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How to Build a Token-Free AI Platform

This paper presents a domain-agnostic framework for building zero-token knowledge assistants that handle retrieval-shaped tasks without consuming generative LLM tokens on the answer path. By utilizing a deterministic four-layer architecture featuring intent routing, structure-first vector retrieval, and calibrated similarity scoring, the system ensures high reproducibility and auditability. Fielded enterprise case studies demonstrate that this approach successfully eliminates recurring API costs and latency, offering a robust and cost-effective alternative to traditional RAG baselines.

• Landscape scan template and prioritisation matrix
• Target-state architecture with BTP extension patterns
• 12–18 month programme plan with named workstream owners