaWallet

Unicsoft provided high level engineering talents that were able to pick up the nuances of the project and adapt to the customer’s processes very quickly. We were always ready to staff candidates of rare stack or unique skills, as well as those who were capable of going the extra mile to drive the project's success.

Unicsoft team always met customer’s requirements and committed to the results. Our account management team was very helpful in facilitating communication and ensuring the right support throughout the engagement.

Unicsoft delivered comprehensive business analysis and consulting on building a blockchain-powered crowdfunding platform that embeds security tokens for digitizing assets and profit sharing to guarantee the most trustworthy and secure fundraising.

Business context

The customer reached out to us with the idea of creating a platform for crypto fundraising management (intentionally via Initial Coin Offering), where startups looking for fundraising can easily and seamlessly create, manage and trade their crypto assets. Our task was to guide the customer through the entire process of turning their idea into a well-thought, functional, regulations-friendly, secure, and successful software product: from value proposition to detailed tech requirements.

Results

A groundbreaking product concept of the crowdfunding platform, providing security tokens to distribute shares of startups and tokenize securities of investors.

Sophisticated platform tokenomics, underlining a system product roadmap to jump-start the software development process as the following stage.

Planned MVP development to validate product ideas at low risk and cost with a user base of early adopters.

Blockchain solution enabling gold supply chain transparency and efficiency

This custom web & mobile application integrates with the Ethereum private blockchain network for managing gold bar production and delivery from end to end, controlling their quality and origin, and providing a complete history of evidence for the end client.

Gold mining industry is subject to multiple environmental and labor regulations from the World Gold Council and continuous financial investigations. The solution was created for compliance purposes as a secure tool for verifying that gold is mined without violating human rights or safety regulations.

What’s more, the system has become a trustworthy source of consolidated data detailing the movements of every gold transaction for all supply chain participants, including any violations. The system verifies that gold mines have attained all the necessary certifications and comply with every regulation: CRAFT code, Market Entry Standard, Fairmined Standard, etc.

Results

As a result of Unicsoft’s collaboration, our client now operates a reliable tracking system that allows for a deeper level of control over the gold’s origin including the entire chain of events, from the mine to the end customer.

From now on, the gold mining industry will leverage supply chain transparency via the tracking of each piece of gold electronically and consolidating data from different supply chain members in a highly secure manner.

Distributed system for food product exchanges and commercialization. 

It is a sophisticated online marketplace that connects rice exporters, importers, and inspection companies in one place to trade and commercialize rice in a more effective, secure, transparent, and effective manner.

Pioneering Hyperledger

The most suitable and promising technology for the case was Hyperledger. However, it has been a relatively new technology so we needed to be prepared for unexpected issues as pioneers.

Azure VS Hyperledger

The cloud infrastructure was required to be built using Azure cloud. We needed to deal with the lack of smooth native integration between Hyperledger and Azure.

Trust & Transparency VS Security

We needed to combine the transparency offered by DLT and the need in security to prevent fraud and misuse of the platform.

Results

As a result of our collaboration, our client received a solid, secure blockchain-based trading platform that helps reach their main project goal: to provide a global rice trading marketplace that is transparent, secure, and flexible, satisfying the worldwide demand for rice while building confidence for all market participants.

- We significantly increased the speed of interoperations between parties through effective digital transformation.

- We decreased documents losing to zero. It takes just a few minutes to add new documents that are stored in unified digital storage.

- By leveraging blockchain, the platform creates a clear audit trail for every operation and market player, disables any data manipulation, and builds trust as a result.

- We increased the turnover of the rice trading marketplace by making it more transparent with blockchain technology: more than 400 early adopter companies from 60+ countries have signed up and are planning to trade more than 7 million tons of rice annually.

Unicsoft’s blockchain-based track and trace system has been successfully implemented in a group of small and medium-sized enterprises and has proved its performance capability within the supply chain management process of goods.

We created a personal cabinet for manufacturers allowing code generation, accounting and analytics, blockchain-based data storage with highly secured encryption of datamatrix codes, mobile apps for manufacturers and customers enabling them to check the authenticity of the goods and report counterfeits, and advanced engineering infrastructure. Our solutions have clean API to integrate with any third-party modules.

