Case study · Enterprise performance
Scaling enterprise RAG: a sales training firm case study.
A growing sales training organization needed its RAG system to scale without performance degradation as data volume and user demand grew. Morphos AI applied Green Vectors to improve efficiency and answer quality at the same time.
The challenge
Scale without unexpected degradation.
The organization needed to ensure its RAG system could scale effectively without unexpected performance degradation as data volumes and user demand continued to grow.
The approach
Three configurations, blind evaluation.
Three dataset configurations were evaluated: the existing methodology, the Morphos approach, and a control without RAG context. Blind testing by content creators and subject matter experts identified the optimal approach.
The results
Smaller database, better answers.
Content accuracy
- Vector database size
- Reduced 76%
- Content accuracy
- 50% to 90%
- Data completeness
- 40% to 100%
- Response speed¹
- Roughly 2x (internal latency index moved 30% to 60%)
¹ Response speed is an internal performance index for vector database latency (0% = slowest, 100% = instantaneous). A move from 30% to 60% indicates average query latency was cut roughly in half.
The same query that produced generic advice from a base model now returns specific, actionable guidance with concrete scripts and frameworks.
Why this matters
From bottleneck to asset.
The system moved from a potential scaling bottleneck to a high-performance asset. The combination of a smaller database and higher data completeness means the organization can pursue an aggressive growth trajectory without infrastructure cost scaling out of control.
Frequently asked
Questions about this deployment.
- Q1
How much did Green Vectors reduce the vector database size?
76%, while improving content accuracy from 50% to 90%.
- Q2
Did answer quality improve or just cost?
Both. Content accuracy improved from 50% to 90% and data completeness from 40% to 100%, alongside the 76% reduction in database size.
- Q3
What does the response speed figure mean?
It is an internal performance index for vector database latency, not literal milliseconds. A move from 30% to 60% indicates average query latency was cut roughly in half.
Scale your RAG without scaling cost.
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