AI Engineering
RAG Engineering & Knowledge Systems
Transform your documents, SOPs, and repositories into an accurate, citeable knowledge brain.
Overview & Engineering Approach
Generic search fails when querying complex regulatory policies, engineering specs, or contract histories. We engineer advanced RAG architectures with hybrid search, re-ranking, and chunking strategies that provide accurate answers with exact page citations.
Core Engineering Advantages
- Every answer backed by clickable source citations and confidence scores
- Supports unstructured PDFs, scan OCR, spreadsheets, and database rows
- Hybrid lexical + dense vector search for high precision on domain jargon
- Granular document permissions respecting user security clearances
Technical Capabilities
- Context-Aware Document Chunking & Parent-Document Retrieval
- Dense Vector (pgvector, Qdrant) + BM25 Hybrid Search
- Cross-Encoder Re-ranking (Cohere / BGE)
- Dynamic Context Window Synthesis & Citation Tracing
Primary Technology Stack
pgvectorQdrantLlamaIndexFastAPIPythonUnstructured.io
Technical Questions & Architecture Notes
How fast is query retrieval in large document sets?
With optimized vector indexing (HNSW) and semantic caching, our retrieval layers consistently respond in 150-350ms across millions of chunks.
Next Steps
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Let's build high-performance technology together.
Speak directly with engineers about technical feasibility, architecture requirements, and timeline projections.