The TENSIX AI Build Method
Many AI tools are just a chatbot added to a website. They guess, they are slow, and they can do the wrong thing. The TENSIX AI Build Method tackles each of those problems in one step. Hemal Shah uses it on every AI agent project.
Step 1: Teach the AI your business (GraphRAG)
The AI should answer from your own documents, not from guesswork. Your files are organised in two ways: a search index that finds text with a similar meaning (pgvector), and a knowledge graph (a map of how customers, products and orders connect, stored in Neo4j). Together they let the AI answer questions that need several facts joined up, such as which customers bought a product and later raised a complaint.
Step 2: Keep the app fast while the AI thinks
AI replies often take 5 to 15 seconds. Instead of making the screen freeze, the app accepts the request straight away and the AI works in the background (using FastAPI and n8n). The user can keep working and sees the answer as soon as it is ready.
Step 3: Check every action before it happens
When the AI needs to do something real, like update your CRM or send an email, it does not do it directly. It writes a structured request that is checked against strict rules (Pydantic models). Plain Python code, not the AI, then carries out the action. This keeps a made-up or wrong action from reaching your systems.