Modern search systems struggle to combine lexical matching with semantic understanding. We explore how we built a probabilistic ranking framework in Cognica Database that transforms BM25 scores into calibrated probabilities, enabling principled fusion of text and vector search results.
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Structural Limitations of Legal Case Search and the Need for Single DB with Vector Search
December 9, 2025
This article provides a technical analysis of why legal case search is challenging in the legal services market. We examine the structural characteristics of legal case data and the limitations of existing distributed architectures (RDB + ElasticSearch + Vector DB), and explain why integrated search based on a single database is necessary.
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Searching Case Law Data with Natural Language
July 4, 2024
Explains how to build a natural language search service by applying vector search to a case law search demo using FTS.