Insights
Research, evidence, and field notes.
Research on compact representation, retrieval efficiency, changing information, and the hardware limits that shape where AI can operate.
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Evidence
What has actually been measured.
Research is only useful once it survives a real collection. Each case study below states the workload, the configuration, the result, and the limits of that result. Read together they describe one finding: removing repeated meaning before storage lowers what a system has to keep and search, without giving up retrieval quality.
- BenchmarkRead the case study
Project Gutenberg at 15-million-vector scale
A public literary collection reduced by up to 99.5% in stored vectors, with roughly 4x faster queries and 59% better search quality under the tested configuration.
- Head-to-headRead the case study
Green Vectors vs Elastic BBQ
Reduction compared directly with binary quantization on the same collection: 2.1x higher accuracy and 116x more storage efficiency, on different axes of the same problem.
- DeploymentRead the case study
Cutting vector storage by ~89% at enterprise scale
An enterprise document-retrieval platform reduced its index by roughly 89% without accuracy loss, running entirely on host CPU.
- DeploymentRead the case study
Patent search, from keywords to meaning
A patent search system moved from keyword matching to semantic retrieval with a 67% storage cost reduction and materially faster conceptual search.
- DeploymentRead the case study
Scaling retrieval for a sales training firm
A 76% reduction in vector database size with content accuracy improving from 50% to 90% on the same corpus and queries.
Every figure above comes from a specific collection under a specific configuration. Other collections, and sensor, perception, or constrained-hardware workloads, require separate tests.
External perspectives
Published elsewhere.
Articles by the Morphos team published by independent outlets.
- External articleRead at Techstrong.ai
Why Semantic Redundancy Is Becoming AI's Hidden Infrastructure Tax
How repeated meaning in enterprise retrieval systems increases storage and compute, and how reducing it earlier changes storage and search requirements.
More research and field notes are in preparation.
Test the research on a real workload.
Bring the dataset or hardware and define the constraint. A focused comparison is more useful than a generic demonstration.
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