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Second Foundation AI

Connecting business leaders with AI driven solutions that solve real-world business problems…and we do it at scale!

Case Study:

Maswer Automates Unit Management with Custom Control System

Challenge

Maswer, a global automotive service provider with operations in Germany, Spain, India, Mexico, and the U.S., faced a critical operational bottleneck:

  • Manual inspection and validation processes managed via Excel

  • Lack of real-time visibility and traceability across unit workflows

  • Delays in reporting and decision-making for internal teams and clients

  • High risk of data inconsistency and inefficiency across service sites

To maintain its reputation for quality and reliability, Maswer needed a scalable, automated system to streamline unit management and quality control.

Solution

Maswer partnered with Ensitech to develop a turnkey “Project Control System” tailored to its operational needs. Key components included:

  • Mobile field application for unit inspection, repair, and validation

  • Back office system for centralized oversight and reporting

  • Lightweight REST APIs to support flexible, step-by-step process changes

  • High-availability cloud infrastructure on Microsoft Azure

  • Real-time reporting interface built with React

  • Role-based access and notification services via Azure App Service

The project followed CMMI-DEV Level 3 standards and agile SCRUM methodology, ensuring high-quality execution and adaptability.

Results

  • Delivered full traceability of units across inspection and repair workflows

  • Reduced time to generate internal and client-facing performance indicators

  • Improved data reliability and operational transparency

  • Enabled real-time reporting and decision-making across global sites

  • Established a scalable foundation for future process automation

Case Study:

FRISA Uses Deep Learning to Automate Aerospace Quality Checks

Challenge

FRISA, a global leader in seamless rolled rings and open die forgings, supplies high-performance components to aerospace turbine manufacturers. At its plant, over 150 complex rings were manually inspected daily—a process that was:

  • Labor-intensive and costly

  • Dependent on subjective inspector judgment

  • Prone to human error and inconsistent classification

  • A bottleneck for engineering teams and decision-making

To reduce costs and improve precision, FRISA sought an AI-driven solution to automate inspection and classification.

Solution

FRISA partnered with Ensitech to develop a custom deep learning system for quality control. The project focused on:

  • Aligning and centering 3D point cloud scans of forged parts

  • Defining machining height, optimal centering zones, and layout classification

  • Building a two-stage pipeline:

    • Stage 1: Alignment using classical optimization and data cleaning

    • Stage 2: Layout using neural networks trained on labeled inspection data

  • Leveraging Microsoft Azure for development and AWS SageMaker for deployment

  • Applying transfer learning with architectures like ResNet-34, ResNet-50, Inception, and DenseNet to reduce false negatives

The solution was built collaboratively, with Ensitech embedding mathematicians and engineers to deeply understand FRISA’s inspection challenges.

Results

  • Achieved 95% precision in identifying parts with potential quality issues

  • Eliminated false negatives and reduced false positives to just 5%

  • Cut inspection time dramatically, freeing up engineering capacity

  • Improved decision-making accuracy and consistency across the plant

  • Enabled reuse of AI models and insights in other FRISA development initiatives

Case Study:

RCSA Unifies Financial Operations with Custom Cloud Platform

Challenge

RCSA (Red de Colegios Semper Altius), a global network of bilingual Catholic schools across 19 countries, faced mounting complexity in financial management:

  • Fragmented systems across dozens of institutions

  • Duplicated records and manual workflows

  • Delays in budget approvals and decision-making

  • Limited visibility into real-time financial data

The finance team needed a centralized, automated solution that could scale across the network and align with RCSA’s internal processes.

Solution

RCSA partnered with Ensitech to co-develop a cloud-based Financial Management Platform (FMP) using Microsoft Azure and Power Platform tools. Key components included:

  • Data integration via Azure Data Factory and Logic Apps

  • Centralized data model in Azure SQL Server for reporting and APIs

  • Interactive app for budgeting, forecasting, MPY, and CAPEX

  • Automated approval workflows to eliminate bottlenecks

  • Real-time dashboards powered by Power BI

  • Role-based access control using Azure Security Groups

The platform was designed collaboratively, with deep alignment to RCSA’s operational structure and school network dynamics.