• Counterfeit prediction. System provides the manufacturers with real-time information about the movement of goods through the logistics chain to predict counterfeit and grey import attempts.
• IP rights secured. Solution makes counterfeiting and grey market import technologically impossible and protects the IP rights of lawful manufacturers/importers through a unique digital passport.
• Goods authenticity guaranteed. Our solution guarantees the quality and authenticity of the goods to the end-customer, due to controlling the warranty, shelf life, and service life of every product, thus positively affecting manufacturers’ brands on the market.
• Product lifetime analytics. Unicsoft’s system offers advanced business analytics and reporting based on the data collected at all points from a product life cycle: code stamped during production, product scanned during transportation, goods stored in the retail store, goods at the POS-terminal during purchase, and product unpacked at home.
• Increased customer loyalty. Solution enables direct customer feedback, personalized loyalty programs, and individual discounts, improving the manufacturer’s PR and marketing strategies in the market.
• Cost-effective application. AWS gave us an option for very slight configuration of each part of the infrastructure, so we could easily change anything we needed to achieve business goals and thus reduce development costs.
• High reliability and scalability of the system. We used AWS to ensure consistency, security, reliability, convenience, and automatic scalability of the track & trace solution.
  Efficient development lifecycle. Infrastructure as Code approach in combination with CI/CD provided us with the possibility to maintain the existing architecture easily and implement new features quickly.
• AWS facilitated cost reduction & streamlined the development process. When we started using AWS, our new features which entailed changes in architecture were implemented more than twice as fast as our previous solution. AWS usage helped us to decrease our spend by 30% by moving part of our workers to Lambda Functions. AWS Auto Scaling groups helped us avoid cases when some parts of application crashed due to high load.

In order for users to take advantage of the Client’s stablecoin, Unicsoft has also developed an exchange platform with a complex and secure KYC procedure, providing the ability to audit all actions. Based on OpenShift, this platform consists of two types of web interfaces  — one for end customers and one for the Client’s back office. Users can order the Client’s coins by transferring BTC or ETH, request the Client’s coins to be delivered in fiat to their bank accounts, view the history of orders, and see the status of execution. Here’s what the daily process looks like: 

Meanwhile, the functionality of the back-office interface is far more complicated, allowing the Client’s employees of different levels to process orders, choose the correct way of trading, set commissions for orders, mint and burn coins, and more.

• A user makes the order.  

• The Client’s company transfers this order to the counterparty.

• The counterparty delivers the requested amount of CHF to the Client’s operational company accounts. 

• Meanwhile, the functionality of the back-office interface is far more complicated, allowing the Client’s employees of different levels to process orders, choose the correct way of trading, set commissions for orders, mint and burn coins, and more.

Apart from being a trusted talents provider for our client AlphaWallet, we act as their technology partner assisting anyone who wishes with customizing and white-labeling the wallet which Alpha Wallet kindly provides as an open-source solution.

• Simplest solution to tokenize business rights.

• First Ethereum wallet that uses Secure Enclave.

• Enables token functions that are portable across wallets and websites.

• Cryptocurrency that is immune to market and counterparty risks

• ERC 20 compliant utility token providing easy transfer of ownership

• Tokenization of digital assets enabling new business opportunities

Business domains: Finance, e-Commerce

Expertise: Blockchain, Smartcontracts, Tokens

Technology and tools: Back-end: Silex, mySQl, Redis (API websockets); Front-end: Angular 2 Blockchain: Parity + Node.js

European fast developing company specialized in online retail of goods for children reached out to us with a request to create a factoring platform for their supply chain. Since classic factoring business is prone to high operations costs and slow factoring requests processing, the “classic” was not aligned with company’s agile and innovative culture. The solution was to apply Blockchain, which offered an automated low-overhead way of securing the transactions and enabling the use of smart-contracts technology. Main intention lay in gaining a competitive advantage due to development of a solution with use of a top-notch technology leading to the highest security and reliability.

As a result of business analysis project team came up with the high level technical solution which included combination of Ethereum and Parity allowing getting benefits of both modern and lightweight Proof-of-Authority mechanism and SmartContracts.

