The problem
A 16-turn voice call makes 48 model calls: STT, LLM, and TTS on every turn. Without an execution layer, each of those 48 runs from scratch. At 1M calls per month, that is 48M inference calls, every one generated fresh regardless of whether the output has been produced before. The same consent disclosure. The same hold message. The same greeting. Generated from scratch, every time.What the execution layer changes
Every turn is routed through the execution path it actually needs. Three stages, one for each part of the voice pipeline:| Stage | What it does |
|---|---|
| STT Routing | Route input to the right transcription model, based on language, accent, noise, and cost. |
| Tiered Decisioning | Determine whether the turn needs full LLM reasoning, local inference, or no inference at all. |
| Output Assembly | Assemble TTS output from cache and synthesis. Don’t generate what already exists. |
The system improves under load
Every call through the system improves routing decisions and cache coverage for the next one. Cost and latency decrease with usage. Reliability increases.- More calls, more cache coverage, fewer model calls, lower cost
- More patterns observed, better routing decisions, lower latency
- More providers configured, more failover options, higher reliability
What customers see
| Metric | Improvement |
|---|---|
| End-to-end latency per turn | Up to 48% reduction |
| Total pipeline cost | Up to 57% reduction |
| Call completion rate | Zero dropped, zero downtime |
How to integrate
SLNG works with the orchestrator and models you already use. The endpoints sit between your orchestrator and your providers.How It Works
Architecture and the request lifecycle.
Adaptive Execution
How path selection works.
Integrations
Connect your orchestrator.