Conversational AI for Meal Subscriptions
LangChain + LLMs powering subscription support for 125K+ customers with 90%+ intent accuracy.
Problem & industry context
Meal subscription businesses face high support volume: plan changes, delivery windows, dietary preferences, and billing questions. Rule-based bots break on paraphrases; pure LLM demos hallucinate policies. Production FoodTech stacks combine retrieval, tool calling, and guardrails so answers stay on-brand and auditable.
Insight
RAG and agent patterns shine when you ground every answer in policy docs and order APIs—not when the model improvises. Measure success on resolution rate and escalation quality, not chat novelty. LangChain helps orchestrate tools, memory, and prompts, but the product boundary (what the bot may never say) matters more than the framework choice.
What I built
Engineered a meal subscription chatbot using LangChain and OpenAI, integrated with business workflows and deployed via AWS SageMaker. Tuned prompts, retrieval, and evaluation loops to reach 90%+ accuracy on core customer intents. Supported 125K+ customers while reducing repetitive support load for operations teams.
Technical approach
Stack and tooling for this work: Python, LangChain, OpenAI, AWS SageMaker, Next.js, MongoDB. Topics covered: LangChain, LLMs, RAG, Customer Support.
Topics
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