git commit -m "Level up from CRUD to AI Engineering"

The Production AI Stack for
Full-Stack Developers

Stop playing with simple OpenAI API wrappers. Build production-grade RAG pipelines, fine-tuned LoRA weights, semantic caches, and low-latency streaming agents.

bash — neuralstack-eval
➜ ~/workspace python deploy_hybrid_rag.py --eval-testset
[12:00:01] Initializing Qdrant dense vectors + BM25 sparse index...
[12:00:02] Loading custom Mistral-7B-Instruct fine-tune (4-bit QLoRA)...
[12:00:03] Running Ragas evaluation: Context Recall = 0.96 | Faithfulness = 0.98
✔ All 12 production integration tests passed with 24ms p99 latency!
MODULE 01

Advanced RAG & Vector Search

Build hybrid search engines with dense embeddings, BM25, Cohere reranking, and semantic chunking.

pgvector Qdrant Rerankers
MODULE 02

Fine-Tuning & Quantization

Fine-tune open-weights models on domain datasets using Unsloth, Axolotl, LoRA, and GGUF quantization.

vLLM Unsloth LoRA/QLoRA
MODULE 03

Autonomous Agent Frameworks

Stateful graph architectures, cyclic multi-agent teams with LangGraph, tool schemas, and self-reflection loops.

LangGraph MCP Protocols FastAPI