Reducing the Cost of Long-Context AI Workflows Without Retraining Models

The enterprise angle

Long-context workflows are attractive for legal review, technical support, code analysis, claims review, and internal knowledge work. The constraint is not only answer quality. It is memory, latency, cost, and the ability to keep the workflow stable under real usage.

What EigenKV explores

EigenKV explores whether KV-cache memory can be reduced without retraining models and without unacceptable quality loss. The relevant enterprise question is whether this lets a workflow process longer context at a cost and latency profile that still makes sense.

• Measure memory reduction and quality together.
• Evaluate on workflow-specific inputs, not only generic benchmarks.
• Track latency and batch behavior under realistic load.
• Keep fallback paths if quality drops on sensitive cases.

How to use the result

Treat EigenKV as applied infrastructure research. It can support a long-context workflow only when evaluation shows that cost, memory, latency, and quality remain inside the operating threshold for that specific use case.