SnapSoft Cloud & AI Transformation Portfolio

By Liminary

July 20, 2026

About this collection

**The Engine of Modernization** This collection details how SnapSoft engineers complex cloud migrations and AI integrations across diverse sectors. The core narrative revolves around transitioning legacy or multi-cloud setups into highly optimized AWS environments to eliminate operational drag. * **Rapid Infrastructure Overhauls:** SnapSoft executes high-stakes migrations with extreme velocity. They moved **FintechX** to AWS in just two weeks and reduced **Meta Carbon's** infrastructure costs by 30 percent through Terraform automation. * **AI as a Workflow Catalyst:** Generative AI is consistently deployed to eliminate manual friction. For **SkillShow**, AWS Transcribe reduced video editing turnaround from three weeks to 24 hours. **MBH Bank** utilizes an AWS Bedrock-powered code analyzer to automate complex linting tasks. **The Single Decisive Reason** The fundamental value proposition across these case studies is the elimination of cognitive logistics and manual intervention. Whether building a custom CMS for **CAPIT Reading** using an NX monorepo or deploying a serverless scheduling engine for **Emerald Transformer**, the focus remains on replacing manual effort with scalable, automated architectures. This portfolio serves as a blueprint for leveraging cloud-native tools to achieve immediate go-to-market acceleration.

Curated Sources

Optimizing Droople’s Water Tech Platform: Enhanced Security and Scalability with AWS - SnapSoft

Droople, a Swiss-based water technology company specializing in IoT-enabled smart water monitoring, partnered with cloud consultancy SnapSoft to remediate and optimize its AWS infrastructure. Droople’s existing setup faced serious security and performance bottlenecks: the primary production database ran on PostgreSQL 12, which was approaching its end of life in 2024. Furthermore, this database was publicly accessible, exposing the firm to security vulnerabilities, and the development and production environments lacked isolation, risking accidental production alterations. SnapSoft designed and executed a migration plan to address these limitations. The production database was fully isolated inside an Amazon Virtual Private Cloud (VPC), restricting public internet access. Droople’s AWS Lambda functions were modified to operate within the VPC to retain secure access to private database resources. The database was successfully upgraded to Amazon Aurora Serverless with minimal downtime, improving performance, scaling capabilities, and cost efficiency. To manage credentials securely, SnapSoft integrated AWS Secrets Manager. To improve governance and control, SnapSoft restructured Droople’s single-account environment into a structured multi-account architecture utilizing AWS Organizations and AWS Identity Center. This separated production and staging environments, mitigating risk. Automated CI/CD deployment pipelines using AWS CodeBuild replaced the less secure Amplify deployment flow for database migrations and Lambda deployments. Additionally, SnapSoft developed an automated, secure cross-account data copying solution to sync production data to the development environment weekly for testing. Security was hardened with continuous threat detection and compliance tools, including Amazon GuardDuty, AWS Security Hub, and Amazon Inspector.

Key Takeaways

Enterprise risk mitigation is achieved by moving away from shared-account architectures, proving that environment isolation is a prerequisite for security compliance.
Transitioning from publicly accessible RDS instances to private VPC-enclosed Aurora clusters dramatically reduces the corporate attack surface without sacrificing operational flexibility.
Automating the synchronization of production-grade datasets back to test environments demonstrates a secure pathway for staging validation without compromising the live environment.
Transitioning from legacy managed services like AWS Amplify to AWS CodeBuild provides the granular controls required for sophisticated database migrations and serverless deployments.

Pioneering Cloud-Based SCADA: Conduit Power's Hitachi SCADA Launch on AWS - SnapSoft

Conduit Power, a developer specializing in natural gas and battery power plants, partnered with SnapSoft to execute the first-ever launch of a Hitachi SCADA application on the AWS cloud platform. This initiative was designed to overcome the significant hurdles associated with traditional on-premises SCADA systems. Typically, these systems require substantial upfront hardware investments and a large, dedicated team of engineers to maintain physical infrastructure. By transitioning to a cloud-based model, Conduit Power sought to achieve the agility needed to meet an ambitious growth target of managing 300 megawatts of power within a three-year period. SnapSoft engineered a sophisticated virtual environment on AWS that successfully replicated the functionality of traditional hardware networks. The technical solution utilized a modern stack including Python, AWS Lambda, S3 buckets, and Terraform to build a resilient and automated infrastructure. This architecture facilitated seamless integration between Conduit Power, Hitachi, and various managed service providers. A central requirement of the project was ensuring high availability to satisfy regulatory mandates. By implementing duplicated availability zones and redundant servers, the team ensured that the system remained compliant with all operational continuity and backup standards. The move to AWS provided Conduit Power with unprecedented operational flexibility. During extreme weather events like major storms, the team could manage the SCADA application remotely. This capability ensured that operations continued without interruption, protecting both safety and revenue streams. From a financial perspective, the cloud-based approach allowed the company to avoid heavy capital expenditures and the ongoing costs of physical maintenance. These savings enabled Conduit Power to focus its resources on strategic growth and innovation. This successful deployment demonstrates the viability of cloud solutions for regulated entities in the energy sector. The final stack incorporates AWS Cloudwatch, CloudTrail, KMS, and EC2 to provide a secure, monitored, and scalable environment for critical power management tasks.

Key Takeaways

Virtualizing SCADA systems demonstrates that cloud infrastructure can satisfy the strict regulatory and safety requirements of the energy industry.
Shifting to the cloud changes the financial model of power management by replacing large capital investments with a flexible operational expense structure.
Remote operational capabilities act as a critical safety feature, allowing for continuous plant control during severe weather events that might otherwise disrupt site access.

