ReClaim Announces the Release of MOSES Reduce Enterprise AI Costs by 30% Without Replacing Your AI Stack. Reclaim Joins Claude Partner Network Program!
AI SYSTEMS
MOSES is a lossless compression layer for your AI pipeline.
It cuts the number of tokens your systems process by ~30%.
Same models, same infrastructure, same answers. Lower inference cost, more capacity, nothing rearchitected.
For teams running AI at cloud scale, conversational products, RAG and agent stacks, or on-device AI.
MOSES
​What it is:
A lossless, deterministic compression system that reduces the number of tokens a large language model must process across the AI pipeline.
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What it does:
Reduces token count while preserving meaning exactly. Tokens are the unit that drives inference cost, latency, memory use, and energy. Fewer tokens processed means lower cost and higher effective capacity on the same hardware. ​​
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Key properties:
Lossless and deterministic: identical input yields identical output, and compressed artifacts are fully reversible. This supports auditability and reproducibility.
The Problem
AI adoption is scaling faster than AI budgets. Every new AI agent, workflow, document, and prompt adds more token work. Over time, token volume becomes a direct driver of cost, latency, and GPU demand.
The Evidence
Validated results show measurable efficiency gains.
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~30% average token reduction.
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20%–50% observed compression range.
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Independent validation through internal and UC Irvine student testing.
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~1.3 ms average time to first token.
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Up to ~75% unused infrastructure capacity observed in the validation environment.
The Answer
MOSES reduces the work AI systems have to process. MOSES applies deterministic, lossless token compression so organizations can reduce unnecessary token volume while preserving integrity, consistency, and auditability.
The Outcome
MOSES lets enterprises do more AI work with less infrastructure, reducing cost per token while increasing the capacity of systems already in place.
Validated AI Efficiency, Measured with Results
Deterministic, lossless AI token compression that reduces token volume while preserving repeatable, auditable behavior.
Independent validation through internal testing and UC Irvine student testing, with external testers observing the same compression ranges and consistency.
Faster AI response times with validation results showing ~1.3 ms average time to first token and ~2.0 sec median full response time.
​Higher infrastructure utilization by reducing token work and creating more usable capacity from existing GPU environments.
Enterprise-ready governance and auditability supported by deterministic, lossless architecture that preserves data integrity for regulated environments.