Results

  • Enabled faster, more strategic decision-making with real-time data

  • Reduced manual errors and duplicated records through automation

  • Improved operational efficiency across finance, HR, IT, and leadership

  • Strengthened transparency and control with robust reporting tools

  • Achieved high user adoption—even among initially hesitant teams

  • Sparked demand for additional modules before they were announce

Second Foundation AI

Case Study:

Long-Term Demand Forecasting for High-Methane Network Gas in Poland

Challenge

Polskie Górnictwo Naftowe i Gazownictwo SA (PGNiG) required a robust, long-term forecast of network gas demand across Poland. The model needed to account for:

  • A 30-year forecast horizon

  • Sector-specific economic drivers

  • Decarbonization targets aligned with national and EU policy

  • Integration of renewable gas development scenarios

Solution

SGH Warsaw School of Economics, led by Daniel Kaszyński and supervised by Prof. Bogumił Kamiński, was engaged from August 2021 to February 2022. Their scope included:

  • Identifying key demand determinants across economic sectors

  • Designing a forecasting methodology tailored to long-term energy planning

  • Developing sector-specific models incorporating decarbonization goals

  • Implementing the system in R, with a complementary Generation Mix model in Python

  • Delivering a complete 30-year forecast based on validated assumptions and input data

Results

  • PGNiG received a comprehensive, sector-informed forecast aligned with national energy policy

  • The system enabled scenario modeling for renewable gas integration and decarbonization pathways

  • The engagement was completed on time, with diligence and professionalism

  • SGH was recognized as a reliable and responsive partner for analytical and forecasting work


Case Study:

Advanced Algorithm Development and Software Integration for Real Estate Analytics

Challenge

Nekken required specialized support for the Amron Project, a strategic initiative focused on developing analytical algorithms for real estate price analysis. The project demanded:

  • Rigorous validation of algorithmic documentation and methodology

  • Development of predictive models for trend analysis and collateral valuation

  • Seamless integration of analytical workflows into a scalable software infrastructure

Solution

DS360 LLC was engaged from October 2020 to March 2022 to deliver a comprehensive technical solution. Their scope of work included:

  • Reviewing and validating algorithmic documentation to ensure consistency and correctness

  • Designing and implementing analytical models in Python, including synthetic data generation and proof-of-concept development

  • Building object-relational mapping in Java using Spring Data JPA for backend integration

  • Deploying automated reporting workflows using Artemis-based queuing within Dockerized environments

Results

  • Delivered robust, validated algorithms tailored to real estate pricing and trend forecasting

  • Successfully integrated backend functionality into a scalable, containerized software architecture

  • Demonstrated high technical proficiency and responsiveness throughout the engagement

  • Completed the project on schedule, meeting all performance and quality expectations

Case Study:

Credit Scoring Model Optimization for Plenti

Challenge

Plenti, a digital rental and leasing platform, sought to enhance its credit risk assessment capabilities. The company faced challenges in:

  • Streamlining customer verification across rental, leasing, and debt collection workflows

  • Improving data governance and assessing data quality

  • Defining and refining risk categories, including “good,” “default,” and “fraud” profiles

  • Establishing a target state for analytics functions and performance monitoring

Solution

DS360 LLC was engaged from October 2021 to April 2022 to deliver strategic consulting and technical support. Their scope included:

  • Analyzing existing business processes related to customer verification and risk management

  • Conducting a data governance audit with emphasis on data quality and reliability

  • Refining customer segmentation and risk definitions to support model development

  • Designing the target architecture for data and analytics functions

  • Developing analytical dashboards to monitor key performance indicators across risk, business, and data domains

Results

  • Plenti gained a clearer, more actionable framework for credit risk evaluation

  • Customer verification processes were optimized for speed and accuracy

  • Data governance improvements supported more reliable analytics outcomes

  • The dashboards enabled real-time monitoring of risk and operational metrics

  • DS360 was recognized for its professionalism, responsiveness, and strategic insight


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Time to Scale!

Second Foundation AI

Case Study:

Rakuten Transforms Fund Advisory with Neuranet AI

Challenge

Rakuten, a leading financial services provider in Japan, faced a high-volume, high-velocity data challenge:

  • Thousands of fund brochures arriving in unstructured formats

  • Constantly shifting market conditions requiring real-time analysis

  • Advisors and clients demanding faster, smarter fund recommendations

  • Compliance and data residency requirements unique to Japan

Solution

Rakuten partnered with Tekmonks to deploy Neuranet, an enterprise-grade AI platform tailored for fund selection. Key implementation steps included:

  • Structuring fund brochures into searchable knowledge blocks using GARAGe templates

  • Integrating real-time market data for dynamic fund performance analysis

  • Enforcing secure, role-based access for brokers, advisors, and retail clients

  • Embedding Neuranet into Rakuten’s SuperSearch platform for seamless user experience

  • Creating a continuous learning loop from user interactions and new fund data

Results

  • Delivered 60% faster fund recommendations, reducing research time from hours to minutes

  • Ensured full compliance with encrypted access and data residency in Japan

  • Provided private, policy-aligned AI responses sourced from Rakuten’s own data

  • Scaled effortlessly with Rakuten’s expanding product offerings and user base

  • Earned advisor trust by enhancing—not replacing—human expertise