Entity resolution breaks when exact matching is too brittle and dense vectors blur identities. This post introduces SPLADE, a learned sparse retrieval model that keeps inverted indexes and token-level explainability while adding transformer-powered expansion and reweighting. We walk through where SPLADE beats BM25 and dense search, where it can fail on SKUs and over-expansion, and how to run it in Postgres/ParadeDB for large-scale product, customer, or patient identity.

Healthcare providers track the same patient under five name variations. Retailers can't tell when the same SKU is under two different codes. CPG companies buy demand data showing one product with three different names across channels. Supply chains have suppliers that are actually the same company. Every week. Same problem. Different domain. Your data doesn't know what it's describing.

While most organizations chase the latest AI trends, data leaders are building something different: reliable foundations. This isn’t about deploying more agents faster—it’s about assets with lineage, harmonization rules, and semantic definitions that make every AI decision trustworthy. Discover why speed without discipline turns into liability, and how to fund the running back while the quarterback steals the headlines.

You’ve spent millions on analytics, yet every critical question is a six-week research project. The problem isn’t your data; it’s the hidden "translation tax" you pay on every query. An Agentic Mesh, built on a Headless BI architecture, eliminates this tax. It stops the endless data movement and empowers your team to get verified answers to complex business questions in minutes, not months. This isn't magic; it's modern data architecture. Stop translating. Start asking.