Modular LLM Reasoning Pipelines

In this live walkthrough, I’ll demo how I built an LLM-powered pipeline system that replaces brittle agent frameworks with modular, composable reasoning stages. The system processes unstructured inputs (like emails and legal docs), extracts structured data, embeds it, retrieves semantically relevant context from a vector DB, and then makes decisions like: “Is this part of an existing deal or a new one?”

No agents. No RAG gimmicks. Just pipelines, embeddings, and judgment calls from a model with traceability.

I’ll walk through:

Canonicalization (turning messy inputs into structured data)

Vector-powered context graph retrieval

LLM reranking and decision logic

Real-time deal creation or attachment via reasoning

How I kept it all observable, debuggable, and scalable across domains

Everything will be live—from the code to the reasoning decisions. No slides, no fluff, just raw execution.