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A top insurer aligned data and AI to cut waste, speed up delivery, and turn preauthorization into a fast, accurate, real-time process.
Energisa modernized 70 systems by migrating to AWS with e-Core’s support, gaining scalability, availability, and performance in the energy sector.
Background Ricksoft is known for its popular work and project management apps on the Atlassian Marketplace, including WBS Gantt-Chart for Jira and Excel-like Bulk Issue Editor for Jira. Originating in Japan, the company has grown to serve over 8,000 customers worldwide through nine apps. Their tools are trusted by some of the world’s most innovative companies to boost productivity and manage complex work. By 2019, Ricksoft was seeing growing demand from customers in the Americas and Europe, and needed to scale its support operation beyond Japan. To serve new markets effectively, they had to provide fast, high-quality service in English and across time zones, while maintaining the responsiveness and care that defined their brand. That’s when they turned to e-Core. Our approach We designed a support solution based on three key principles: timezone alignment, consistent high-quality service, and cultural alignment . To get things right from the start, our analysts flew to Japan for in-depth training. They didn’t just learn the products; they learned how Ricksoft works , communicates, and supports customers. Once training wrapped, we built the support operation around Ricksoft’s priorities: timely responses, consistency, and a thoughtful, human tone. We created detailed documentation and a clear process for hiring and onboarding, ensuring every new analyst could step into the role with confidence and alignment. We also introduced monthly updates to the knowledge base and took responsibility for keeping existing articles relevant. That work paid off: most tickets are now resolved in just two to three exchanges , and self-service performance has improved across the board. Today, Ricksoft holds a satisfaction rate of 89 percent and a CSAT of 4.63 out of 5 , well above industry averages. As new analysts joined over the years and support expanded to more products, the service quality remained consistent. “We place great confidence in e-Core’s hiring decisions,” says Alessandro Cristiano, Support Manager at Ricksoft. “They understand how we work, and their mission, values, and culture attract good talent. We had five different generations of agents in seven years, and the work was consistent all the time.” Building long-term value through support. To make support even more impactful, we set up a feedback loop between support and product. Recurring issues and feature requests are flagged and shared with product managers, turning support into a valuable channel to inform product strategy. Tone and empathy matter, too. Ricksoft’s brand is rooted in respect, clarity, and thoughtful communication. We greet returning customers by name, tailor our closings, and have consistently met their SLA targets—with 99% first-response compliance within 12 hours or less. “What is special about e-Core is that they listen first,” Cristiano says. “They don’t try to mold things just for efficiency if it doesn’t work for you. First, they absorb your culture, and then transform it.” To strengthen Ricksoft’s presence in the Atlassian Marketplace , we introduced a post-ticket review request process. That simple step led to more customer reviews, better app ratings, and increased visibility. “We’re now at the top search results, which helps increase our app installs, and ultimately our revenue,” says Cristiano. We also monitor Ricksoft-related activity in the Atlassian Community. When their apps are mentioned, our team responds quickly and accurately, helping Ricksoft remain visible, helpful, and credible in the ecosystem .
Our Work Migrated ITSM tickets and project work from ServiceNow, VersionOne, and SharePoint to Jira Service Management (JSM) and Jira Software, creating a unified platform for managing IT services and project tasks Simplified change management by replacing multiple forms with a single change request issue type in JSM Set up automation rules for ticket prioritization, comment synchronization, and SLA tracking Implemented Insight Asset Management integrated with JSM for real-time visibility and streamlined asset management Provided comprehensive training for JSM agents and administrators
By Gabriel Marchelli, Startup Solutions Architect at AWS; Bruno Vilardi, Solutions Architect at e-Core and AWS Community Builder; Matheus Gonçalves, Data Engineer; and Ricardo Johnny, Cloud Architect at EEmovel.
About the Client This e-Core client is a technology company focused on the real estate sector. Its web platform provides real-time data, such as insights, market reports, search tools, property appraisals, and investment feasibility analysis for real estate professionals, brokers, and asset managers.
