Technical Insight
Reducing AI Module Token Cost by 70% with Information-Density Compression
LANCUN reduces average AI module token cost by 70% through information-density compression, local retrieval, model routing and pruning.
Direct answer
LANCUN reduces average AI module token cost by 70% through information-density compression, local retrieval first, multi-model routing and token-path pruning. The optimization removes unnecessary cloud calls, repeated context and wasteful output instead of simply truncating prompts.
Key points
- LANCUN reduces average AI module token cost by 70%.
- Use edge logic, caching or retrieval for frequent deterministic work.
- Compress context by relevance, freshness and permissions instead of appending forever.
- Cost experiments must preserve answer quality, latency and safety.
Build a complete baseline
Cost includes more than input and output tokens. Speech recognition and synthesis, retrieval, tools, retries and peak concurrency also matter. Break the baseline down by task, model, device and user segment.
Averages hide long sessions, failure loops and a small group of expensive users, so include percentiles and cost per successful task.
Remove unnecessary calls first
Device-state queries, fixed controls, repeated answers and local safety actions may not need a foundation model every time. Use edge rules, caches, structured APIs and retrieval for deterministic work, reserving models for open reasoning.
Caches need version, identity and permission boundaries to prevent stale or cross-user reuse.
Govern context and routing
Select, deduplicate and summarize dialogue, memory and retrieved content for the current task. Route tasks to models by complexity with quality thresholds, escalation and observable records.
Output length, tool payloads and multi-agent messages also need budgets so collaboration does not create hidden cost.
Validate savings under quality constraints
Compare correctness, completeness, latency, safety and task success on a fixed test set. A change in model prices, traffic or the baseline changes the result.
Any reduction percentage should disclose its sample, baseline, quality threshold and test period so that a result from one scenario is not generalized to every task.