01
GROUNDED_RETRIEVAL
Answers are anchored in approved business knowledge, with the system designed to favor traceable assistance over unsupported responses.
01 · Agentic AI / RAG
Semai AI Support case study.
A governed, retrieval-backed support system with human escalation—not a black-box chatbot.
AI Product Architect & Lead Engineer2026Built
Project overview
What was built and why it mattered.
A concise project view first, then the longer narrative and implementation notes below.
Semai turns approved business knowledge into a support layer that can answer routine customer questions while preserving a clear path to human ownership. The design goal was dependable, reviewable automation—not a novelty chatbot.
I designed the system across the agent runtime, API, and operational surfaces: retrieval and tool use for grounded answers, explicit escalation workflows for sensitive or unresolved issues, and a dashboard for reviewing conversations and maintaining knowledge.
The implementation combines Python, FastAPI, LangGraph/LangChain patterns, OpenAI-compatible models, and a React operator experience. The result demonstrates the architecture required to run AI support as an operational product.
Key systems
The pieces that carried the product.
This is the short implementation map before the fuller markdown write-up.
02
AGENT_WORKFLOWS
LangGraph-style orchestration connects intent handling, knowledge retrieval, tool execution, and controlled responses into one lifecycle.
03
HUMAN_ESCALATION
Escalation records, prioritization, and operator review keep the team accountable for complaints, edge cases, and outcomes that need judgement.
04
OPERATOR_EXPERIENCE
Conversation, knowledge, and analytics surfaces make the system maintainable for the people responsible for support quality.
Detailed notes
Long-form case study
The fuller project narrative, results, and architecture notes from the markdown source.
The brief
Semai was conceived as an AI support product for businesses that needed faster, more consistent customer assistance without surrendering control to an opaque chatbot. The real requirement was an operating layer: one that uses approved knowledge, captures context, knows when to stop, and gives a human team a practical way to improve it.
What I owned
I designed the product architecture across the agent, API, knowledge, and operator layers. That included the conversation lifecycle, retrieval and tool boundaries, escalation behavior, and the surfaces that let a business review conversations and maintain its knowledge base.
Architecture
Engineering decisions that matter
Grounded answers over fluent guesses
The system is structured around approved business knowledge and retrieval. The objective is not simply to make the assistant sound confident; it is to make its answers useful, bounded, and maintainable as business information changes.
Escalation is part of the product, not a fallback
Complaints, sensitive situations, and unresolved questions are captured as explicit escalation records with priority and context. This means the team can take ownership of the work instead of treating an unhelpful AI answer as the end of the interaction.
Operators need a feedback loop
The companion dashboard is designed for conversation review, knowledge maintenance, and analytics. An AI support system improves when operators can see where customers get stuck and refine the material that shapes future responses.
Implementation
- Python services and FastAPI for the API and integration boundary.
- LangGraph/LangChain patterns for agent orchestration and tool-aware workflows.
- OpenAI-compatible model access and retrieval-backed knowledge handling.
- React-based web and dashboard surfaces for customer interaction and operations.
- Structured escalation tooling for complaints and human follow-up.
What this case study proves
Semai shows how I approach production AI as a system-design problem: connect models to trustworthy context, give people control of exceptions, and build the operational feedback loop that keeps the product useful after launch.