Most enterprise search is tuned at launch and then left to drift. Catalogs grow, buyer language shifts, and relevance degrades while buyers and AI systems feel the impact. This session demonstrates SearchOS, the agentic optimization layer that keeps search working after launch, and shows how the same work that improves search for buyers makes it AI-ready.
- Search is the Retrieval Layer for Every AI System: A chatbot, copilot, or RAG pipeline is only as good as what the search engine returns. When search drifts, AI answers degrade, agents re-query multiple times, and token costs rise in ways that are difficult to trace and hard to explain.
- Search Degrades Continuously After Launch: Catalogs grow, buyer language shifts, and supplier data introduces inconsistency. Most organizations do not notice until conversion rates slide, and by then the cost of fixing it is significantly higher than the cost of preventing it.
- Ten Autonomous Agents Keep Search Continuously Optimized: SearchOS monitors query performance, catalog coverage, taxonomy drift, supplier data quality, and LLM behavior across the search environment, surfacing issues before they become problems and applying fixes without waiting for a scheduled project.
- Information Architecture is What Makes Agents Work: Agents are only as good as the data and structure underneath them. Deterministic, IA-directed retrieval reduces token costs, improves precision, and keeps AI answers grounded in the right content rather than the most statistically similar content.
- Results Are Measurable from the First Sprint: The engagement model is built around before-and-after measurement at every stage, connecting search performance directly to revenue impact. Recent results include a 15% conversion improvement, a 34% lift in click-through rates, and $150 million in net new revenue identified for a manufacturer whose product data gaps were making them invisible in distributor channels.
Speakers