SnapSoft and ziggiz: Accelerating Enterprise Data Management with AWS - SnapSoft

ziggiz, a semantic data platform provider, partnered with SnapSoft to migrate its enterprise data management infrastructure from Azure to AWS. This strategic move aimed to support rapid growth within the healthcare and government sectors by establishing a scalable, automated environment. The migration involved transitioning containerized applications to Amazon ECS, moving Databricks analytics, and migrating SQL databases to Amazon RDS for PostgreSQL. A core component of the project was the implementation of an AWS Landing Zone using Infrastructure as Code (IaC) to ensure consistent governance and security across multiple client environments. This setup utilized AWS Organizations, CloudTrail, and Config to maintain a strong security posture. To address the need for high volume data processing, the solution integrated Amazon Kinesis, Firehose, and Managed Streaming for Apache Kafka (MSK). These services facilitate efficient data streaming and delivery for large scale healthcare organizations. For AI initiatives, ziggiz transitioned from Azure cognitive services to Amazon Bedrock, which aligns with their long term strategy for intelligent automation. A significant outcome was the creation of a robust CI/CD pipeline using GitHub Actions. This automation allows for the rapid provisioning of new client accounts and streamlined software updates for their Golang application. By moving to AWS, ziggiz achieved faster client onboarding and enhanced compliance for regulated industries like GovCloud. The project also included cost optimization exercises to ensure the infrastructure remains efficient as it scales. This foundation enables ziggiz to focus on product innovation rather than manual infrastructure management. The partnership successfully transformed a manual, time consuming process into a streamlined, multi tenant environment capable of handling complex data workloads for multinational clients.

Key Takeaways

The single decisive reason for this migration was the need for radical scalability in client onboarding. By automating the Landing Zone, ziggiz transformed a manual bottleneck into a repeatable process that directly supports their aggressive growth targets.
This project demonstrates the ERA framework (Efficiency, Reliability, Authority) in action. The automation drives efficiency, the AWS infrastructure ensures reliability for healthcare data, and the compliance-first approach establishes authority in regulated markets.
Strategic AI positioning is achieved through the integration of Amazon Bedrock. This move suggests that ziggiz is preparing for a future where the platform does more than just store information; it proactively organizes and recalls data for enterprise users to reduce their mental workload and eliminate manual retrieval friction.

WebLytics, remote monitoring and diagnostics software for collaborative robotics applications - SnapSoft

WebLytics represents OnRobot's first major software-only solution, developed in partnership with SnapSoft to provide remote monitoring and diagnostics for collaborative robot applications. As a One Stop Shop for robotics tools, OnRobot designed this platform to target small and medium-sized enterprises (SMEs) by offering an affordable way to implement smart factory technology without the typical complexity or high costs. By gathering real-time data from both robots and tools, the software transforms raw equipment information into visualized intelligence at the device and application levels. This allows operators to track overall equipment efficiency (OEE), monitor running time versus downtime, and receive alerts for preventive maintenance. The system is designed for local or virtual network deployment with a built-in web server for secure remote access via HTTPS. The user interface includes four distinct views: Factory View for high-level monitoring, Application View for OEE metrics, Device View for health and utilization, and a Custom View for user-defined parameters. SnapSoft contributed significantly to the core development team, focusing on frontend responsiveness and backend infrastructure. Their work included optimizing page load speeds, developing localization logic for various languages, and building a microservices communication framework. A critical component was the creation of a cloud-based license server to support the SaaS business model, which also serves as a customer management tool and sales asset. The backend was engineered to handle high-volume data storage and processing, ensuring that statistics are generated and displayed in real-time even under heavy loads. This collaboration enabled OnRobot to launch a high-quality, plug-and-play product that integrates seamlessly with a wide variety of hardware and software solutions, effectively removing traditional barriers to automation adoption.

Key Takeaways

WebLytics reduces the complexity of managing multiple automation tools, effectively lowering the orchestration tax for factory managers by providing a single point of visibility for all collaborative applications.
The transition to a SaaS model for robotics hardware companies requires a strategic focus on licensing infrastructure and real-time data processing to maintain value-driven customer relationships.
By focusing on Overall Equipment Efficiency (OEE) as the single decisive reason for adoption, the software provides immediate, quantifiable ROI for SMEs that previously lacked access to sophisticated factory analytics.

Building specialized CMS for EdTech: How SnapSoft developed a complex lesson data management system for CAPIT using AWS, NodeJS and React - SnapSoft

CAPIT Learning partnered with SnapSoft to solve a critical bottleneck in their educational platform. Their existing system required direct database modifications by engineers whenever content needed updates. This rigidity prevented the team from adding new lessons or modifying existing audio and visual assets. SnapSoft built a specialized Content Management System using a modern stack of React, NodeJS, and AWS. This new system allows non-technical staff to manage complex educational data structures, including sounds, letters, and spelling rules. The architecture utilizes an NX monorepo to ensure frontend and backend compatibility during updates. Key features include a Lesson Builder and Exercise Builder that integrate with Flagsmith for A/B testing and controlled beta releases. This allows CAPIT to test new curriculum levels with specific user groups before a full launch. The solution also includes a Curriculum Dashboard and a Student Dashboard for tracking progress. By moving to this cloud-native CMS, CAPIT successfully launched their Level 4 curriculum and significantly reduced their reliance on engineering support for daily operations. The project also improved system stability through automated testing and comprehensive documentation. The technical implementation focused on creating a completely new system from scratch to separate workloads from the original application. This separation ensured that daily user activities remained unaffected while the new CMS backend, built on the Fastify framework, handled content management. To maintain high quality, SnapSoft implemented a robust CI/CD pipeline that runs system-wide checks on every code change. This approach ensures that the frontend remains compatible with the backend at all times. The use of Terraform and Terragrunt allowed for easy replication of environments, making the transition from development to production seamless. Ultimately, the project delivered a scalable solution that minimizes operational costs while providing CAPIT with the flexibility to iterate on their curriculum based on real-time user feedback.

Key Takeaways

The transition from direct database edits to a user-friendly CMS represents a strategic shift from engineering-dependent to product-led operations.
Integrating A/B testing tools like Flagsmith directly into the CMS allows for data-driven curriculum development without requiring code changes.
Using a monorepo setup with automated CI/CD checks ensures that complex dependencies between lesson logic and the user interface remain stable during rapid updates.
The project demonstrates how separating content management workloads from the core application can improve system reliability and reduce maintenance costs.

