AI Engineering Stack
Build reliable, production-grade AI applications
Moving AI from prototype to production requires more than a single API call. This stack covers the full pipeline: LLM access, retrieval-augmented generation, vector storage, evaluation, and monitoring — the tooling used by AI engineering teams shipping real products.
Anthropic Claude
Claude models excel at reasoning, long-context tasks, and following complex instructions. Strong safety track record for enterprise use.
LlamaIndex
Connects LLMs to your private data. Handles chunking, indexing, retrieval, and query pipelines with minimal boilerplate.
Weaviate
Open-source vector database with hybrid search (vector + BM25). Runs locally or managed in the cloud.
MLflow
Tracks prompt versions, model parameters, and evaluation scores across every experiment. Essential for reproducibility.
DeepEval
Unit-tests your LLM pipeline — correctness, hallucination, context recall. Integrates into CI so quality regressions are caught early.
BentoML
Deploys LLM-backed applications as scalable APIs with request batching, GPU autoscaling, and multi-model orchestration.