Back-end of the tool had two main nodes – first one was kind of admin-panel, and the second one was public. Admin-node was intended to provide a synchronization of all invoices that were stored in the tokens using internal API. Internal API, in its turn, reached to the Smart-Contract module enabling use of abovementioned technology. Public node let users get any requested information regarding the tokens. Initial infrastructure was deployed using AWS because of reliability and resilience of that service. The development team consisted of one front-end developer, one back-end developer, and one designer. Overall timeframe amounted to eight months.

Business domains: Trading

Expertise: Blockchain, smart property & smart contracts

Technology and tools: Angular.JS, Python, Django, Blockchain API library 1.3.3 & 1.4.0, MLlib, pandas. Docker

The goal of the project was to create a supply chain transparency platform capable of tracking each piece of gold electronically and in a secure manner. At the time there already were similar successful cases in other industries, for example, jewellery and medical implant tracking, which were Blockchain based solutions, which gave us insight regarding the technical solution in this case.

The basic technical complexity was that existing platforms track the origin of one product, for example, an implant. With gold, it is more difficult – the mine produces large ingots of unrefined gold, further refineries clean it and one unrefined gold ingot produces several bars of a smaller size of high-grade gold, then other institutions can melt these slivers into smaller ones, so the original “product” is crushed into components and it is necessary to track the way of each component back to that big ingot from which it went. Thus, the ready solutions were inapplicable in this case.

The problem was solved by applying the QR code to each ingot and to the derivatives of ingots and creating a custom system for registering all operations of the type in blockage (ingot A was turned into ingots B, C, D by X LTD, ingot A itself no longer exists) and all displacements (ingot B is moved from companies X to company Y with the help of transport company Z).

The system is realized with the help of Ethereum, and the logic of operations with ingots is programmed in the form of smart contracts.

The MVP was approved by the National Mining Association and (most likely) will receive investments from this organization.

Challenge

Modern global supply chains are becoming vast and increasingly dynamic ecosystems. Supply chain optimization becomes the goal for almost all companies to gain competitive advantages on the market.

The client is a leading provider of supply chain consulting, software and fourth-party logistics services. Aiming to bring more business value to their clients, they strive to optimize logistics execution, using Machine Learning for automation of exception prediction and data processing from different suppliers and thus facilitating the process of decision making. There is a strategic R&D department inside the client’s company, which works with Unicsoft on improving their current solution by making it more efficient, accurate and sophisticated in terms of predicting errors in logistics.

Our clients’ R&D department works on the Proof of Concepts. And Unicsoft helps to transform PoCs with the potential of developing into a solution as a feature of the existing product. It helps to improve the product via optimizing of logistics operations.

Solution

As the time to market was of the highest priority we have established very tight customer-Unicsoft cooperation with bi-weekly sync-ups and direct communication between technical teams to complete the project on agreed delivery date.

We have deeply reworked the Machine Learning model for logistics incidents prediction and supplier-related incidents. The tool takes the input from the past for training and then trained model is used to predict exceptions next week. It focuses on forecasting a group of exceptions. The tool predicts if the consignor provides a higher or lower volume than advised or the consignor fails to ship at all. The tool is used every Thursday and does a prediction for the upcoming week. It was programmed in R.

Result and Future Plans

Unicsoft team considerably improved the machine learning model as well as other components of the solution and as a result, the client received:

• Higher precision for the forecasts (80%).
• The user interface that was recreated from scratch to make the tool easier to use with a modern interface.
• Opportunity to generate reports in a number of formats and with needed periodicity.

Being satisfied with the project success the client asked us to complement the solution with analysis and prediction of the carrier-related incidents (last mile delivery) in the nearest future. Also, Unicsoft team will be responsible for the research and development of the product’s other concepts regarding the precision for the supply chain forecasts.

Business domains: Finance, e-Commerce

Expertise: Cryptocurrency, Blockchain, Finance-related tools

Technology and tools: Go, Angular 2, Blockchain

This request constituted of creating a cryptocurrency and embrace it in environment containing related service. The Client was a Chinese company that was striving to launch its own cryptocurrency for economic purposes and to increase its investment-oriented attractiveness, and, last but not least, – to successfully complete pre-ICO and ICO stages.