Optimizing Logistics with Intelligent Scheduling and AWS-Backed Automation with Emerald Transformer - SnapSoft

Emerald Transformer, a national leader in decommissioning and refurbishing distribution transformers, transitioned from manual spreadsheet-based scheduling to an automated, cloud-native logistics system. The company faced significant operational hurdles due to its reliance on manual processes for coordinating pickups and deliveries across a national network including California, Arizona, Texas, Kansas, Ohio, Georgia, and Florida. These challenges included difficulty integrating contaminated versus non-contaminated equipment logistics and a lack of real-time tracking for routes and job progress. While the company maintained a robust ERP system, it lacked the dynamic scheduling capabilities required for multi-site coordination. SnapSoft developed a multi-phase solution leveraging AWS serverless infrastructure to modernize these operations. The technical architecture utilizes AWS Lambda for compute, RDS PostgreSQL for data storage, and AWS Batch to run a custom Python-based optimization engine. This engine generates optimized schedules by analyzing route matrices, time constraints, and specific trailer-driver configurations. To facilitate precise logistical planning, the system integrates distance and routing data from AWS Location Service. The user interface was built using React and AWS Amplify, providing dispatchers with a centralized dashboard to manage trailer assignments, upload jobs, and visualize schedules. Security was prioritized through the implementation of AWS Cognito, enabling Single Sign-On and two-factor authentication. The initial pilot program launched at the Florida facility, focusing on digitizing workflows and replacing legacy data inputs with structured ingestion processes. The resulting system supports two to five stops per driver daily with optimized assignments. Beyond immediate efficiency gains, the architecture is designed for multi-tenancy, allowing Emerald Transformer to scale the solution across its entire national footprint. This digital transformation provides dispatchers with the flexibility to manually adjust system-generated assignments while maintaining high levels of data accuracy and operational visibility.

Key Takeaways

The transition from ERP-adjacent manual processes to a dedicated cloud-native optimization engine highlights a common gap in legacy enterprise resource planning systems regarding dynamic, real-time logistics.
By using AWS Batch for the Python optimization engine, the solution separates heavy computational logic from the core application, ensuring that complex route calculations do not impact UI performance or system responsiveness.
The inclusion of multi-tenancy at the architectural level suggests a strategic move toward internal platform scaling, where the solution can be deployed as a standardized tool across diverse geographic service centers.
Integrating AWS Location Service directly into the scheduling engine demonstrates how specialized cloud APIs can replace manual distance calculations to improve route accuracy and driver efficiency.

Transforming AI Video Production: Newcast Migrates to AWS for Enhanced Performance and Scalability - SnapSoft

Newcast provides an AI-driven marketing platform that generates high-quality video content from product descriptions or e-commerce links. The company faced significant hurdles while operating on a different cloud provider, including performance bottlenecks and high costs associated with GPU-heavy inference workloads. Their existing infrastructure relied on standalone scripts and monolithic pipelines that could not handle bursty demand or scale effectively. To modernize their operations, Newcast partnered with SnapSoft to migrate their core infrastructure to Amazon Web Services (AWS). The migration followed the AWS Migration Acceleration Program (MAP), starting with a thorough assessment of existing workloads. This included analyzing inference tasks running on L4 GPUs, AI pipelines using Stability AI and LoRA, and video processing via FFMPEG. The transition focused on moving these workloads to GPU-optimized AWS instances, specifically the g6.12xlarge and g6.4xlarge types. By utilizing Amazon Elastic Kubernetes Service (EKS) paired with Karpenter, the team established an intelligent auto-scaling foundation that optimizes resource usage based on real-time demand. A critical component of the new architecture is the use of Infrastructure as Code (IaC) through Terraform. This approach ensures that the entire environment, including S3 buckets for model storage and ECR repositories for Docker images, is repeatable and consistent. The team also modernized the video processing workflow by replacing manual FFMPEG scripts with AWS Elemental MediaConvert. Automation was further enhanced by integrating GitHub Actions for CI/CD, allowing for faster deployment cycles and improved development velocity. The results of the migration include substantial performance gains in inference times and a more modular, decoupled architecture. Operational costs were optimized by right-sizing instances and leveraging AWS funding, which covered approximately $107,980 in engineering costs during the mobilization phase. This new foundation prepares Newcast for future growth and allows for easier experimentation with managed AI services like Amazon SageMaker.

Key Takeaways

Decoupling monolithic AI pipelines into a containerized EKS environment allows for granular scaling of GPU resources, which is essential for managing the high costs of model inference.
Implementing Infrastructure as Code through Terraform transforms infrastructure from a manual bottleneck into a repeatable asset that supports rapid GTM experimentation.
Leveraging managed services like AWS Elemental MediaConvert allows AI startups to offload non-core technical tasks and focus resources on proprietary model development.

Tartan CargoSmart Integration - SnapSoft

Tartan, a US-based data analytics firm, specializes in providing port-to-port container visibility on a global scale. The primary challenge addressed in this project was the significant inaccuracy of estimated time of arrival (ETA) data for cross-ocean shipments, which often deviated by several days. Existing data streams lacked the reliability needed for customers to make informed inquiries or logistics adjustments. To resolve these inaccuracies, SnapSoft implemented a robust cloud architecture on AWS. The core of the solution involves an Elastic Container Service (ECS) deployment designed to pull data from external providers. This system utilizes autoscaling to manage fluctuating loads based on the volume of in-transit containers. The architecture is divided into independent services: data fetching for external source integration, a data analyzer for calculating ETAs, and front-end services for user interaction via the TartanHub portal. The integration of the CargoSmart API was a pivotal component of the solution. CargoSmart provides direct access to GPS coordinates, shipping events, and vessel names from shipping companies. This integration allowed Tartan to expand its carrier coverage from 4 to 20 carriers. For data management, the system uses DynamoDB, a serverless database that scales automatically to handle unpredictable usage patterns. Additional AWS tools include S3 for file storage, Cognito for secure user management, and CloudFormation for infrastructure as code. The results include a substantial increase in data volume, which directly feeds Tartan's predictive algorithms. By leveraging historical data from completed shipments, the system continuously improves the precision of its ETA forecasts. This scalability ensures that Tartan can track tens of thousands of containers daily while providing reliable, easy-to-consume tracking services to carriers and their customers worldwide.

Key Takeaways

Data diversity is the primary driver for predictive accuracy in logistics. By expanding carrier integrations fivefold, Tartan moved from limited visibility to a comprehensive dataset that powers more precise machine learning models for ETA forecasting.
Serverless and containerized architectures solve the unpredictable load problem inherent in global logistics. Using DynamoDB and ECS allows the system to scale costs and performance in direct proportion to the number of active shipments without manual intervention.
The transition from raw data fetching to an analyzer service layer represents a shift from simple tracking to proactive intelligence. This allows the company to transform fragmented carrier events into a unified, actionable timeline for the end user.