About the client The client is the first fintech specialized in import solutions for businesses in Brazil and Latin America that offers financial solutions in credit, financing, and currency exchange, along with technology solutions that simplify, streamline, and unify services on a single platform. The company stands out through three fundamental pillars: Technology : A team of programmers specialized in innovative solutions. Financial Expertise : Financial knowledge to identify the best products and maximize results. Import Focus : Founded in international trade with a long history in one of Brazil’s leading trading companies. The challenge The company operates through Sales Development Representatives (SDRs) who create business opportunities by contacting clients through various channels, including direct phone calls with leads. The business challenge was to extract quality metrics from these calls and ensure optimal use of the sales pitch , improving SDRs’ operational quality and increasing business opportunity conversion. Previously, all call analysis to identify service improvement points , pitch adjustments, and service quality assessments were conducted manually by the manager, who would listen to each call’s audio and perform evaluations. To help generate automated reports for analyzing these calls, e-Core offered support with a custom artificial intelligence solution using AWS resources . The solution The solution begins by using the SDRs’ phone call recordings with leads. The first step was to create a processing pipeline to convert the recordings into text. With the transcribed audio, we used Generative AI to evaluate the call. We developed a prompt to assess the dialogue between the SDR and a lead, analyzing aspects such as pitch adherence, communication skills, and presentation of the company’s product and services. The final analysis result provides constructive feedback focused on areas for improvement, development, and motivation for the SDR. We used a Generative AI model to automatically transform call transcriptions into structured data with Amazon Bedrock, extracting and organizing essential fields, and generating constructive feedback to improve SDR performance. The resulting file is stored in an AWS S3 bucket and sent to the manager’s area on the company platform via an AWS SQS queue. Below is the architecture of the custom solution developed by e-Core.
e-Core We combine global expertise with emerging technologies to help companies like yours create innovative digital products, modernize technology platforms, and improve efficiency in digital operations.
The challenge Wilson Sons , the largest integrated port and maritime logistics operator in Brazil, embarked on a digital transformation journey to modernize its IT infrastructure and processes. With a large number of applications and extensive operations, Wilson Sons needed a secure and compliant cloud environment that would provide high availability, security, and auditing capabilities. The challenge was to establish clear usage and access rules, adhering to the Principle of Least Privilege, while ensuring the security of the new cloud infrastructure. The solution To address these challenges, Wilson Sons partnered with e-Core to develop and implement a cloud adoption strategy . The strategy included migrating the existing VMware-based infrastructure to AWS using VMware Cloud on AWS, while also migrating certain applications directly to AWS services. Key elements of the solution included: Landing Zones: Structured environments were created for development, testing, QA, and production across multiple accounts using AWS Control Tower and AWS Organizations to enhance governance and security. Single Sign-On Integration: e-Core integrated AWS Organizations with Single Sign-On (SSO) to simplify access management across multiple accounts, assigning permissions based on user roles and ensuring compliance with security requirements. The integration also included Domain Controllers to manage authentication requests, such as login and permission checks. Transit Gateway: All accounts communicated securely through the Transit Gateway, which acted as a cloud router, ensuring encrypted data transmission and streamlined account access. Security and Monitoring Tools: Several AWS services were deployed to ensure security and monitoring: CloudTrail: Enabled centralized logging, monitoring, and retention of account activities, simplifying operational analysis and auditing. AWS Config: Monitored and recorded configuration changes, automating desired internal configurations. Security Hub: Provided security alerts and compliance status monitoring. IAM Access Analyzer: Logged and restricted access to a dedicated security account for monitoring. CI/CD Pipeline: In the second phase, a CI/CD structure was created using AWS services: ECR for container image storage. ECS Fargate for running applications. CodeBuild for building images, CodeDeploy for application deployment, and CodePipeline for creating delivery pipelines. Application Load Balancer protected by AWS WAF and ACM-managed digital certificates. CloudWatch and Container Insights for monitoring and alerting. Results Centralized Account Management: Wilson Sons now manages all AWS accounts centrally with defined corporate policies, enhanced network traffic control, and secure connections between cloud and on-premises environments. Secure Cloud Migration: Applications were successfully migrated to VMware Cloud on AWS, and new applications were developed using AWS services with increased speed, availability, security, and auditing. Modernized CI/CD Pipeline: The CI/CD structure allowed for rapid application modernization and replication across other applications, further accelerating the digital transformation process. At e-Core, our expertise in cloud migration and digital transformation has made us a trusted AWS partner. We have successfully guided companies like Wilson Sons through their cloud journey, ensuring security, governance, and operational efficiency every step of the way. Ready to take your digital transformation to the next level? Discover why e-Core is a trusted AWS consulting partner . Learn how we can help your business achieve its goals with secure, scalable, and efficient cloud solutions.
The challenge A leading manufacturer faced significant challenges maintaining quality control throughout their production line. Poor issue control hindered process improvement and prevented productivity increases. The absence of a robust quality assurance system led to poor product quality, posing a risk to the brand’s reputation. Additionally, the manual registration of issues was prone to errors, making it difficult to identify and address process bottlenecks efficiently. The solution: digital inspection e-Core implemented a digital inspection solution to enhance quality control in the production line. This system was designed to log and analyze occurrences in real time, allowing quality control specialists to monitor and respond promptly. The digital platform recorded all relevant information and provided real time monitoring capabilities, enabling more effective responses to issues and facilitating productivity improvements. Results Improved Process Efficiency: The digital inspection solution significantly reduced errors and bottlenecks in the production line. Enhanced Product Quality: Improved quality control resulted in higher product quality, reducing the risk of damage to the brand’s reputation. Increased Productivity: The manufacturer experienced a notable increase in productivity due to more efficient issue tracking and resolution. Stronger Brand Reputation: Consistent product quality and reduced errors strengthened the brand’s reputation in the market.
