> forward_deployed
Token Optimization
Token spend is the one AI cost that scales with success: the better the adoption, the bigger the bill. I treat the context window as a budget where every token pays rent.
> what this looks like in practice
▸Context compression in the retrieval path: documents contribute relevant spans, not full bodies, before they reach the model.
▸Prompt-payload reduction: system prompts, schemas, and few-shot examples audited and trimmed; anything the model does not need for THIS call gets out of the window.
▸Model rerouting by complexity: low-complexity queries route to lighter models, reserving the expensive path for calls that need it. Combined with compression, this cut per-tenant LLM spend on the AI-Infused QE Platform.
▸Measured, always: the observability layer's per-tenant token telemetry is what proves an optimization worked and catches regressions when prompts drift.
> where it shows up
The AI-Infused QE Platform (per-tenant spend engineering under live customer scrutiny) and Loop Copilot (a lean Groq/Llama path where latency and cost budgets are tight).