Bradford Tax Institute innovates their customer experience by launching an AI-Driven Search Tool - SnapSoft

Bradford Tax Institute, operated by Bradford Company Publishing, Inc., partnered with SnapSoft to modernize its digital presence through the implementation of a sophisticated AI-driven search tool. The primary challenge centered on a massive repository containing more than 2,300 specialized tax strategy articles. While the content was highly valuable, the existing keyword-based search functionality was insufficient for the needs of tax professionals and business owners. Users frequently encountered overwhelming lists of irrelevant results when searching for specific, situation-based answers, leading to significant friction in the user experience. To address these issues, SnapSoft developed a conversational chatbot utilizing Amazon SageMaker and advanced Natural Language Processing (NLP) capabilities. This solution allows users to input natural language questions regarding complex tax scenarios. The AI interprets these queries and provides accurate, context-aware responses that are directly linked to the relevant articles within the Bradford library. Technically, the tool was integrated using a robust client-server API system to ensure seamless connectivity and efficient data retrieval from the institute's existing technical systems. The tool serves a dual strategic purpose within the company's business model. For active subscribers, it functions as a high-efficiency navigation layer that delivers targeted, actionable insights without the need for manual filtering. For potential customers, the chatbot is accessible outside the paywall, providing brief answers and then prompting users to subscribe for full access. This integration with subscription management effectively transforms the search interface into a lead generation and sales tool. The project was executed ahead of schedule, resulting in immediate improvements in site engagement and customer satisfaction. Future iterations are expected to focus on further refining response relevance and increasing processing speeds to maintain a high standard of content accessibility.

Key Takeaways

Conversational AI transforms static content libraries into interactive knowledge bases, solving the discovery problem inherent in large-scale B2B repositories.
Integrating AI search with subscription paywalls creates a powerful middle-of-the-funnel conversion point by demonstrating value before requiring a full commitment.
The use of Amazon SageMaker for NLP allows for a scalable architecture that can handle complex, situation-based queries that standard search engines typically fail to process.

Meta Carbon’s Cloud Migration: Enhancing Scalability and Security with AWS - SnapSoft

Meta Carbon, a digital infrastructure firm, successfully moved its operations from a scattered multi-cloud environment to a unified AWS setup. Before this change, the company managed resources across AWS, GCP, and Digital Ocean. This fragmented approach created high management burdens, unpredictable monthly costs, and significant security gaps. SnapSoft created a structured plan to focus on growth and automation, ensuring the new environment could scale without increasing manual work. The technical solution used AWS Organizations to manage multiple accounts under one umbrella, which simplified governance. A suite of security tools, including GuardDuty, Security Hub, and IAM, was integrated to provide enterprise-grade protection and monitoring. To fix slow deployment processes and manual errors, the team used automation tools like Terraform and CloudFormation to implement Infrastructure-as-Code. This means the entire environment can be managed through code rather than manual clicks. The new setup uses Amazon EC2 and EKS for computing needs, along with Amazon Aurora Serverless v2 for database management, which allows the system to adjust resources based on demand automatically. Other tools like Amazon Route 53, AWS Config, and AWS CloudTrail were used to handle DNS, compliance, and auditing. This move cut infrastructure costs by an estimated 30% and created a central security system that meets modern compliance standards. The project shows how bringing cloud services together and using automation can make business operations smoother, more predictable, and ready for future expansion. By moving away from a complex multi-cloud strategy, Meta Carbon reduced its operational overhead and improved its ability to deploy new features quickly. This case study highlights the importance of centralized cloud management for technology companies looking to optimize their digital foundations. The absence of centralized security policies and monitoring mechanisms had previously heightened compliance risks and data protection concerns for the firm. Additionally, maintaining multiple cloud providers resulted in inefficient resource utilization because teams had to learn and manage different interfaces and billing models. The migration addressed these deployment inefficiencies and scalability limitations directly. The outcome provided a scalable and secure foundation that allows the infrastructure to run more efficiently while supporting future growth and potential CI/CD pipeline integrations.

Key Takeaways

Consolidating a scattered multi-cloud setup into one environment can lead to major savings, such as the 30% cost reduction achieved in this case.
Using automation for infrastructure is vital for companies to remove manual errors and speed up their internal processes.
Centralized management through AWS Organizations provides better security and oversight than trying to track policies across different cloud providers.

Reimagining user experience for Lightware through SnapSoft's strategic approach - SnapSoft

Lightware, a global manufacturer of precision video signal management products, partnered with SnapSoft to overhaul its digital presence and product strategy. The primary challenges included a slow, on-premise server infrastructure that hindered global performance, an outdated CMS that restricted multilingual content management, and a complex product portfolio that made navigation difficult for users. Additionally, Lightware lacked direct access to customer data and regional pricing visibility, which created friction in the sales process. SnapSoft utilized a design thinking methodology based on the double-diamond framework to address these issues. This involved a three-month discovery phase consisting of nine specialized workshops with different departments. The team identified core pain points using Google Analytics data, defined specific user personas, and established a Product Market Fit Pyramid to ensure the new site met market demands. A significant focus was placed on user journey mapping to align technical features with business objectives, resulting in a proposal to modernize the website infrastructure. The resulting strategy transitioned Lightware to a serverless AWS architecture to ensure global scalability and speed. The technology stack features Next.js, AWS Amplify, and a self-hosted Strapi headless CMS with custom implementation. Key functional improvements for customers include an advanced search functionality, a product wizard, and a partner portal that provides access to regional pricing and project management tools. For Lightware's internal team, the solution offers automated customer inquiries and faster support through Salesforce integration. The project delivered a clarified roadmap, clickable prototypes for usability testing, and a technical proof of concept. This holistic approach ensured that the new digital platform not only improved SEO and user experience but also streamlined internal marketing and sales operations. By involving Lightware experts throughout the iterative consultation process, SnapSoft achieved a robust, user-friendly, and globally accessible website aligned with the company's business growth and customer needs.

Key Takeaways

Transitioning from on-premise servers to a serverless AWS architecture is critical for B2B manufacturers with a global footprint to eliminate regional latency and improve SEO performance.
The use of a headless CMS like Strapi combined with modern frameworks like Next.js allows for more flexible multilingual content management compared to traditional monolithic platforms.
Integrating deep discovery workshops across multiple departments ensures that the digital product roadmap addresses both external customer friction and internal operational bottlenecks like manual inquiry handling.

