MOSES | ReClaim | Reduce LLM Token Usage and Lower AI Costs

Our team has SOLVED a critical infrastructure challenge & uncovered a deterministic, lossless compression layer with the potential to redefine how AI scales, that we call MOSES.

What does this mean to you?
Delivering a conservative
~30% reduction in per-token processing cost.

~30% lower AI processing costs (conservative)

Lossless token compression reduces token volume by ~30%, directly lowering LLM inference and processing costs with no impact to accuracy or auditability.

Low risk, low‑friction deployment

Implemented before/after inference; no changes to applications, schemas, or downstream systems.

Preserves compliance and evidentiary integrity

Perfect round‑trip reconstruction ensures original text is always recoverable, supporting audits, records retention, and regulatory requirements.

Customer‑optional efficiency layer

Organizations can offer MOSES as an opt‑in option for customers to reduce AI costs or increase usage headroom without changing workflows.

Security‑aligned
by design

MOSES operates entirely within controlled environments, does not persist customer data, and reduces data exposure by minimizing the volume of text processed and transmitted during inference.

Clear, measurable
value shown

Savings are easy to explain and justify: token reduction × per‑token cost; simplifying customer budgeting, procurement, and approvals.

ReClaim’s core technology: a high‑performance system built specifically for modern AI workloads.

Rather than expanding model size or context windows indefinitely, MOSES focuses on lossless optimization—preserving meaning while dramatically reducing how much data must be carried forward.

MOSES enhances existing AI stacks and deployment patterns, enabling the scaling of AI systems without runaway cost or complexity

Validated

Performance
~30% token compression
1.54x token reduction
1.87x byte compression
Lossless & deterministic generation
Audit‑ready for enterprise and regulated workloads

Why it Matters

Direct reduction in inference cost and Watt per Token
Works across retrieval, embeddings, reasoning, and generation
Designed for hyperscale and Enterprise environments

What's Different

Core representation layer, not RAG or middleware
System‑wide efficiency vs point optimizations
Patent‑protected, non‑trivial to replicate

Next Steps

We recognize that AI is a race and ReClaim is locked in and ready to go
System live and ready for testing.

What does this mean to you? Delivering a conservative ~30% reduction in per-token processing cost.