The Client’s roadmap contained the development of the coin itself, cryptowallet, and also featured bespoke framework for Consensus and Opinion over p2p networks.

Development team was distributed and consisted of ten software engineers located all around the globe. Go was picked as the main language, so all of the tools and environmental components were written using its native tools. It’s worth mentioning that one of the peculiar parts of project was an implementation of Merkle tree. Main concept of the tree might be described as a tree where, every leaf node is labelled with the hash of a data block and every non-leaf node is labelled with the cryptographic hash of the labels of its child nodes. This approach enhanced security and reliability level of the entire transactions system.

After successfully getting through ICO stage the cryptocurrency was launched into market in September, 2017. Project is currently under ongoing development and is being geared up with new features. The Client remarked high level of Unicsoft technical capabilities and versatility of management approach.

Big Data & AI technologies mitigate data security risks & improve customer experience in data management services.

Challenge: How to discern insights & detect anomalies in a sea of corporate data

Unicsoft customer is a leading Office 365 email migration and cloud management provider in the UK.

Operating in highly competitive enterprise environments, the organization needed a fail-safe method to detect anomalies in massive amounts of data. They needed to be able to detect and alert their clients about data breaches, fraud, or other suspicious activity before it became a massive problem.

In the cloud data security space, one undetected breach can change a client’s business forever, so the stakes could not have been higher for this enterprise service provider.

Solution: AI-based end-to-end intelligence system preventing data security threats

Unicsoft developed a comprehensive intelligence solution using AI & ML algorithms for the email data ecosystem to empower an organization with the following data management capacities:

• detecting and analyzing threats to the email ecosystems much faster, alerting customers of any potential issues;
• having the early-detection capability necessary to identify fraud, data breaches, unauthorized users, and security flaws;
• segmenting customers based on behavior and pattern matching and building customized marketing services based on this data.

Unicsoft’s solution is a custom intelligence model, integrated seamlessly with the organization’s email services. Our team built a Python-based script to automate data collection and anomaly detection for the organization, combining it with a sophisticated machine learning algorithm.

For ease of visualization, the developers decided to deliver the anomaly and novelty detection system as a Jupyter Notebook (an easily shareable open-source web application), enabling the Python code to be easily analyzed and iterated upon for future prototypes. The engineering team utilized Kibana Elastic Search to process data on the fly, with no intermediate data storage required, allowing for much greater flexibility and agility on behalf of the client organization. The custom-built Neural Network then ingested the data, using machine learning to categorize information and determine which anomalies truly required immediate attention.

Results: Reducing spending & increasing customer satisfaction 

Higher customer retention rate: In early testing of the prototype, the organization has already witnessed a reduction in SLA violations and an increase in retention rate.

Reduced costs on legacy systems: Due to the easily customizable Python-based algorithm, integration of the Unicsoft intelligence solution allows the organization to reduce spending on costly legacy systems in the long run.

Avoided downtime costs: Thanks to security threat detection, the organization alerts customers of any potential issues early enough to avoid costly downtime.

Prevented data breaches: Our customer is able to identify frauds and data security flaws before they become a problem for the enterprise.

Increased customer satisfaction: The organization is able to build customized marketing services based on their behavior segmentation and pattern matching.

Business domains: Media, Social

Expertise: Behavioral analytics, Predictive analytics

Technology and tools: Python, SQL & noSQl, R, mlLib, Spark

One of the most renowned EU&USA publishers reached out to us with an intention to build a new value-added service for their subscribers focused on prediction of a bill-voting outcome in US congress. Such service was offering an innovative, impartial and accurate approach to political analytics, as opposite to classic “expert” approach used by other players on the publishing market.

The journey began with deep analysis of the problem and available data to solve it. The main challenge was that in order to get high accuracy the machine learning model had to gather attributes of a bill and voting habits of senators from multiple non-congruent sources. Added complexity was the fact that both “bill” and “voter” are complex entities and specific voter decision regarding specific bill depends on multiple attributes and their combinations, therefore requiring advanced approaches for prediction.

