THE PROBLEM
Why this system existed
Enterprise knowledge is fragmented across structured and unstructured sources. Naive retrieval can surface stale, weak, or adversarial context and produce confident unsupported answers.
Retrieval · Security · Evaluation
Enterprise RAG & Security Platform
A production-oriented knowledge system with hybrid retrieval, reranking, evidence lineage, deterministic validation, and defense against malicious retrieved content.
0.94faithfulness
P95latency tracked
100%cited claims
OUTCOME
What changed
A reusable pattern for grounded answers under strict data and audit boundaries.
REFERENCE ARCHITECTURE
Controls around the model
01Classify
02Route
03Retrieve
04Rerank
05Generate
06Validate
07Observe
DECISIONS
Trade-offs considered
Hybrid retrieval instead of vector-only search
Reranking before context assembly
Treat retrieved content as untrusted input
Deterministic citation and schema validation
FAILURE CASE
What did not work
Internal abbreviations weakened vector-only recall. Query expansion, keyword retrieval, metadata filters, and reranking improved the evidence set.
SECURITY BOUNDARY
Public-safe by design
This case study exposes patterns, not employer architecture. It uses synthetic data, no client identifiers, no internal prompts, no proprietary datasets, and no production endpoints.