ReClaim Announces the Release of MOSES | Reduce Enterprise AI Costs by 30% Without Replacing Your AI Stack.
AI SYSTEMS
Find out how you can Reduce Enterprise AI Costs by 30% Without Replacing Your AI Stack with MOSES
Today's Challenges without MOSES
Higher token, incresed utilization and sky-rocketing energy costs. Not to mention the need for expanding your hardware along with slower speeds all taking place at the cost of your organization and your customers!
Today's Solutions with MOSES
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. It is 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 runway cost or complexity
ReClaim’s architecture delivers continuity, and cost efficiency across heterogeneous environments.
MOSES Operating in a Typical Tech Stack
How do organizations scale AI without scaling costs?
Scale AI Usage Without Scaling AI Spend
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Enterprise AI costs grow because every agent, workflow, document, prompt, and retry sends more token work through the system.
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As usage expands, organizations often face a difficult choice: limit adoption, accept rising inference costs, or invest in more infrastructure.​
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MOSES changes that equation. By compressing token volume across the AI pipeline, MOSES helps organizations deliver more AI value from the infrastructure they already have.
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In fact, validated compression results translate directly into lower inference cost, increased workload capacity, and better utilization of existing GPU environments.​
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The result: enterprises can scale AI adoption while keeping infrastructure costs flat.