Long story short – the model had an accuracy of 84% proving the business case. Such successful proof of concept initiated series of new initiatives and value-added services based on the data existing in the organization and utilizing the power of data science and machine learning to gain unique insights from it. This solution became a capstone for a versatile solution allowing users splice&merge datasets and predicting not only bill-voting outcome but a precursors leading to the decision of even factors, affecting behavioral pattern of a particular congress member.

AI model for analyzing the cryptocurrency market

Challenge

Crypto industry reached the peak of its popularity last year. There was a need to create useful tools for crypto analysis.

The client came to us with the startup idea to develop an AI model for analyzing the cryptocurrency market. As he has already built an algorithm for cryptocurrency auto-trading, he decided to add cryptocurrency price prediction feature to it as well.

The solution combines natural language processing, sentiment analysis, and machine learning algorithms (LSTM) to predict prices for cryptocurrency. It crunches huge amounts of data and tells you which cryptocurrencies to invest in and attempts to maximize your returns.

The product brings value to:

• Private traders who want to receive hints from the system of artificial intelligence and a forecast on the trend of cryptocurrency prices;
• Professional companies that need to evaluate a comprehensive forecast for the whole real-time cryptocurrency market, portfolio management, investments, risk management, etc.

Solution

Unicsoft dedicated team was in charge of developing a Cryptocurrency price prediction solution based on LSTM models and sentiment analysis of Crypto forums and channels. The model provides the prediction of 4 type of price: ‘Open’, ‘High’, ‘Low’, ‘Close’ for every Cryptocurrency individually.

The processes of price prediction model is recomputed constantly by AI Engine, potentially in a constant update loop. The model works in real time.

The model accepts any number of input parameters (the network architecture allows you to add many technical market characteristics of a selected cryptocurrency), and gives a forecast at specified prices (‘Open’, ‘High’, ‘Low’, or ‘Closed’ ) at the output. The architecture also provides a simple way to add new parameters to the line of the forecast in the future: for example, the forecast of the Volume of funds for this cryptocurrency or another indicator. No significant code changes will be required: flexible parameters are already embedded in the solution.

At present, the models work with a time step of one day, and make forecasts for 7 days ahead (forecast interval is adjustable); however, the solution can be applied to hourly input data and the forecast horizon set in hours.

The solution was deployed in Python, Jupyter Notebook deployed for demo purposes. All documentation and the code was delivered to the customer on time and according to the initial requirements.

Result

The team created the fully ready working solution for Cryptocurrency price prediction within four weeks.

The main challenges were:

• Research and selection of the optimal model architecture for Cryptocurrency price prediction within this solution.
• Improving prediction quality, optimizing time and resources for model training and real-time prediction. In the result the client received a model with 73% – 90% accuracy of prediction.

Also, we provided the client with the demonstration materials about this solution. It’s allowed the client to demonstrate the capabilities of this solution both to specialists in AI and to investment market traders as well as to those interested in investing in this project.  So the Client has an opportunity to attract new users and investors to his AI based product.

Business domains: E-commerce

Expertise: NLP, Text Mining, Data Science

Technology and tools: Python, NLTK, NLP

One of well-known Central Europe e-commerce merchants asked Unicsoft for cooperation to create a tool intended to analyse a customer’s feedback on goods purchased through web marketplace, The business goal was to increase customer loyalty, drive business changes, and deliver real return on investment. Customer experiences fall into three basic categories, positive, negative or neutral. Through sentiment analysis, the goal was to detect the tone and temperament of each and every word found in a customer’s social postings and categorize those sentiments as either positive, negative or neutral.

After data cleaning and munging , an initial step of words tokenization was applied; having it done, an NLTK tools was used to define synonyms, semantics, and overall mood of feedbacks; aligned with scores given by these particular feedback authors, a section of manual business logics was brought in: language specifics, abbreviation, collocations and vernacular expression played a significant role in overall semantic analysis. Furthermore, analysis of new products or product lines could significantly affect overall strategy. Alongside with NLP and semantic analysis, there were used a Data Science techniques : given a data from social network that customer logged in through, a set of demographic features was involved to model, summing up into complex analytical solution.

Unicsoft set a dedicated team comprised of highly-skilled analysts and mathematicians and software developers; this particular solution helped the Client to define his marketing and sales strategy which resulted in 10% revenue increase within one year after the deployment. The Client noticed the overall high-level of Delivery and solution architecture.

