Capability
RAG & Knowledge Retrieval
Retrieval-augmented generation over real documents: answers that cite their source, retrieval that can run locally, and costs with a hard ceiling.
Problems I Solve
Where this capability fits
Answers have to cite the exact source, not paraphrase model memory.
Positional chunks lose structure, so a hit can say "chunk 37" but not which section it came from.
Exact terms, names, and dollar amounts get missed by dense vectors alone.
Sensitive text and user questions should not be sent to a third party just to be indexed.
Scope
Capabilities
Structure-aware chunking: by section, not by character count
Hybrid retrieval: lexical (tsvector) and dense (pgvector HNSW) fused with reciprocal rank fusion in SQL
Local embedding models, swappable without a rebuild
A citation for every claim, and refusal when a question is out of scope
Prompt-injection handling: the question is treated as untrusted text
Answer reuse with similarity thresholds measured on real question pairs
Alias and trigram lookup for the names people actually use
Example Systems
Systems I design and build
Grounded Q&A over legislation and other long documents
Semantic search inside an existing PostgreSQL database
Research assistants that show their sources
Extraction pipelines for encrypted or awkward PDFs
Technical Stack
Tools and platforms
Need this capability on your product?
Start with a technical scoping call focused on data sources, users, and success criteria.