Intelligence Delivery Network
CDN-style routing model for LLM workloads
route requests to the right compute tier before spending frontier-model money
What it is
Intelligence Delivery Network, or IDN, is a CDN-style architecture for AI workloads. Instead of sending every request to the same large model, IDN profiles the request first and routes it to the compute tier that fits its complexity, privacy needs, latency target, and tool requirements.
The central idea is simple: trivial, private, or latency-sensitive requests should stay close to the user; expensive global frontier models should be reserved for work that actually needs them.
The tier model
| Tier | Location | Typical use |
|---|---|---|
| L0 | On-device | Offline use, privacy-forced prompts, PII preprocessing |
| L1 | Edge | Fast extraction, classification, lightweight RAG |
| L2 | Regional | Coding, summarization, document workflows, tool agents |
| L3 | Global | Deep reasoning, research synthesis, multi-agent planning |
Core components
Workload profiler
Analyzes token size, complexity, reasoning hops, domain, PII risk, and latency sensitivity.
Router
Maps profiler output to a tier, expert model, tool plan, fallback path, and cost estimate.
L0 privacy firewall
Keeps data on-device when egress is blocked and can redact sensitive fields before escalation.
Async planner
Splits multi-part requests into subtasks that can route to different tiers and merge cleanly.
Status
Development is currently on hold. The repository captures the architecture, routing model, profiler schema, and prototype direction.
See the GitHub repo for the current design notes.