Business domains: Finance, e-Commerce

Expertise: Machine Learning, Data Security

Technology and tools: R, Python, mlLIB

We live in the “era of data”. Everyday companies face a compelling number of interactions with data source, and the more data is being produced, the more risks of this data to be used maliciously arise. Our Client, European start-up had an idea of developing a solution for the forecasting of spear-phishing of personal data for a wide scope of organizations located in various countries. This type of solution is excessively sought-after nowadays – especially combined with ML approach for striking prediction accuracy. Knowing about Unicsoft expertise in that domain and having previous successful track record with us, Client decided to choose us as an authorized solution developer.

Firstly we defined main datasources: the pinnacle was VCDB database (catalog of data security incidents using VERIS framework). Second source used in the analysis is FT500 data set in 2016. Then, after merging these two data sets we obtained set for predicting data breach probability for a particular company. Key ML models were GLM and Random Forest models with RF prevailing because of its higher precision. Moreover, after implementation of Monte-Carlo simulation methods we added prediction of incident density within particular timeframe. As an additional perk we predicted expected loss in USD in case of attack for various industries.

Solution was accepted positively right after its demonstration by various investors and got a lot of encouragement for the further development into more complex product with extended functionality. Due to model precision that amounted to 83% and the fact of Monte – Carlo simulation techniques implementation one may be confident that abovementioned solution has a huge potential and versatility.

Business domains: Automotive, Advertising

Expertise:Bigdata Processing, Predictive & Advanced Analytics, Machine Learning, IoT

Technology and tools: Apache Hadoop, Hive, Cassandra, Python, Scala, mlLib

One of the Leading Automotive Dealers (further – AD) that has been performing its activities since the 1990s in CIS and Eastern Europe is looking to increase the company’s revenue by exploring new monetization opportunities. The idea is to use the data collected from smart devices installed into a car for Target Advertising and to increase additional revenue stream through presenting precisely targeted, high-quality ads to car owners and sharing added value with selected DSP and SSP platforms.

AD’s internal analytics team required additional expertise which was not available at the moment, specifically: expertise in complex BigData processing and building SaaS solutions for IoT and Advertisement domains. Unicsoft was able to offer the required expertise which laid grounds for further long term collaboration.

Unicsoft successfully implemented and deployed the solution which allowed AD to build additional revenue stream in cooperation with several DSP and SSP platforms; R&D investment carried out by AD reached the breakeven point within 12 months.

The prediction of car owner’s response lead to lower than expected number of opt-outs, positive response of car owners to new service and high advertising conversion rates.

Given these projects are completed, AD indicated a high level of delivery, R&D value and top-notch class of specialists involved. AD encouraged Unicsoft on further partnership that led to another Data Science initiative in Automotive Insurance domain.

Business domains: Finance, Microfinance

Expertise: BBlockchain, Predictive analytics

Technology and tools: Angular.js, Python, Django, Blockchain API library 1.3.3 & 1.4.0, MLlib, pandas. Docker

Client decided to extend their audience due to to novelty and security of the product. One of the peer-to-peer lending platforms, operating in the UK & Ireland and regaled as an one of the leaders in industry asked Unicsoft to provide development services. The key peculiarity of aforementioned system was the use of Blockhain technology. Client wanted to implement smart-contract system that would allow Users to exchange secured contracts alongside with opportunity to request & receive loans in Ethereum as well as in more “accustomed” currencies.

One should mention that all of the Blockchain-related frameworks were successfully integrated to the system, particularly, to its core. As far as an adherence to microservice paradigm was chosen, we were quite indubious regarding framework for UI, and perceiving both compatibility and resilience we chose Django.

Then a full-scale platform was developed within 12 months work.

The platform was successfully launched and reached its breakeven point within 6 months, providing the Client with the growing Users portfolio and maintenance of financial risks thus positively affecting the revenue. Portfolio, in its turn, accreted to 115% within three months after launch compared to a same period of the previous year.