Website modernization and product information system implementation with AWS - SnapSoft

**Lightware**, a global leader in precision video signal management, partnered with **SnapSoft** to execute a comprehensive digital transformation. The project addressed critical bottlenecks including slow site performance, an outdated content management system, and the inability to support a global presence from a localized server farm. By migrating to a well-architected **AWS** environment, the team implemented a headless architecture that separates content delivery from product data management. This strategic move was designed to eliminate the friction users faced when trying to retrieve technical specifications and pricing across different regions. The technical core of the solution features **Strapi CMS** for marketing content and **Pimcore** for product information management, both running on **AWS Elastic Container Service**. This dual-system approach ensures that complex product data remains synchronized across global locales while allowing marketing teams to update site content independently. The frontend utilizes **Next.js** with on-demand **Static Site Generation**, which serves pre-built files via **Amazon CloudFront** for maximum speed. This architecture allows for instantaneous content updates without the performance penalties typically associated with traditional database-driven websites. A significant highlight of the modernization is the integration of a custom search engine powered by **Apache Solr**. This system allows for advanced filtering and near-instant results, which is essential for Lightware's technical audience looking for specific user guides or firmware. Security and reliability are maintained through a **Web Application Firewall** and a multi-account **AWS Landing Zone**, providing a robust foundation for future growth. The business outcomes were substantial. Search result delivery time dropped from 1.2 seconds to under 250 milliseconds. Average page load times were reduced from 4.8 seconds to just 1.1 seconds. Beyond performance, the transition to a pay-as-you-go AWS model and static architecture resulted in a 35 percent reduction in monthly infrastructure costs. This modernization provides Lightware with a scalable foundation to support its 18 global offices and diverse customer base across industries like medical, defense, and live events, effectively turning their digital presence into a high-performance asset.

Key Takeaways

Decoupling product data from marketing content through a PIM and CMS integration allows B2B enterprises to scale technical documentation without slowing down marketing agility.
Moving from on-premise servers to a multi-region AWS infrastructure solves the performance latency tax for global companies, directly improving SEO and user retention.
On-demand Static Site Generation represents a strategic middle ground for high-performance websites, offering the speed of static files with the ability to publish content changes instantaneously.
Advanced search functionality is a primary driver of B2B user satisfaction, as reducing search time from seconds to milliseconds significantly lowers the friction for engineers seeking technical specifications.

Optimizing the game architecture and mobile framework of CAPIT Reading through migration - SnapSoft

SnapSoft partnered with CAPIT, a US-based EdTech startup, to modernize their reading application for children. The primary objective was to replace an outdated Haxe framework with Pixi.js to improve game performance and scalability. The project addressed several critical bottlenecks, including slow image rendering and the inability to deploy updates to centrally managed school devices. By migrating the mobile framework from Cordova to Capacitor, the team implemented over-the-air (OTA) updates. This allows the application to download the latest version seamlessly upon startup, ensuring all users have access to bug fixes and new content without manual intervention. To support segmented pricing and beta testing, SnapSoft integrated Flagsmith for feature flagging. This enables CAPIT to release specific content, such as Level 4 lessons, to dedicated user groups or districts without a full public rollout. The technical overhaul also included a transition to modern build tools like Webpack and Babel, alongside the implementation of Google Analytics and Sentry for better visibility into user behavior and system errors. The new architecture utilizes SVG-based configurations for clearer images and higher frame rates, significantly enhancing the student experience. The migration was handled incrementally, allowing Haxe and Pixi.js to coexist during the transition to ensure stability. This strategic modernization prepared the platform for future content expansion while resolving over 10,000 linter issues and optimizing the CI/CD pipeline for faster, more secure releases.

Key Takeaways

Incremental migration strategies allow for the modernization of large-scale legacy applications without the risk of a total failure. By using feature flags to toggle between old Haxe exercises and new Pixi.js implementations, the team maintained service continuity while upgrading the core engine.
Over-the-air (OTA) updates are a critical strategic asset for EdTech companies operating in institutional environments like schools. Since school devices are often locked down or centrally managed, bypassing the manual app store update process ensures that critical educational content and security patches reach students immediately.
Integrating feature management tools like Flagsmith directly into the GTM strategy enables non-technical teams to manage beta tests and tiered pricing models. This reduces the technical burden on developers by allowing product managers to control which districts or user segments see specific curriculum levels.

Generative AI in bank: How MBH Bank Leverages GenAI to help its review processes - SnapSoft

MBH Bank, a leading financial group in Hungary, has prioritized innovation and sustainability by modernizing its operations through cloud technology. To enhance its software development practices, the bank collaborated with SnapSoft to create a custom generative AI-based static code analyzer. This solution is built on AWS Bedrock and specifically utilizes the Claude model to provide automated linting and analysis for code as it is pushed to repositories. Unlike traditional static analysis tools that rely on rigid rules, this GenAI-powered system identifies nuanced issues and logic errors that were previously difficult to detect, thereby streamlining the code review process and boosting overall efficiency. The implementation journey involved several technical hurdles. SnapSoft conducted extensive research to select the most effective AI models and focused heavily on prompt engineering to ensure the feedback provided to developers was both accurate and context-aware. A primary concern was mitigating hallucinations, where the AI might generate incorrect or irrelevant suggestions. To ensure the tool was practical for daily use, it had to support multiple programming languages and deliver high-speed performance to avoid slowing down the development workflow. SnapSoft developed the application framework from scratch, tailoring it to MBH Bank's specific requirements. The team trained and tested the AI using large volumes of existing code and refined the tool through iterative cycles based on direct feedback from the bank's developers. The final product was integrated directly into MBH Bank's GitLab CI/CD pipelines, allowing for seamless automation. AWS was chosen as the underlying platform because it integrated perfectly with the bank's existing cloud environment and provided the necessary scalability and security. By enforcing strict policies within predefined AWS accounts, the solution met the bank's rigorous compliance standards while providing a cutting-edge tool for its development teams.

Key Takeaways

Generative AI provides a layer of semantic understanding in code reviews that traditional rule-based static analysis tools cannot achieve.
Successful AI implementation in highly regulated sectors like banking requires tight integration with existing security policies and cloud infrastructure.
The transition from manual to automated AI-driven reviews depends heavily on iterative refinement based on direct developer feedback to ensure accuracy.

