RAG and source-grounded enterprise AI
Retrieval-Augmented Generation is most useful when the retrieval layer is treated as a product capability rather than a hidden implementation detail. I design ingestion and chunking strategies, semantic search, context selection, and responses that point users back to their sources.
This approach supports enterprise knowledge assistants and contract intelligence tools where accuracy, explainability, and controlled access are more important than novelty.
- LLM integration and prompt orchestration
- RAG pipelines and vector databases
- Document ingestion and semantic search
- Source-grounded answer generation
- Ollama and OpenAI-compatible model workflows
Document intelligence with OCR and NLP
My AI experience also includes OCR and NLP pipelines for extracting and validating information from structured and unstructured financial documents.
I combine extraction models with deterministic validation, multi-occurrence handling, deduplication, and human review. This hybrid approach is often more dependable for regulated enterprise workflows than relying on model output alone.