AI & Automation
Grounded RAG Knowledge Systems
Context-aware AI assistants grounded exclusively in your company's proprietary documents, policies, and databases.
Engineering Overview
Enterprise-grade Retrieval-Augmented Generation (RAG) that prevents hallucinations by grounding responses directly in your internal SOPs, Notion docs, PDFs, support tickets, and databases. Every answer includes interactive source citations with exact page and chunk attribution.
The Operational Problem Solved:
Employees and customers waiting hours to find answers buried across fragmented Google Docs, Confluence spaces, PDFs, and ticket archives.
Key Capabilities
Semantic hybrid search (Vector + BM25 keyword)
Exact source citation & text snippet highlight
Role-based document access controls
Automated vector re-indexing pipelines
Hallucination guardrails & confidence scoring
Fallback to human escalation when uncertain
System Architecture
Document Ingestion Pipeline → Chunking & Embedding (text-embedding-3-large) → Qdrant/pgvector → Hybrid Re-ranker (Cohere) → Grounded LLM Response with Citations.
Underlying Technology Stack:
QdrantPostgreSQLCohere RerankNext.jsFastAPIOpenAILangChain
Guaranteed Deliverables & IP Transfer
Custom embeddable web chat widget
Document ingestion portal (PDF, Notion, Web, SQL)
Admin analytics on top queries and missing knowledge
Security compliance & PII redactor filter