Buyer’s guide: what to look for in an enterprise AI platform for token-efficient deployment

What this post added

This post details Glean's approach to token-efficient deployment of its enterprise AI platform, focusing on optimizing retrieval, context selection, and orchestration to minimize waste and maximize outcome per token. It introduces evaluation criteria for token efficiency, including cost per task, latency per task, output quality and groundedness, and scalability. Key technical aspects discussed are passage-level evidence retrieval, context management (deduplication, permission respect, source preference), intelligent model routing across different model tiers, and multi-step orchestration with scoped working memory and intermediate result compression. The post also highlights the importance of observability and governance for measuring and managing token efficiency, and identifies red flags in vendor evaluations related to model-centric discussions, oversized context, lack of context narrowing explanation, uniform model path usage, and poor visibility into spend.