Transforming Integration Challenges into Strategic Success: SnapSoft's Swift Resolution for SA onDEMAND on AWS Marketplace - SnapSoft

SA onDEMAND, a SaaS platform designed to connect partners with major cloud providers, encountered substantial technical roadblocks during their attempt to integrate with the AWS Marketplace. The core objective of the project was to implement **consolidated billing**, a critical feature for streamlining financial operations and providing a seamless purchasing experience for their enterprise clients. However, the internal team faced a series of persistent challenges that halted progress for over two months. These difficulties primarily stemmed from incomplete and ambiguous technical documentation, coupled with restricted access to the high-level support resources required to navigate the complexities of the AWS ecosystem. Recognizing the need for specialized intervention, SA onDEMAND engaged **SnapSoft** to leverage their deep AWS expertise. The SnapSoft delivery team immediately focused on identifying the specific documentation gaps and technical bottlenecks that had previously hindered the integration. By applying their extensive experience with cloud marketplace architectures, SnapSoft was able to bypass the technical friction that often plagues complex software integrations. They maintained a high level of proactive communication with the SA onDEMAND leadership, ensuring that every technical hurdle was addressed with clarity and speed. The results were immediate and impactful. SnapSoft successfully completed the integration within a **two-week window**, a timeframe significantly shorter than the two months of prior unsuccessful internal efforts. This swift resolution allowed SA onDEMAND to finally offer consolidated billing, which simplified their financial reporting and enhanced their operational efficiency. Beyond the immediate technical fix, the successful AWS Marketplace presence serves as a strategic growth lever. It provides SA onDEMAND with a credible platform to reach new enterprise customers and forge stronger partnerships within the cloud provider network. This case study highlights the value of expert technical support in overcoming integration friction and accelerating a company's go-to-market strategy.

Key Takeaways

Cloud marketplace integrations often fail due to a **documentation gap** where standard guides do not address specific platform complexities, necessitating specialized external knowledge.
The dramatic reduction in project time from two months to two weeks demonstrates the significant ROI of hiring experts to handle the complex technical overhead inherent in cloud environments.
Enabling **consolidated billing** via AWS is a strategic move that reduces procurement friction for enterprise customers and significantly accelerates the sales cycle.

Accelerating SkillShow’s Editing Workflow Using GenAI and AWS - SnapSoft

SkillShow, a leader in youth sports video production, faced significant operational bottlenecks due to manual video editing processes. With over 700 outsourced contractors and 230 terabytes of annual video data, the company struggled with high costs and a three week turnaround time for recruiting videos. These challenges forced a temporary halt in pursuing new events in mid 2023. To resolve this, SkillShow partnered with SnapSoft to build an automated video processing pipeline on AWS. The solution utilizes audio logging and automated clip generation rather than facial recognition, as it proved more cost effective and scalable. The workflow begins with video storage in Amazon S3. AWS Lambda orchestrates the serverless compute tasks, triggering Amazon Transcribe to convert audio into text. This transcription allows the system to identify specific players through audio callouts in noisy sports environments. The pipeline then automatically splits long videos based on player presence and stitches clips together to create highlight reels. This approach successfully navigated technical hurdles such as memory limitations in serverless functions and matching disparate video formats from various cameras. The implementation delivered transformative results. Turnaround time for video production dropped from three weeks to just 24 hours. By reducing reliance on manual contractors, SkillShow achieved substantial cost savings and regained the ability to scale operations for more events. The system also improved accuracy in player identification and standardized video formats. Future developments include exploring Amazon Bedrock for advanced generative AI capabilities, real time processing for live events, and deeper integration with sports leagues.

Key Takeaways

Audio logging serves as a more robust and cost effective alternative to facial recognition for player identification in complex, high volume video environments.
Transitioning from a contractor heavy manual model to a serverless automated pipeline can reduce production cycles by over 95 percent, moving from weeks to hours.
Infrastructure as code via AWS CDK ensures that high volume data processing workflows remain repeatable and scalable as business demand grows.
Automating the stitching process for highlight reels allows for personalized content delivery at scale, which was previously impossible under manual constraints.

Revolutionizing AI Monitoring: Okahu and SnapSoft's AWS Chatbot Deployment - SnapSoft

Okahu, an AI observability specialist, collaborated with SnapSoft to integrate its monitoring services into the AWS ecosystem. This partnership focused on enhancing performance tracking and cost efficiency for generative AI applications. The project delivered reference implementations for chatbot services using AWS Bedrock and Amazon SageMaker. By leveraging Bedrock, a serverless platform, the team refactored Okahu chatbot service to optimize performance monitoring. For SageMaker, the focus was on hosting a chatbot application trained on Git repository data, deployed via Elastic Beanstalk. The technical architecture utilized a serverless approach supporting Titan-based embedding and the OpenSearch vector database. Users can select Large Language Models from the Bedrock repository. For traceability, the system instruments application code to monitor API calls and service utilization, including token usage and GPU or CPU metrics, through CloudWatch. This setup allows for proactive monitoring of reliability and cost-effectiveness. SnapSoft implemented the solution using Infrastructure as Code to ensure reproducibility and clear documentation. This approach minimized configuration errors and simplified future updates. Additionally, SnapSoft secured AWS funding to offset project costs. The engagement included multiple onboarding sessions and continuous communication within Okahu Amazon environment. The four-week project resulted in Okahu successfully launching its offerings on the AWS Marketplace. The outcomes included improved deployment processes based on well-architected frameworks and reduced operational costs. The reference implementations provided Okahu with a clear roadmap for future cloud-based AI deployments, allowing them to make informed decisions based on specific application requirements and performance needs. This collaboration established a foundation for Okahu to scale its AI Observability as a Service model within the cloud.

Key Takeaways

The shift to serverless architectures like AWS Bedrock allows AI companies to focus on observability and performance without the overhead of managing underlying infrastructure.
Using Infrastructure as Code is critical for AI startups to maintain consistency across complex cloud environments and ensure rapid, error-free scaling.
Securing cloud provider funding can be a strategic lever for B2B SaaS companies to accelerate their go-to-market timeline on major marketplaces.
Effective AI monitoring requires deep instrumentation of API calls and token usage to balance high performance with cloud cost management.