Business domains: Media, Entertainment, Recommendation engine

Expertise: Bigdata Processing and Analysis, Machine Learning, Predictive Analytics, Recommendation Engine

Technology and tools: Python, Scala, Apache Spark

One of the leading Software Development Agencies (SDA) from the UK contacted us for cooperation on behalf of one of its Clients, who performed activities in the Content Media Market. The end-client successfully launched a platform similar to 9gag and Reddit alongside a mobile application. The Client’s business was focused on enhancing platform monetization by increasing the end-user’s loyalty and willingness to click & go to the partners’ offers.

The high-level goal was to build a recommendation engine which would use Big-Data analysis and machine learning to analyze individual end-users’ preferences and to help discover new content accordingly.

The project comprised two substantial parts – setting up the dedicated team of experts for continuous collaboration and transforming business needs into added-value solutions, in addition to direct development of the aforementioned. Unicsoft allocated two in-house experts, as well as quickly engaged one pre-interviewed expert from the HR pool. The proposed solution consisted of researching and developing two different Recommending Engines, each of which used different approaches (content-based and user-based).

As a result, SDA met all the deadlines for this Client, having brought the highest level of delivery and technical expertise. Client, respectively, managed to increase its audience monetization crucially (for the first six months after the deployment, click-and-go for partners offers grew by 20% compared to the previous period), whilst the overall audience increased by approximately 8%.

Business domains: FMCG, brand management, customer response prediction

Expertise: Bigdata Processing and Analysis, Advanced Analytics

Technology and tools: R, Python, Tableau

One of the FMCG leaders, Large tobacco company, approached us with a need to expand their brand-line alongside with increasing the number of distribution outlets. The Initial and foremost goal was specified as prediction of customer response to various marketing collaterals and design elements, i.e. different advertisement types, external branding elements such as outlets, big boards, and even colour combinations used in visual materials. Large tobacco company possessed a significant amount of historical data comprised of sales data and description of branding action of a particular moment. The final model was supposed to be complex and intended to detect any response within the predicted deviation of customer’s behaviour or purchasing activity which might have been caused by promotional and/or advertising activities.

Having considered the Client’s current and future needs, the key points of the solution were defined as: fine-tuning of current Client ETL solution; multicomponent predictive model containing several sub-models; price elasticity and sensitivity model; embedded Bi-solution showing customer’s response to any applied activities in both short-term and long-term time frames.

The initial effect of the model became noticeable and appreciable within first four months. In addition to that, due to the complex analytical model for new activities prediction and doing business considering its “advice”, Large tobacco company managed to have a solid increase through customer response rate and, consequently, a revenue increase.

Large tobacco company pointed out a remarkable level of Delivery as well as high level of overall performance provided by deployed solution.

Business domains: Gaming, E-commerce

Expertise:DWH, Big Data Processing, Machine Learning, AI, Fraud detection & prevention

Technology and tools: Apache Spark, Hadoop, Hive, Python, mllib, Random Forests

A well-known online game producer that launched the line of browser games faced a need to maximize monetization of one of his games through a thorough analysis of gamers’ data (purchases, activities, etc.) as well as need to make internal game security stronger due to up-to-the-minute detection of fraud and cheating activities.

As a result of Business Analysis stage, Unicsoft identified following deliverables as required to fulfil set business goals:

1. Gaming analytics: given a historical data for entire game users and after its preprocessing (cleansing, enrichment, etc.) a cluster of active users has been defined. Then, key features of gaming behavior and in-game purchase occurrences were carefully analysis with the purpose to tune up gaming AI: main thing was to keep balance between game difficulty for keeping gamer involved and adding several “hard spots” which might be passed easily if particular in-game purchase made.

2. Anti-fraud module: analysis of suspicious activities occurred during gameplay or internal currency purchase: detecting fake cards and adding them into database; detection and consequently prediction of cheating activities in game process (multiple accounts, fake teams, etc.)

3. DWH/ETL auditing and improvement: regardless of having plenty of various datasets, all of them were stored in old and rather unstable SQL-type solution, so need of rapid access arose eventually; after thorough analysis of requirements, Clients’ data was successfully transferred to BigData solution that consisted of Hadoop + Hive alongside with analytics modules plugged onto.

