IOV LABSThe RAI Pipeline: Enterprise AI Value Comes From the Control Layers, Not the Model
Abstract
Enterprise AI adoption usually fails not on model quality but on the absence of control and integration. We formalize a field-tested five-stage request pipeline, authorization, DLP masking, internal-document RAG, model routing and caching, and audit and metering, as a single composed function, and quantify the one layer that is measurable in dollars, routing and caching, at 2026 list prices. For a representative enterprise RAG request (5,000 input, 500 output tokens), the pipeline cuts per-request cost from $0.0225 to $0.0077, a 65.9 percent reduction: prompt caching accounts for 36.0 percent and model routing, sending only 30 percent of requests to the frontier model, adds the rest. Routing alone yields 46.7 percent, matching the field-observed 47 percent unit-cost drop. The reduction is fully determined by two levers, the cacheable-input share and the frontier-routing share, and we publish the full sensitivity grid rather than a single headline. We read the pipeline as value equals safety times accuracy times economy times controllability, a product in which any zeroed layer zeroes the whole, and argue this is why single-feature copies do not reproduce it. Finally we note the pipeline defends only the input side and propose output verification, output DLP plus citation checking, as a sixth layer, the same conclusion IOV Labs' completion-illusion and self-preference studies reach: do not trust the model's self-report, verify at the system layer.
Keywords
- enterprise AI
- AI gateway
- prompt caching
- model routing
- cost model
- DLP
- RAG
- governance
- control plane
- reproducibility