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

This post details Glean's approach to achieving token efficiency in enterprise AI platforms, focusing on platform design rather than solely model choice. It highlights the importance of optimizing retrieval quality (passage-level evidence, relevance, permissions, recency, ownership, metadata, cross-system relationships), context management (selecting only necessary evidence, deduplication, respecting permissions, preferring current sources), and intelligent model routing (using smaller/specialized models for simpler tasks, reserving high-cost reasoning). It also emphasizes testing multi-step orchestration with realistic workflows and verifying observability, governance, and measurement capabilities. Red flags for inefficient platforms are identified, such as a sole focus on models, reliance on oversized context, lack of context narrowing explanation, uniform model path for all tasks, and poor visibility into spend.