How STONE
Restructures Your Tokens
A deep dive into how our semantic token optimization engine reduces costs while preserving quality across all major AI providers.
The Problem
AI APIs charge per token. As context windows grow, so do your costs.
Rising Costs
GPT-4o costs $10/M output tokens. Claude 3.5 Sonnet costs $15/M. At scale, AI API bills can reach thousands per month.
Context Window Bloat
Multi-turn conversations accumulate tokens rapidly. System prompts, chat history, and function definitions consume context before your actual query.
Token Redundancy
Up to 95% of tokens in multi-turn conversations are repeated context that the model has already seen. You pay for the same information over and over.
What STONE Does
STONE — Semantic Token Optimization and Natural Encoding — restructures your prompts and context to use dramatically fewer tokens while preserving the full semantic meaning.
It analyzes the semantic structure of your input, identifies redundant and repeated information, and restructures the token sequence so the AI model receives the same meaning in a fraction of the tokens.
The process is fully automatic and transparent. Your application sends a normal API request and receives a normal response. STONE works entirely behind the scenes.
The Paradigm Shift
See the difference between standard API usage and the Lexi approach.
Standard Approach
Full tokens sent to provider
Provider charges for all tokens
Full cost — no optimization
With Lexi
STONE restructures your tokens
Fewer tokens reach the provider
Lower cost — same quality
Benchmark Results
Real-world performance across different use cases.
| Use Case | Savings % | Quality Score |
|---|---|---|
| Coding Assistants | 70–90% | 8.6 / 9.0 |
| Customer Support | 80–95% | 8.2 / 9.0 |
| Research & Analysis | 60–80% | 8.8 / 9.0 |
| Multi-turn Chat | 75–95% | 8.0 / 9.0 |
Results vary by use case, prompt structure, and model. Benchmarks conducted with blind human evaluation on a 1-9 scale.
Built for Every Use Case
STONE adapts to your specific workload for maximum savings.
Coding Assistants
IDE plugins, code review bots, and AI pair programming tools with large codebases in context. STONE restructures repeated code context across turns.
Customer Support
Chatbots, helpdesk AI, and automated support with repetitive knowledge bases and FAQ context. Highest savings category at 80-95%.
Research & Analysis
Document analysis, data extraction, and research workflows with large input contexts. STONE preserves analytical precision while reducing token volume.
Multi-turn Conversations
Chat applications with long conversation history. Each new turn carries the full context — STONE restructures the accumulated history so you only pay for new information.
Start Saving on AI Costs Today
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