Our leadership team spent few weeks looking for some knowledge graph consultancies to help us map our internal data for an agentic retrieval project and the journey was incredibly eye-opening (and highly frustrating). If you start reaching out to traditional enterprise IT consultancies or the big-four firms, you quickly realize they are still playing an outdated playbook. Their default proposal is always a massive, multi-million dollar data unification phase where they want to spend months cleaning data, building rigid schemas and migrating everything into a centralized database before you can even run a basic AI pilot. We looked into some enterprise context graph tools where a few operates on an outcome-aligned model where they deploy an overlay context layer directly over existing unstructured silos (sharepoint, outlook, crm) using their platform. Architecturally, it maps entity consolidation and tracks temporal states using cypher queries over an Apache age graph database backend out-of-the-box. If your core business isn’t database engineering, trying to manage a massive custom graph infrastructure project with traditional consultants is a complete money pit. The big shift is that we moved from a consulting phase to a deployed working prototype in less than two weeks without moving a single file or changing how our teams store documentation. submitted by /u/sibraan_
Originally posted by u/sibraan_ on r/ArtificialInteligence
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