KBSE: Knowledge Base Search Engine
Legal answers that cite their sources — 94% validated accuracy.
Context & Constraints
Corporate tax and legal teams operate on massive, fragmented knowledge bases: legislation, regulatory guidance, internal memos, and technical opinions spread across dozens of documents.
Traditional keyword search fails in this context — it lacks semantic understanding, provides no reasoning trace, and offers poor traceability. The core challenge was retrieving accurate legal-tax answers while preserving explainability and source traceability — not just 'what is the answer,' but where it comes from and why it is valid.
Architecture
KBSE is a web-based multi-agent semantic search engine built with Python and CrewAI. Instead of relying on a single LLM response, the system decomposes reasoning into specialized stages: semantic retrieval from a vector store, cross-document consolidation, validation/auditing of retrieved excerpts, and final answer generation with explicit citations. Every response includes the synthesized answer and the exact legal text used as evidence.
KBSE — multi-agent semantic search with citation validation
My Role
Led the solution architecture and reasoning design. Implemented the multi-agent workflow using CrewAI (retrieval, consolidation, validation, redaction) and designed the RAG pipeline with citation validation logic.
Results & Validation
Multi-agent reasoning decomposition (Retrieval -> Audit)
Vector-based RAG with explicit citation trails
Sequential validation to eliminate hallucinations
Achieved 94% accuracy in validation tests. In regulated sectors, trust is not about magic; it is about traceability. By validating every output against source documents, we turned 'hallucination risks' into an auditable compliance asset.
Stack
Building something where reliability matters?
I design, ship and operate systems like this one.
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