Domain-Constrained Intelligent Chatbots with Symbolic Guardrails
Hallucination-resistant conversational engines for healthcare triage and high-volume e-commerce catalogs with 100% catalog veracity.
The situation & problem
Standard LLM chatbots hallucinate medical diagnoses and quote out-of-stock items or incorrect prices, creating legal and commercial liabilities.
The operational constraints
Zero tolerance for inaccurate medical facts or broken commerce catalog promises; sub-second streaming responses.
Architectural tradeoffs & what was refused
Unchecked zero-shot system prompts with no grounding retrieval layer or deterministic validation parser.
What I built & shipped
RAG architectures backed by structured schema validation, LangChain retrieval chains, database catalog locks, and human-in-the-loop fallback hooks.
Architecture & stack
Python, FastAPI, LangChain, OpenAI / Claude APIs, Pinecone / pgvector, Next.js UI.
Result & measurable impact
Maintained 100% catalog veracity with zero hallucinated SKUs or out-of-bounds medical guidance across tens of thousands of conversations.
Next iterations & roadmap
Autonomous action agents executing end-to-end checkout and appointment booking via tool calling.
System surfaces & interface captures
Hover and click any record to open the full-resolution pixel inspector with zoom lens and 1:1 precision.
Have a similar constraint?
Tell me where your current operational system or product is breaking.