This case study explores the collaboration between e-Core and a leading pulp producer, who faced a critical challenge: assessing the potential impact of frost on paper production. Frost poses a significant threat, ranging from minor disruptions to severe losses in cellulose output. Given the unpredictable nature of forest asset management, especially under the influence of climate change, our partnership aimed to develop an advanced frost detection mechanism powered by AI , enabling proactive strategies to mitigate potential losses. Opportunities The primary objective of this project was to conduct a proof of concept (PoC) to verify the ability to identify unexpected production changes, specifically those caused by frost. We analyzed data from the Monitora and Zeus systems — tools designed for real-time monitoring and data collection in forest operations — focusing on critical areas such as geographic intelligence and forest asset management, utilizing AI to manage the inherent unpredictability of this field. Project Stages Our project adhered to the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, a widely recognized methodology for data mining and machine learning projects. This structured approach guided us through six key phases, each contributing to the successful development of our AI-driven PoC. Business Understanding: We thoroughly understood the business objectives and PoC goals. Collaborating with stakeholders, we defined requirements, set key performance indicators (KPIs), and ensured alignment with the operational context. Data Understanding: We assessed the available data to understand its quality and relevance. Although Sentinel-2 satellite images were considered, they were ultimately deemed impractical due to complexities and uncertainties. Key variables, particularly meteorological factors like humidity and minimum temperature, were identified as critical for frost prediction. Data Preparation: We focused on ensuring the dataset’s quality, selecting the most relevant columns and applying interpolation to create a comprehensive data area. This preparation was crucial for feeding the model with robust inputs. Modeling: In this phase, we built the PoC’s analytical foundation. We selected appropriate algorithms, split data into training and testing sets, and continuously optimized performance using accuracy and recall metrics. Evaluation: A thorough analysis of the model’s performance was conducted, ensuring it met the business understanding criteria. Collaboration with the client’s experts helped validate predictions and refine the model as needed. Implementation/Delivery: The final phase involved implementing and delivering the PoC’s results. We worked closely with the client to validate and refine outcomes, ensuring the model aligned with both business and technical goals. Project Results The project led to significant advancements in frost detection for our pulp sector partner through a series of five experiments. Leveraging AI and machine learning models, we developed a highly accurate classifier that reliably distinguishes frost events from other anomalies. The experimentation process, involving several iterations, was vital in optimizing the model’s performance. Key Results: Enhanced Frost Detection Accuracy: The final model achieved approximately 98% accuracy in identifying frost occurrences, making it highly reliable. Refined Feature Selection: Meteorological factors, particularly minimum temperature and humidity, were identified as the most crucial variables for predicting frost, streamlining the model for better performance. Consistent Historical Data Performance: The model consistently identified frost events when applied to historical data , avoiding false positives during periods without frost. Improved Data Handling: Unsupervised learning in Experiment 5 effectively addressed challenges in labeling large datasets, enhancing the model’s robustness. Operational Impact: The AI-driven frost detection model enables proactive decision-making, helping the company mitigate potential losses from frost damage, thereby reducing operational risks and improving resource management. Advanced AI Solutions Using precise data classification, our team developed an advanced AI model capable of identifying frosts with notable accuracy. One particular experiment stood out, achieving 98% accuracy in tests and consistently validating the model’s effectiveness with previously unclassified historical data. The chosen Random Forest model, known for its efficiency and ease of training, provides a robust solution for precise frost identification. Projected Benefits Early identification of at-risk areas within forest assets Optimized resource allocation based on predictive analytics Improved accuracy in weather forecasting, enhancing operational decision-making Automation of processes leading to increased efficiency and significant cost reductions Strengthened technological innovation, reinforcing the company’s competitive market position Future Perspectives In conclusion, the Proof of Concept demonstrates the feasibility of using data from Monitora and Zeus systems, combined with AI and machine learning algorithms, to detect changes caused by frost. This approach significantly enhances forest asset management, particularly in a field as unpredictable as forestry under climate change. Looking ahead, there are opportunities to further refine the model by exploring more complex data characteristics, integrating additional relevant information, and expanding its capacity to recognize a broader range of climate anomalies.