AI-Powered Agricultural Analytics: RAG Chatbot & Intelligent Assistants
Natural language access to every data point in the platform — without leaving the screen you're already on.
Managers navigating multiple screens for every portfolio question
Grain marketing managers needed a way to quickly interrogate client portfolios, analyze performance across crop years, and run scenario-based questions without navigating through multiple screens and manual calculations. The client envisioned a “chat with your data” experience where managers could ask natural language questions like “How is my 2025 Corn position compared to last year?” or “What happens if the market hits $11?”
A product-aware AI advisor connected to live data
We built an AI Marketing Advisor as an integrated chatbot within the BushelView platform, powered by a Retrieval-Augmented Generation (RAG) architecture. The system connects Claude (Anthropic’s LLM) to the client’s live database, enabling context-aware, conversational analytics.
The chatbot is product-aware: when users are in the Grain (BushelView) module, it functions as a grain marketing assistant with knowledge of orders, breakeven calculations, profitability metrics, and marketing plan data. When users switch to the Beef View module, the same chatbot interface automatically transforms into a livestock insurance assistant, knowledgeable about endorsements, coverage expiration, and LRP quote data.
Each user sees their own conversation threads (similar to ChatGPT or Claude), with chat history persisted per session and user. The chatbot UI is accessible as a floating panel from any screen in the application, enabling managers to ask questions while reviewing client data. The implementation includes proper error handling, data type validation, and conversation thread management.
AI Model: Anthropic Claude API as the core LLM, with OpenAI embeddings powering the retrieval layer. Designed for easy model substitution as the AI landscape evolves.
RAG Architecture: Retrieval-Augmented Generation connects Claude to the client's live PostgreSQL database, pulling real-time portfolio data, order history, breakeven calculations, and profitability metrics as context on every query.
Product Context Switching: The chatbot automatically detects which product module the user is in (Grain or Beef) and adjusts its knowledge context accordingly — no manual switching required.
Conversation Persistence: Per-user conversation threads stored in PostgreSQL, with full chat history available per session — matching the UX pattern users expect from modern AI tools.
Integration: Implemented as a floating panel accessible from any screen in the application, with proper error handling, data type validation, and conversation thread management throughout.
How we built it
AI Model: Anthropic Claude API as the core LLM, with OpenAI embeddings powering the retrieval layer. Designed for easy model substitution as the AI landscape evolves.
RAG Architecture: Retrieval-Augmented Generation connects Claude to the client's live PostgreSQL database, pulling real-time portfolio data, order history, breakeven calculations, and profitability metrics as context on every query.
Product Context Switching: The chatbot automatically detects which product module the user is in (Grain or Beef) and adjusts its knowledge context accordingly — no manual switching required.
Conversation Persistence: Per-user conversation threads stored in PostgreSQL, with full chat history available per session — matching the UX pattern users expect from modern AI tools.
Integration: Implemented as a floating panel accessible from any screen in the application, with proper error handling, data type validation, and conversation thread management throughout.
Natural language access to every data point in the platform
Managers can now interrogate their entire client portfolio through conversation — no extra screens, no manual calculations, no context switching.
RAG Architecture, LLM Integration, Claude API, Conversational AI, Real-Time Data, FastAPI
Agricultural Technology