LLM infrastructure

AI Core

The LLM stack, layer by layer.

Tokenization with BPE, semantic chunking, multimodal document parsing and an annotation studio for RLHF/DPO data; contrastive embeddings, an HNSW vector database, knowledge-graph extraction, hybrid dense + BM25 retrieval and cross-encoder reranking.

On top: an inference server with token streaming and dynamic batching, a semantic model router, bidirectional guardrails, ReAct agents with structured memory and a sandboxed tool runtime — and the MLOps loop around them: synthetic data, dataset versioning, QLoRA fine-tuning, an eval harness and tracing. A hub orchestrates all of it.

Repositories
22
Lines
25,716
Commits
255
Test files
43
Built
Jun 2026 – Jul 2026
Mostly
Python

↳ Flagship

Where it all comes together

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