Our hand-picked team consists of passionate, hard-working, collaborative, and forward-thinking
individuals with one common interest in mind: a love for data and retail.
Project Overview:
This project focuses on enhancing client support and delivery operations by leveraging data analytics, strategic management frameworks, and process optimization techniques. The objective is to analyze the current support processes, identify inefficiencies, and redesign workflows to improve performance, client satisfaction, and resource utilization.
The project starts with a comprehensive analysis of the current workflows in client engagement and delivery, using historical data from 2023-2024. Advanced business analytics techniques will be employed to extract insights from this data, such as identifying bottlenecks, and recurring client issues that delay resolution times.
Based on this analysis, the project will propose a re-engineered process that eliminates inefficiencies and better aligns with business goals and client needs. A capacity planning model will also be developed to build a customer support process allowing the company to allocate resources more effectively across departments using predefined KPIs.
Customer journey mapping will be used to further enhance the client experience, ensuring that all touchpoints—from onboarding to ongoing support—are seamless and efficient. This will result in a client-centric support system that boosts satisfaction and retention.
The success of the new processes will be measured by tracking key performance indicators (KPIs) like response times, resolution rates, customer satisfaction scores, and alignment with service level agreements (SLAs). The final outcome will be a strategic roadmap that includes recommendations for further digital transformation and continuous improvement in support and delivery functions.
Skills and Tools Needed:
Benefits for the Company:
Overall, this project will provide the company with a strategic framework that improves both short-term operational performance and long-term client engagement and support processes.
Project Overview:
Historically, Jenkins has been our main CI/CD tool, offering extensive flexibility for custom workflows. However, this flexibility comes the cost of having to maintain a large amount of plugins and dependencies. The aim of this project is to transition to a more modern CI/CD platform that has built-in automation features that will reduce the maintenance complexity. The second part of the project will focus on the integration of Terraform with GitLab to automate infrastructure management using Git as the main source of truth.
Goal: Create a PoC of a CI/CD system migration and infrastructure automation using Terraform and GitLab runners.
Steps:
Technologies: Jenkins / GitLab runner / Kubernetes / Argo CD / Terraform / Azure
Project Overview:
The goal of this project is to develop an automated framework to perform security testing on web applications. This framework will identify common vulnerabilities such as SQL Injection, Cross-Site Scripting (XSS), Broken Authentication, and Sensitive Data Exposure, using automation tools and custom scripts.
The system will be capable of :
The framework will leverage modern security tools and libraries, integrating with continuous Integration/Continuous Deployment (CI/CD) pipelines for early vulnerability detection in the development cycle.
Skills and Tools Needed:
To execute this project effectively, the following skills are required:
1. Programming Knowledge
2. Web Technologies
3. Security Basics
4. Automation Tools
5. DevOps
6. Reporting and Visualization
The goal is to analyze defect data from Quality Assurance cycles to uncover patterns, identify root causes to enhance the efficiency and accuracy of defect management within the software development lifecycle (SDLC) by implementing an AI-driven classification system for Jira bug tickets. This will be achieved by analyzing text-based inputs (e.g., ticket descriptions, comments) and additional features (e.g., issue type, testing phase, occurrence frequency) to automatically classify tickets by priority, component, defect type, and team assignment.
The project will involve training machine learning models, such as NLP-based or deep learning models, to identify patterns and categorize tickets based on historical data. This classification will improve prioritization, streamline workflows, and enable better resource allocation, reducing manual effort and minimizing errors.
Skills and Techniques:
Project Overview:
We aim to develop a fully free, AI-powered chatbot capable of searching and retrieving information from our extensive documentation stored on Google Drive. Using open-source tools and models, the chatbot will be able to answer user queries by locating relevant content across our files, summarizing the information in a clear response.
The solution will leverage free models like Meta’s Llama 2 or Mistral 7B, alongside LangChain for easy integration and document management and open-source vector database and search tools like FAISS for efficient retrieval.
The project offers the opportunity to build a sophisticated document-querying chatbot using cutting-edge, cost-effective AI technologies and to explore data organization and retrieval mechanisms within a real-world business context.
Project Overview:
Effective inventory management is crucial to meet customer demand while minimizing costs.
Forecasting plays a pivotal role in this process by predicting future demand, enabling businesses to optimize their inventory levels and replenishment strategies.
Accurate forecasts provide the foundation for setting key Inventory Replenishment Parameters. These parameters are what determines the optimal inventory levels ensuring that stock levels are maintained efficiently without overstocking or stockouts.
This project aims to assess how the forecast perfomance is impacting the inventory optimzation by analyzing multiple forecast scenerios issues and model their impact. And eventually the goal is to determine the right forecast adjustements to improve inventory optimization results by computing ROI metrics and simulate the impact based on different forecasts feed.
Through this analysis, we will explore the balance between demand forecasting performance and inventory optimization results in order to come up with the right forecast adjustments which will lead to reaching the right inventory levels and replenishment strategies.
Project Overview:
The project aims to enhance sensitivity analysis for retail shrinkage by addressing the lack of data in terms of quantity, diversity, and unseen but foreseeable scenarios. Retail sensitivity models often face challenges such as overfitting and unreliable predictions due to limited or heterogenous datasets. To overcome these challenges, the project will leverage generative AI for the following purposes:
Founded by experienced data scientists and retail experts, Cognira is the leading artificial intelligence solutions provider.