Business domains: FMCG, commerce/retail

Expertise: NLP, Text Mining, Data Science

Technology and tools: Python, NLTK, Apache Solr

The largest Central & Eastern Europe retailer asked Unicsoft faced a need of a tool that would be a capstone of category management: having over 50000+ of PLUs and getting a dozens of new names every couple of days, Client had only a manual tool for category definition. So far, the basic intention of a tool was a creation of “tag clouds” supposed to tell one sub-categories from the other ones based primarily on a descriptive information, and, consequently, for proper management of PLU turnover, placement and promotion.

First step was defined as data preparation and enrichment: given more than 50k PLU names alongside with variety of features (main category name, manual tags, supplier notes, etc.), a main thing was to set up an proper DWH solution for storage and handling with further data pre-processing activities. Starting with SQL-type DB combined with Python libraries for pre-processing, a huge bunches of data was cleaned up and enriched. Next step was a tokenization of data: main goal was to create a tags cloud for working out subcategories : I.e, given the “sweets” category through applying these methods based on keywords, a list of sub-categories came up (diabetic sweets, soya sweets, etc.). Subsequently, that approach was applied to entire portfolio of PLUs providing opportunity not only to categorize existing goods but to process incoming ones through fetching their description and its analysis.

Break-even point for this particular solution was reached after 9 months of use through saving a significant amount of money by the cost-cutting on the staff that previously did such activities manually.

Business domains: Commerce, Pharmacy

Expertise: Data Science, BI, Predictive analytics

Technology and tools: Python, noSQL, xgBoost, Tableau

One of Unicsoft clients, a large pharmaceutical network came up with request for a collaboration regarding development and implementation of OOS (Out of Stock system). For that current moment, Client had over 2000 drugstores all around the country and a reporting module comprised of reports concerning revenues and daily turnover.

The main goal was to set up a proper ETL process and perform a predictive analytics module intended to predict absence of drugs & remedies alongside with BI-tool deployment for ad-hoc reports and predictive analytics. Subsequently, the request evolved into not only a analytics & reporting module, but a complex solution providing access for suppliers to let them see the picture considering sales & trends of their products in any point of sale.

Unicsoft started from setting a dedicated team comprised of data architects and engineers to establish and consequently maintain data migration process. Considering necessity of being able to do close to-real-time analytics as well as requirements of DB consistency and availability, a noSQL architecture was considered as the most suitable. Thence, a several data scientists were involved to manage data enrichment and munging process. Having this step completed, a predictive analytics module was implemented to calculate estimated risk of product absence and predicting sales trend. As a copestone, a BI module was added providing a visualization of predictive module outputs.

Client underlined well-organized and orchestrated delivery process from Unicsoft side, and, consequently, this case of collaboration led us to establishing a successful cooperation along with new projects for pharmaceutical industry.

Business domains: Finance, Insurance, Automotive

Expertise:Bigdata Processing and Analysis, Predictive Analytics, Machine Learning

Technology and tools: Python, XGBoost, Neural Networks, Decision Trees

Being aware of successful cooperation between Unicsoft and one of Lead Automotive Dealer, an International Insurance Broker (further referred as IB) contacted Unicsoft with an idea to develop a solution for a data-driven assessment. Being a partner of several automotive dealers, IB has an access to data received from in-car gauges and in-car smart devices. Therefore, there are two sources of available data: historical and descriptive data about users and the above mentioned data coming from devices, which build the foundation for increasing accuracy of the final model through determining a driving style and User’s proneness to an accident.

Key points of solution for IB were defined as: choice of proper solution for handling streams coming from two data sources (internal from IB and external from ADs); data cleaning, noise and outliers reduction; development a Scoring model for risk level and proneness assessment;machine learning and model improvement within a specified period of time after its launch.

Unicsoft set a team of Advanced Analytics experts along with DevOps and Data Warehousing specialists, which completed initial data cleaning and predictive model within two months and a deployment-ready version of solution within seven months; further, a support team was set up and introduced to IB.

The solution was successfully launched and had a commercial success with reaching the breakeven point within the first 18 months. The solution generated additional sales by offering a personal value proposition due to implementation of multi-feature risk assessment model and proper approach of rate evaluation for a particular User.