Enhancing Developer-Centric Document Automation: A SnapSoft Success Story with Sensible - SnapSoft

Sensible, a developer-focused SaaS company specializing in document process automation, collaborated with SnapSoft to refine its AWS architecture and backend functionality. While Sensible had established a functional system on AWS, the engineering team required deeper expertise in cloud best practices to prevent performance bottlenecks and manage costs effectively as they scaled. A primary technical hurdle involved the integration of server-side events (SSE) into a backend environment powered exclusively by AWS Lambda. Because Lambda functions are ephemeral and lack a persistent internal network, implementing real-time communication through server-side events required a specialized architectural approach that differed from traditional server-based models. This specific challenge was critical because Sensible provides tools for developers who expect seamless, real-time document processing capabilities. SnapSoft addressed these needs through targeted architectural design consulting. They developed a comprehensive sample repository that served as a proof of concept for server-side events integration within a serverless framework. This solution allowed Sensible to maintain its preferred serverless architecture while enabling the necessary backend communication features required for a modern developer experience. The engagement provided Sensible's leadership, including Co-Founder and CEO Josh Lewis, with a strategic roadmap to avoid common AWS pitfalls related to performance, cost, and maintainability. By focusing on a proof of concept rather than just theoretical advice, SnapSoft ensured the Sensible team could immediately apply these insights to their production environment. The partnership resulted in a streamlined path for Sensible to enhance its platform as it scales toward upmarket customers. By adopting the best practices demonstrated in the SnapSoft repository, Sensible reduced engineering overhead and improved the long-term maintainability of its infrastructure. This collaboration highlights the importance of expert architectural guidance for SaaS companies looking to balance rapid feature development with long-term system stability. The outcome ensured that Sensible could confidently expand its platform's capabilities without compromising the clean, serverless design that defines its technical identity.

Key Takeaways

Bridging the gap between functional infrastructure and AWS best practices is critical for SaaS companies moving upmarket to ensure cost-efficiency and performance.
Implementing persistent communication patterns like server-side events in an ephemeral AWS Lambda environment requires a specific architectural shift to maintain a serverless footprint.
Providing a tangible proof of concept through a sample repository is often more effective for developer-centric organizations than theoretical consulting alone.
Strategic architectural interventions early in a company's growth phase prevent technical debt that could otherwise hinder product-led growth and enterprise adoption.

FintechX leverages SnapSoft for rapid migration to AWS - SnapSoft

FintechX, a digital banking platform, successfully transitioned its entire infrastructure from a previous cloud provider to Amazon Web Services (AWS) in just two weeks. This rapid migration, facilitated by SnapSoft, focused on moving away from basic virtual machine setups toward a sophisticated, secure, and scalable architecture. The primary drivers for this shift included the need for advanced networking capabilities, enhanced security protocols, and stricter compliance standards required for financial services. The solution centered on a robust AWS Organizations structure designed to ensure complete resource isolation. SnapSoft implemented a multi-account strategy that separated core functions into dedicated environments. This included a Management Account for billing and Service Control Policies, a Network Account for centralized VPN and Transit Gateway management, and a Security Account for monitoring tools like GuardDuty and Security Hub. Additionally, a Logging Account was established to centralize application telemetry, while separate Organizational Units (OUs) were created for internal DevOps and individual client systems. This architecture ensures that each partner operates in a fully isolated environment, which is critical for maintaining security in a fintech context. A key component of the migration was the introduction of Infrastructure as Code (IaC) practices. While FintechX prefers to build and manage its custom, self-hosted application stack from scratch, SnapSoft provided the foundational virtual machine setups and networking layers. FintechX maintains control over its CI/CD processes, utilizing a self-hosted Git repository and custom Amazon Machine Images (AMIs) to deploy software across partner instances. The new environment leverages Application Load Balancers and Web Application Firewalls (WAF) to protect public-facing instances, while private subnets and NAT gateways manage outbound traffic. This setup not only improved operational efficiency but also empowered the internal FintechX team to manage deployments independently. The project concluded with a highly optimized infrastructure that reduces costs and provides a clear path for future serverless exploration.

Key Takeaways

Rapid cloud migrations in highly regulated sectors like fintech are achievable through a modular multi-account strategy that prioritizes security and compliance from day one.
Implementing Infrastructure as Code (IaC) allows for a clean hand-off between external consultants and internal teams, enabling the client to maintain long-term operational independence.
Resource isolation via AWS Organizations and Transit Gateway provides a scalable blueprint for SaaS providers who need to host distinct environments for multiple enterprise partners.
Moving from generic virtual machines to a structured cloud environment significantly reduces the operational burden by automating security monitoring and centralizing network management.

Fruccola bird automated restaurants - SnapSoft

Fruccola Bird is a Hungarian smart restaurant startup that aims to provide healthy, fresh meals through automated technology. Backed by the established Fruccola restaurant and VC firm SZTA, the company developed a high-tech solution specifically designed for office buildings. The system allows customers to bypass traditional delivery wait times by using automated restaurant units that prepare hot meals within 25 seconds of an order being completed. Users interact with the service through native iOS and Android applications or onsite kiosks. The mobile app includes advanced features such as a food calendar and detailed nutritional information, allowing users to build pre-orders based on their dietary needs. SnapSoft developed the underlying modular system using a microservices architecture to ensure scalability and reliability. The core components include an Admin page for managing stock and menus, a Sales module for order processing, and a Vending module for hardware communication. The technical stack utilizes Spring Boot and .NET Core for the backend, while the Admin frontend is built with Angular 8+. The infrastructure is hosted on AWS, leveraging services like S3 for file storage, RDS for data persistence, and Route 53 for DNS management. A critical part of the implementation involved the Vending module, which communicates with physical machines via Amazon MQ. This setup allows the server to monitor machine status and send real-time notifications if errors occur. The development process followed an iterative approach, starting with iOS to validate user flows before expanding to Android. Integration testing was conducted in partnership with Hunify Labs to ensure the software and hardware worked together seamlessly. The final solution was validated by a technical auditor and launched with a live demo where users tested the end-to-end ordering and vending process. This project demonstrates how combining high-quality ingredients with modern cloud infrastructure and IoT can redefine the convenience of healthy eating in professional environments.

Key Takeaways

The 25-second fulfillment time represents a significant shift in food technology by moving production closer to the consumer and eliminating the need for traditional couriers.
Adopting a microservices architecture on AWS allows the platform to scale its operations across various office locations while maintaining independent control over payments, invoicing, and inventory.
The integration of Amazon MQ for hardware communication illustrates the necessity of robust messaging protocols when bridging cloud-based sales logic with physical IoT vending units.

