Applied AI · Paid consulting
AI Customer Service & Commerce Integrations
Paid client work combining an OpenAI-powered customer service agent, RAG, and a custom C#/.NET MCP server with e-commerce and payment workflows.
≈80% less manual support work · Client-reported
Interactive walkthrough · Synthetic data
From customer question
to controlled action.
Follow a representative support request through retrieval, business tools, and human review. This simulation illustrates the documented design; it does not call a live AI service.
- 01Understand the requestCustomer → Agent
- 02Retrieve relevant contextRAG → Policy context
- 03Inspect the business resultC#/.NET MCP → Order tools
- 04Respond with the available evidenceAgent → Customer
Step 1 / 4
Understand the request
The customer asks where an order is. The workflow needs order context rather than a plausible shipping estimate.
“Where is my order DEMO-1042?”
Distinguish a factual lookup from a general policy question before choosing a business tool.
All names, order numbers, policies, and tool outputs above are invented examples. The client implementation and its reported outcome are described in the case study →.
Overview
Paid client work combining an OpenAI-powered customer service agent, RAG, and a custom C#/.NET MCP server with e-commerce and payment workflows.
Business Problem
Customer service teams needed to handle order, account, shipping, payment, and product questions with access to business-system context and appropriate controls for sensitive actions.
Constraints
Client systems and transaction volumes are private. Refund actions require human approval; sensitive information and generated responses need explicit policy controls.
My Role
Implemented the AI customer service agent, C#/.NET MCP integrations, human approval controls, and containerized deployment as an independent consultant.
Architecture
OpenAI API and RAG provide the answer workflow; a C#/.NET MCP server integrates business operations. The Docker-containerized AI solution runs on AWS ECS, with GitHub Actions automating builds and deployment. SQL Server supports payment transaction tracking and financial data management.
Technical Decisions
Connected the agent to order processing, customer profiles, shipping updates, payment methods, refunds, and product information through a custom C#/.NET MCP server. Added human-in-the-loop refund approval and guardrails for sensitive information and response reliability.
Implementation
Delivered paid Stripe and PayPal integrations using C# and ASP.NET Core REST APIs, the AI customer-service workflow, and an AI finance audit assistant for e-commerce payment and internal finance workflows. Independent multi-tenant SaaS development is a separate project.
Results
Approximately 80% reduction in manual customer service workload, based on client-reported operational data. Transaction volumes and internal financial data are not published.
Lessons / Tradeoffs
The agent can access operational context, while sensitive refund actions require human approval. Guardrails address sensitive-information disclosure and response reliability; they are controls rather than a guarantee that generated answers are always correct. The workload reduction is client-reported, not an independently audited benchmark.
Technology
C# / .NET MCP · OpenAI API · RAG · AWS ECS · Docker · GitHub Actions · ASP.NET Core · SQL Server · Stripe · PayPal