Same AI.
Fraction of the tokens.
Lexi sits between your code and the model. It restructures context before each call — fewer tokens sent, same results back. You keep the savings.
GPT-4o benchmark, 75-turn conversation, March 2026. Results vary by content and conversation pattern.
Built for production AI workloads
Four reasons teams move their AI traffic through Lexi.
Your models. Your keys. Our savings.
OpenAI, Anthropic, Google, Mistral, xAI, DeepSeek, Meta — 33 models, one endpoint. Your provider key goes straight to them. Lexi never stores it.
Swap one URL. Ship the rest.
Replace your provider's base URL with Lexi's. Streaming, tool calls, structured output — all pass through unchanged. Integration takes minutes.
No saving? No fee.
Lexi takes 40% of what it saves you — nothing else. When a request doesn't benefit from restructuring, you pay exactly what you'd pay going direct.
Long sessions that actually work.
Most AI conversations hit a wall. Lexi keeps context bounded so sessions stay sharp across dozens of turns. Restructured, not truncated — facts survive.
Every turn resends everything
Standard APIs send the full conversation on every request. Turn 10 carries the weight of turns 1 through 9. Lexi restructures that history into a bounded form — cost stays flat.
Context windows grow. Your bill grows faster.
Models went from 4K to 1M tokens in two years. But token pricing hasn't dropped at the same rate — and usage scales faster than prices fall. The team spending $10K/month on AI today won't spend less next year. They'll send more.
Bigger windows mean your conversations can fit. Lexi means they don't have to. O(1) beats O(n) regardless of what n costs.
Powered by STONE
Semantic Token Optimization and Natural Encoding. A purpose-built engine that restructures your conversation into a bounded form before it reaches the AI provider. The amount sent stays constant — whether you're on turn 3 or turn 30.
Bounded
Turn 50 sends roughly the same amount as turn 5. Context size stays constant no matter how long the conversation runs.
O(1) resources
14.4 KB per session. Constant memory and CPU. Resource usage never scales with conversation length.
Fact pinning
Numbers, dates, and decisions are pinned into a permanent anchor that survives every restructuring pass. In a 75-turn benchmark, every directly-queried fact was recalled correctly.
Zero-negative guarantee
If restructuring can't help, the original is sent instead. You never pay more, and quality never drops below baseline.
One endpoint. Every major provider.
Lexi detects the provider from the model name in your request. Same models you already use — nothing to reconfigure.
Benchmark results
75-turn conversation benchmark on GPT-4o, March 2026. Multi-domain: project planning, database design, infrastructure, debugging, marketing. Results vary by content and conversation pattern.
1.6M tokens reduced to 135K over 75 turns — 88% cost reduction ($0.52 vs $4.37 provider cost on GPT-4o). The longer the conversation, the more STONE saves.
Tokens sent over time
Without Lexi, every turn adds to the payload. With STONE, it flatlines. By turn 75, direct sends 21K+ tokens per request while Lexi holds steady under 2K.
Live benchmark data. Results vary by content and conversation pattern.
A system that learns, not just forwards.
A proxy forwards requests. STONE remembers your conversation, understands what matters right now, and gets better at it over time — all within fixed resource bounds.
Total recall
Every message is permanently archived with encryption at rest. Nothing is ever discarded. When a fact from turn 3 matters at turn 70, it's retrieved in full — not from a summary, from the original.
Query-conditioned
When you ask "what port did we decide on?", STONE doesn't retrieve a generic chunk. It extracts the minimum content the model needs to answer that specific question — facts, decisions, and exact values, not filler.
Learns from use
After every response, STONE scores what it recalled against what the model actually used. Strategies that produce useful context get reinforced. The system gets sharper the more you use it.
Self-repairing memory
Three layers of recall, each backing the next. If the fast path misses, a deeper layer finds it — then teaches the fast path so it hits next time. Every retrieval makes the system faster.
Fixed cost at any depth
Turn 75 costs the same as turn 5. Every layer — storage, recall, scoring, learning — operates within constant resource bounds. Memory grows, but what's sent to the model stays flat.
Zero-negative floor
If any of this can't help on a given request, the original goes through unchanged. The worst case is always identical to not using Lexi at all. You never pay more. Quality never drops below baseline.
Verified: 75-turn blind benchmark against GPT-4o. 91.6% token savings. 88% cost reduction. Facts recalled correctly at 70+ turn depth.
Every cent. Every request. In the headers.
No estimates. No end-of-month surprises. Every API response carries the exact cost breakdown in HTTP headers you can log, alert on, or surface in your own product.
Two lines of code
Change the base URL, combine your keys. Streaming, tool calls, structured output — all pass through unchanged.
Full documentation →const openai = new OpenAI({
baseURL: 'https://api.lexisaas.com/v1',
apiKey: 'lx_live_yourkey:sk-your-openai-key',
});
// Anthropic works the same way:
const anthropic = new Anthropic({
baseURL: 'https://api.lexisaas.com',
apiKey: 'lx_live_yourkey:sk-ant-your-key',
});Cut your token bill
from the first request.
Sign up, paste your provider key, change one URL. Lexi handles the rest — you only pay when you save.