The Future of Enterprise AI Is Context Infrastructure, Not Better Models
summary
Claims that the real bottleneck is fragmented business context, not better LLMs, and demonstrates Spiintel: an ontology-driven intelligence foundation that ingests six example documents from Google Drive/Notion/Slack, auto-generates schemas, stores schema + data in Neo4j-style graphs, combines vector and full‑text retrieval, uses Cytoscape for viz, preserves temporal memory with timestamps, enables governed execution (create/update/delete) and exports portable context; demo builds content and support modules that work but require many backend calls.
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chapters (12)
- 0:00Introduction and speaker background
- 1:33Problem space and context fragmentation
- 2:58Siloed AI systems and limitations
- 4:00Business context as enduring asset
- 5:45Four pillars of context infrastructure
- 7:10Demo setup and data integration
- 9:39Querying the graph and AI reasoning
- 11:23Creating custom AI modules
- 13:27Generating LinkedIn content
- 16:37Customer support assistant module
- 18:22External customer support demo
- 19:24Summary and Q&A
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