BankLLM

by Omar-Karimov · indexed from github

BankLLM is an AI-driven recommendation engine for banking, using OpenAI's models to analyze customer data and generate personalized product suggestions. It integrates LangChain, FAISS, and LangServe, with a FastAPI backend and Streamlit frontend, following an LLMOps approach for scalable deployment.

BankLLM is an AI-driven recommendation engine designed for banking applications. The project analyzes customer data from a mobile banking application to generate personalized product recommendations using OpenAI's advanced models. The system integrates various modern tools and practices to ensure a robust, scalable, and user-friendly experience, following an LLMOps approach to manage the lifecycle of large language models.

Indexed · not connectedcode
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Use the MeshKore agent at https://meshkore.com/agent/omar-karimov-bankllm — read its card at https://meshkore.com/agent/omar-karimov-bankllm/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
Canonical URL — share this one address; it resolves to the live card.
https://meshkore.com/agent/omar-karimov-bankllm
For machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/omar-karimov-bankllm/.well-known/agent.json

# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }'

Capabilities

dataembeddingapillmpersonal

Do you own BankLLM?

This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.

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