How AI-native orchestration helps here
AI-native development helps move faster through ingestion design, retrieval debugging, evaluation scripts, diagnostics, and product integration without reducing the work to prompt wording.
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RAG Systems
RAG and LLM Systems Engineering
When an LLM needs to answer from documents, product data, or internal knowledge, the quality of retrieval and response policy matters more than model hype. I build grounded systems that can explain what they used, refuse when context is weak, and stay maintainable as the corpus grows.
What I handle
Ingestion pipelines for PDFs, docs, markdown, web content, and structured artifacts.
Chunking, embedding, metadata, retrieval, and answer-policy architecture.
Evaluation harnesses for retrieval quality, refusal behavior, and source-grounded outputs.
Operational tooling for rebuilds, diagnostics, and corpus lifecycle management.
What I usually build
- Grounded Q&A with citations and refusal behavior when evidence is insufficient.
- Knowledge systems that blend retrieval, business logic, and product constraints.
- Document pipelines designed for repeatability, reindexing, and safe evolution.
Relevant case studies
VoiceCrunch AI
AI-powered speech analytics platform that scans recorded calls for keywords, phrases, sentiment and conversational patterns, then transcribes and translates key moments.
Speech-to-textNLPSentimentDashboards
Deep Research Agent
Production-grade deep research agent that plans, fetches, and writes structured reports from URLs and documents with strict guardrails.
PythonLangChainLangGraphOpenAIFastAPI
OpenClaw DocOps Agent
DocOps automation agent for production-grade document ingestion, grounded Q&A with citations/refusals, and an audit harness for evaluation.
TypeScriptNode.jsOpenAIQdrantOpenClaw
Questions clients usually ask before starting
Do you optimize for retrieval quality or just model prompting?
Retrieval quality first. Prompting matters, but weak chunking, bad metadata, or no refusal policy will break a RAG product long before prompt wording becomes the main issue.
Can you work with an existing vector stack?
Yes. I can improve retrieval and answer behavior on top of an existing vector database and ingestion flow, or rebuild the weak parts if the current design is the bottleneck.