Nextgensoft Technologies LLP

A leading company in the UK completely redefined its sales forecasting and operational efficiency through a custom AWS-powered machine learning solution I developed and implemented. The existing process suffered from low forecast accuracy, manual data handling, fragmented information sources, and an inability to scale with growing data demands. I designed and deployed a robust, end-to-end cloud architecture that automated the entire data lifecycle. Using AWS Glue, I built ETL pipelines to seamlessly extract, clean, and transform raw sales and market data from multiple channels. AWS Lambda functions enabled real-time event-driven processing, ensuring that the system reacted instantly to new data. I leveraged Amazon SageMaker to train, fine-tune, and deploy predictive models capable of accurately forecasting sales trends across diverse product categories. The results were integrated into interactive Power BI dashboards, delivering actionable insights directly to stakeholders. This solution not only boosted forecast accuracy and optimized inventory management but also reduced operational costs, accelerated decision-making, and provided a scalable foundation for future analytics initiatives.

For a major US shipping company, legacy technology was becoming an anchor. Quarterly release cycles, manual deployments, and a rigid monolithic ERP system were slowing progress, increasing downtime, and making it difficult to scale with business expansion. With growing operational demands, the company needed a faster, more resilient, and scalable approach.

The transformation began with a shift to agile delivery, reducing release cycles from months to just four weeks and using Jira to track progress and priorities. DevOps practices were embedded into the process, with Bamboo for continuous integration and Selenium for automated testing cutting manual effort and improving quality. The ERP system was re-architected into Spring Boot-based microservices running in containers, then migrated to AWS for elastic scalability and cost optimization.

The results were clear: faster rollout of new features, improved system resilience, greater visibility into quality, reduced downtime, and measurable cost savings from optimized cloud infrastructure. What was once a slow-moving system is now an agile, cloud-enabled platform ready to meet the demands of a growing shipping network.

Slow, manual processes were stalling software releases code merges caused conflicts, deployments were risky, and feedback loops lagged behind the pace of development. For one software team, these delays meant missed opportunities and increased production risks.

We introduced a fully automated CI/CD pipeline to transform their development lifecycle. Using Git for version control, the CI pipeline handled automated builds, unit tests, and integration testing to catch issues early. The CD pipeline took over from there, packaging and deploying code seamlessly across environments, ensuring consistency and reducing deployment risk.

The result was a faster, more reliable release process bugs were caught early, releases went out on schedule, risks were minimized, and feedback cycles tightened. With automation doing the heavy lifting, the team could focus on delivering features instead of fighting process bottlenecks.

Managing sprawling cloud environments had become a bottleneck manual provisioning, patching, and configuration were draining IT resources while slowing deployments. For one client running a custom cloud-based solution, this complexity led to downtime, inconsistent infrastructure designs, and difficulty maintaining security compliance at scale.

Our approach was to replace fragmented, manual processes with a standardized Infrastructure as Code strategy built on Terraform best practices. We began with a full assessment of their current architecture, identified inefficiencies, and created a migration strategy that minimized disruption. From IAM role creation to network reachability planning, CI/CD integration via GitHub Actions, and automated monitoring with alerts, every step was documented for repeatability. The DevOps team was trained on coding standards, branching strategies, and Terraform’s organizing conventions to ensure long-term sustainability.

The transformation delivered immediate results rapid environment creation, reduced human error, improved security posture, cost savings through optimized provisioning, and a scalable, consistent infrastructure ready to support business growth.

For a leading company in the US, data was everywhere but insights were nowhere. Operations generated vast volumes of information from multiple sources, yet silos, slow processing, and inconsistent quality prevented a unified view of the business. The company needed more than just storage it needed a complete, scalable, and real-time data ecosystem to guide decisions.

The solution brought every step of the data journey into focus: Azure Data Factory automated ETL pipelines, Azure Data Lake and SQL Database centralized and secured information, and Azure Databricks transformed raw data into actionable intelligence. Azure Synapse Analytics accelerated analysis, while Power BI turned numbers into interactive, real-time dashboards for leadership and operational teams alike.

The impact was immediate faster, more confident decision-making, agile responses to market shifts, deeper customer insights, and significant cost savings through cloud scalability. For this organization, data went from a fragmented challenge to a strategic asset powering every move.

In the high-stakes world of sports analytics, milliseconds matter. A leading analytics company found its Azure PaaS-based infrastructure straining under the weight of massive datasets, sluggish queries, and the rising costs of Azure Data Factory. Real-time insights crucial for game-day decisions were out of reach.

Enter a new playbook built on Apache Software Foundation technologies. Apache Airflow replaced ADF, bringing cost savings and Python-powered workflow automation. Apache Kafka streamed events in real time, while Apache Ignite’s in-memory HTAP database delivered query speeds up to 100x faster. Apache Spark (via Databricks) crunched large-scale aggregates, and Kubernetes with Azure API Gateway kept the whole system scalable and nimble.

The result? Real-time analytics that keep pace with the game, a dramatically leaner cost structure, and a data pipeline built to scale as fast as the action on the field.

For one of the world’s leading bitcoin payment processors, growth came with a price: too many containers to manage, scaling bottlenecks, and a deployment process that moved at the speed of a manual checklist. Their Docker Swarm setup simply couldn’t keep pace with rising transaction volumes or the security demands of handling cryptocurrency.

The solution? A Kubernetes-driven revamp. By introducing Ingress Controllers for smart traffic routing, Horizontal Pod Autoscaling for instant performance boosts, and Helm for frictionless rollouts, the team turned container chaos into an orchestrated symphony. Services were split across pods to localize faults, while Network and Kyverno Policies automated security enforcement from the inside out.

Today, scaling is effortless, deployments are push-button fast, and security is built into every microservice creating a crypto-ready platform that can handle tomorrow’s demand without breaking a sweat.

A leading data transformation company embarked on a strategic overhaul to address critical gaps in security, monitoring, and operational efficiency within their application ecosystem. Lacking visibility into performance, formal DevSecOps practices, and robust design documentation, the company faced escalating cloud costs, compliance risks, and heavy vendor dependency.

The transformation introduced end-to-end monitoring with Uptime Kuma, ELK Stack, and Sentry for real-time performance insights and proactive error detection. Security was embedded throughout the development lifecycle using Snyk for comprehensive code, library, and image scanning. Each feature implementation followed a rigorous evaluation and design process, supported by detailed documentation via GitHub Wiki and Readme.io. A dedicated DevSecOps team continuously optimized environments, while targeted Azure cost-saving techniques streamlined cloud expenditures.

This future ready architecture now delivers full performance visibility, reduced vendor reliance, enhanced security compliance, and significant cost efficiencies ensuring the platform is scalable, resilient, and primed for growth.

A leading data product company revolutionized user interaction by enabling natural language commands for their application and integrating its API with advanced LLMs starting with Claude Desktop. This dual approach allowed users to directly access and manipulate platform data within an AI-powered environment, benefiting from intelligent formatting and contextual responses.

The solution was architected on the open-source Model Context Protocol (MCP), ensuring maximum code reuse and unified implementation for both application interaction and AI agent automation. A REST layer atop the MCP client streamlined communication, while AWS Bedrock enabled flexible, future-proof model selection.

The result: a unified, cost-efficient, and scalable AI-driven architecture that accelerates deployment, reduces integration costs, and sets the stage for advanced automation capabilities.