🧩 What This Covers#
I build search systems that use embeddings to understand meaning - not just keywords. These workflows match questions to relevant documents, messages, or data points, making it easier to surface insight from unstructured sources.
🛠Common Scenarios#
- You want to query internal knowledge without building a rigid search index
- You’re building a retrieval-augmented generation (RAG) system
- You need better ways to find relevant documents, tickets, or notes
- Teams are spending time manually digging for context
- You want to improve LLM performance with reliable source grounding
📌 What I Focus On#
- Structuring embedding-based retrieval flows (e.g., via RAG)
- Using chunking, metadata, and indexing strategies to improve match quality
- Integrating search into tools, bots, or workflows
- Ensuring traceability of results (where it came from, why it matches)
- Keeping pipelines efficient, testable, and maintainable
🚀 Outcomes You Can Expect#
- Smarter, faster access to the information that matters
- Fewer missed insights hidden in unstructured data
- More accurate and grounded LLM responses
- A scalable foundation for knowledge-driven AI tools