D:PLOY - The next big leap in automation - SnapSoft

D:PLOY is an automated platform developed by OnRobot in collaboration with SnapSoft to simplify the building, running, and re-deploying of collaborative robot applications. It addresses the high barriers to entry in industrial automation by offering a solution that requires zero programming or simulations. Users can deploy complete applications directly on the manufacturing floor in just a few hours, representing a 90 percent reduction in deployment and re-deployment time compared to traditional methods. This breakthrough democratizes robotic technology, making it accessible to companies of all sizes by maintaining control over continuing costs and speeding up production changes. The platform automatically discovers nearly all installed hardware, including manipulator arms, grippers, sensors, and CNC machines. It features a user-friendly graphical interface where operators define workspace boundaries and cell borders. D:PLOY then automatically generates collision-free paths, program logic, signal exchanges, and event handling. The system supports four primary applications: palletizing, packaging, machine tending, and transferring. Users enter workpiece attributes and pick positions through a straightforward step-by-step flow, and the application logic is optimized automatically for a one-click start. SnapSoft contributed three full development teams to the project, totaling 18 engineers specializing in web applications and AWS cloud infrastructure. They were responsible for the web application's frontend and backend, as well as migrating the infrastructure to a scalable, high-available AWS environment. Technical challenges included creating an interactive 3D model simulation that remains resource-efficient on tablets and desktops while allowing for complex transformations via touch or cursor. The cloud component, based on the WebLytics functionality, allows for real-time monitoring and data visualization to help reduce downtime and increase productivity. The development process involved 14 months of iterative work, including in-person testing with actual robots to ensure the software could handle real-world calibration and on-the-fly changes.

Key Takeaways

D:PLOY applies a single decisive reason for adoption by focusing almost exclusively on the 90 percent reduction in deployment time, removing the primary friction point for robotic automation.
The platform shifts robotics from a high-touch service model to a product-led experience where the software handles the complex logic of path planning and signal exchange.
By integrating 3D simulations directly into a web interface, the solution eliminates the need for expensive offline simulation software, further lowering the total cost of ownership.
The transition to a cloud-based microservices architecture allows for proactive recall of performance metrics, enabling predictive maintenance and reducing the orchestration tax usually associated with managing multiple robot cells.

Customer Success Stories - SnapSoft

SnapSoft specializes in delivering complex cloud and AI solutions, with a heavy emphasis on the AWS ecosystem across various global regions. Their portfolio demonstrates a high level of proficiency in executing rapid cloud migrations, such as transitioning FintechX to AWS in only two weeks and moving Kane Solutions from IBM Cloud to a modern containerized architecture. A core part of their service offering involves the integration of Generative AI and machine learning to solve specific business problems. This includes building conversational agents for The Trucker Media Group using Amazon Bedrock and developing text-to-SQL functionalities for Deltia.ai to improve data accessibility on manufacturing floors. They also developed a proof of concept for Meso AI using SageMaker and YOLO inference for social media image analysis. The company also focuses on infrastructure modernization through the implementation of AWS Landing Zones and Infrastructure as Code (IaC) using tools like Terraform and Terragrunt. These practices are evident in their work with MBH Bank and Elroi, where they established secure, multi-account environments to meet strict regulatory and privacy requirements. For industrial clients, SnapSoft has implemented advanced automation like D:PLOY, which allows for robotic application deployment in hours without programming. They also optimized logistics for Emerald Transformer by building an automated scheduling engine that processes real-time data to improve route planning. Beyond migration and AI, SnapSoft provides managed services and operational governance, helping clients like Bongo Media mitigate infrastructure risks through continuous monitoring and incident response. Their work in specialized sectors like EdTech with CAPIT Reading and Fintech with Moonfare highlights their ability to modernize legacy frameworks and improve user experiences through strategic product roadmaps and serverless architectures. By leveraging serverless technologies like AWS Lambda and Athena, they help companies like Volie and Sunme Solar process large datasets and calculate complex cost ranges efficiently.

Key Takeaways

Cloud migration serves as a critical entry point for companies to unlock advanced AI and machine learning capabilities.
SnapSoft prioritizes rigorous security and compliance frameworks, making them a preferred partner for highly regulated industries like banking and healthcare.
The transition from manual deployments to automated CI/CD pipelines and IaC is a primary driver for increasing developer efficiency and system scalability.

Frequently Asked Questions

Given that Newcast and ziggiz both executed major migrations from other cloud providers to AWS, how do their respective choices of container orchestration—Newcast leveraging Amazon EKS with Karpenter and ziggiz deploying container services onto Amazon ECS—reflect their distinct workloads, and what are the trade-offs in operational overhead and scalability for AI video production versus semantic data platforms?
Consider the different security constraints of FintechX (which required strict virtual machine isolation and created custom AMIs for partner instances) versus Droople (which isolated its PostgreSQL/Aurora database inside a private VPC with Lambda integration). How do these two approaches to isolation and infrastructure-as-code (IaC) resolve the tension between giving clients/developers operational freedom and maintaining strict regulatory compliance?
Both CAPIT Reading and Lightware underwent massive website and application modernization transitions (CAPIT migrating from Haxe to Pixi.js/Capacitor, and Lightware moving to a Next.js frontend with Strapi and Pimcore). How do their deployment strategies—specifically CAPIT's use of over-the-air (OTA) encrypted bundle updates and Lightware's on-demand Static Site Generation (SSG)—address the distinct challenges of centrally managed school devices versus a global, multilingual corporate product database?
Given that Okahu and Bradford Tax Institute both developed AI-driven chatbot and retrieval systems utilizing Amazon SageMaker, how does Okahu's approach to tracing and monitoring API calls and token utilization via AWS CloudWatch compare with the performance and subscription integration requirements of Bradford's customer-facing, paywalled search tool?
OnRobot's WebLytics and D:PLOY solutions both target collaborative robotics but serve different operational roles. How does the local-network and built-in web server model of WebLytics balance against the zero-programming, interactive 3D simulation, and AWS cloud-backed infrastructure of D:PLOY when trying to democratize automation for small and medium-sized enterprises (SMEs)?
Fruccola Bird's automated restaurant system relies on a Spring Boot-based Vending module communicating via Amazon MQ to physical onsite machines. In contrast, Conduit Power's Hitachi SCADA launch on AWS replicates physical hardware networks using Python, Lambda, and S3. What are the key differences in managing real-time physical device connectivity and operational continuity during network failures or emergencies between these two IoT/automation designs?
Both ziggiz and MBH Bank utilized AWS Bedrock to integrate generative AI into their operations (ziggiz for migrating cognitive service calls from Azure, and MBH Bank for building a custom static code analyzer using Claude). Given the security-sensitive nature of their industries (healthcare/government and fintech), how do their implementations address the challenges of data privacy, compliance, and model hallucination within their respective AWS environments?