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I worked on Sand Bullet (Unity 3D) a top-down shooter indie title and owned the full development cycle from early concept to final deployment. I built the core tech including enemy AI, a flexible weapon system, and in-game power-ups, then pushed it through release on Steam, where it picked up real user traction and feedback that helped shape polish and iteration.
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Kubernetes on AWS: a managed EKS cluster serving a load-balanced web app, built and torn down cleanly.
eksctl provisions the EKS control plane and a managed worker node. Kubernetes manifests deploy NGINX with three replicas, and a LoadBalancer service puts it behind an AWS load balancer. I validated the pods, the service and the live page in a browser, then deleted the cluster so nothing kept billing.
For a small business, this is the step from "it runs on one server" to an app that can scale and recover, without paying for idle infrastructure. Code is on my GitHub: github.com/nujabesploo/aws-eks-production-platform
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OnPrem .NET to AWS Migration
Migrated an on‐premises .NET web application and SQL Server database to AWS to achieve scalability, high availability, and cross‐region resilience. The legacy environment ran on Windows servers with a clustered SQL Server, resulting in high maintenance and limited elasticity. The new AWS architecture features EC2 Auto Scaling behind an Application Load Balancer (ALB), Amazon RDS for SQL Server with Multi‐AZ and a cross‐region read replica, and DNS failover via Route 53.
◦ Deployed the .NET application on an EC2 Auto Scaling group (Windows AMI) across private subnets in us‐east‐1.
5- Build Scalable Real-Time Data Analytics on AWS for Instant Insights
Designed and implemented a real-time data analytics platform on AWS, enabling stream processing, transformation, and visualization for high-volume IoT and log data. This system was built to process and analyze millions of records per second, providing actionable insights for business decision-making.
The solution leveraged AWS native services like Kinesis, Glue, Lambda, and Redshift, integrating Apache Spark Structured Streaming to handle real-time data ingestion and transformation. Airflow and Step Functions were used for workflow automation.
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Your insurance team shouldn't spend 20 minutes searching documents for an answer that AI could help locate in seconds.
Insurance agencies manage hundreds of policy documents, coverage details, renewal procedures, and claims guidelines.
But when a customer asks a simple question, employees often have to search multiple PDFs, check internal records, and manually prepare a response.
What if your team could ask questions in plain English and instantly find relevant information from approved insurance documents?
The Business Problems
Insurance operations often face:
Policy information scattered across PDFs and shared folders.
Employees manually searching lengthy insurance documents.
Delayed responses to coverage and renewal questions.
Inconsistent answers across customer service teams.
Repetitive administrative questions about claims procedures.
Difficulty locating the latest approved policy information.
Limited integration between document systems and internal support tools.
The Solution: AWS-Powered Insurance Knowledge Chatbot
I can develop a secure AI knowledge assistant using Amazon Bedrock, AWS Lambda, Amazon S3, and Retrieval-Augmented Generation (RAG).
The chatbot can search approved insurance documentation, retrieve relevant passages, and generate answers grounded in those sources.
Unlike a generic chatbot, this solution can be designed around your organization's actual policy documents and internal procedures.
Core capabilities:
Natural-language insurance document search.
AI-powered policy information retrieval.
RAG-based answers with document citations.
Internal employee knowledge assistance.
Policy version management and access controls.
Escalation to licensed insurance professionals when needed.
Secure integration with existing insurance software.
How the AWS AI Workflow Works
Employee Question → API Gateway → AWS Lambda → Amazon Bedrock Knowledge Bases → Policy Retrieval → AI Answer → Response Validation → Employee
Step 1 — Document Storage
Approved policy documents, procedure manuals, and FAQs are stored in Amazon S3.
Step 2 — Knowledge Indexing
Amazon Bedrock Knowledge Bases processes and indexes approved documents for semantic retrieval.
Step 3 — Employee Question
An authorized employee asks a question through an internal chatbot.
Step 4 — Relevant Document Retrieval
The system retrieves relevant policy passages from the knowledge base.
Step 5 — AI Answer Generation
Amazon Bedrock generates an answer using the retrieved context, including source references when available.
Step 6 — Verification and Escalation
The application checks access permissions, handles missing evidence, and routes sensitive or uncertain cases for human review.
Step 7 — Monitoring
AWS CloudWatch tracks application errors, performance, and operational metrics.
Business Benefits
A well-designed insurance AI assistant can help:
Reduce repetitive document searches.
Improve access to approved policy information.
Support faster internal customer service workflows.
Make documentation easier for employees to navigate.
Reduce reliance on scattered knowledge sources.
Improve consistency in routine informational responses.
Keep important coverage decisions under human supervision.
Two Ways We Can Work Together
Already have insurance documents and chatbot requirements? → Let's design your AWS RAG chatbot and integrate it into your internal workflow.
Not sure whether your agency needs RAG, automation, or custom software? → Share your current document-handling process, and I'll help identify the most practical solution.
SEO Keywords
AWS AI Chatbot • Insurance AI Chatbot • Insurance Automation • Amazon Bedrock • AWS Lambda • Amazon S3 • AWS API Gateway • Insurance Document Processing • RAG Development • AI Knowledge Base • Policy Document Search • Insurance Technology • InsurTech Development • AI Agent Development • Enterprise AI Assistant • AWS Cloud Development • Generative AI Solutions • Insurance Workflow Automation • Semantic Search • Document Retrieval • LLM Integration • Python Backend Development • Custom AI Development • AI Document Assistant • API Integration
Result: A proposed AWS-powered insurance knowledge assistant designed to simplify policy document retrieval, support faster internal responses, and provide source-grounded information with human oversight.
#AWS #AmazonBedrock #AWSLambda #AmazonS3 #AmazonAPIGateway #AIChatbot #InsuranceAutomation #InsurTech #InsuranceTechnology #RAG #RAGChatbot #GenerativeAI #AIKnowledgeBase #DocumentAutomation #AIAgents #CloudDevelopment #EnterpriseAI #PythonDevelopment #LLMIntegration #WorkflowAutomation #SemanticSearch #AIChatbotDevelopment #CustomSoftwareDevelopment
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