Legal documents frequently contain heterogeneous references to judicial precedents, making automated Precedent Reference Extraction (PRE) challenging for legal AI systems. Existing approaches rely heavily on LLM-based pipelines, which are often effective but also incur recurring prompting costs in high-volume production workloads and struggle with tracing extracted entities back to their character offsets in the source text. In light of these issues, we present Notarius, a domain-specific and production-ready hybrid framework that decomposes PRE into two stages: Overlapped NER Feature Extraction (ONFE), which extracts fine-grained precedent features, and Precedent Feature Aggregation (PFA), which reconstructs complete references for downstream applications. Our implementation integrates the Unified NER method W²NER with domain-specific heuristics. We evaluate Notarius at both feature and reference levels on a collection of judicial decisions and compare it in production against an LLM-based pipeline. Notarius achieves higher extraction quality, eliminates recurring LLM API costs for PRE, and provides span-level offset traceability. The system currently powers precedent extraction on Jusbrasil, demonstrating that discriminative, domain-specialized NER architectures remain valuable for cost-effective, high-precision legal information extraction in the LLM era.