Validating Vector Databases for Scalable GenAI Workflows
A leading financial institution wanted to evaluate vector databases for high-performance GenAI use cases - testing scalability, query speed, and integration within a secure sandbox.
Outcomes
70%
Faster Query Performance
30%
Compliance Readiness Boost
<100ms
Key Workflows Query Time
10M
Data Points Handled
Technology Vendors Suited to Evaluation
Business Problem
The bank’s existing database infrastructure struggled to support the scale and complexity of GenAI workloads. Performance degraded as vector dimensions increased, limiting real-time semantic search and recommendation capabilities. Integration challenges added uncertainty around which solutions would align best with internal infrastructure and compliance requirements.
The bank needed a controlled environment to benchmark performance and scalability safely.
Challenges
- Scalability Issues: Existing database solutions struggled to support high-dimensional embeddings required for GenAI.
- Performance Limitations: Difficulty in delivering real-time responses for semantic search and recommendation tasks.
- Integration Concerns: Lack of clarity on which vector databases would integrate best with existing banking infrastructure.
From Idea to Evidence with NayaOne
Using NayaOne’s platform, the bank conducted a side-by-side evaluation of four vector databases to identify the most efficient and compliant solution.
- Query Performance: Measured response times for searches across millions of data points.
- Scalability Testing: Assessed throughput and latency as data scaled from 1M to 10M vectors.
- Semantic Accuracy: Evaluated precision and relevance for financial query tasks.
- Centralised Testing: Compared multiple databases within a single sandbox environment, eliminating the need for separate infrastructure setups.
- Compliance Validation: Ensured all testing met regulatory and data-handling standards using synthetic and masked datasets.
Impact Metrics
PoC Timeline Reduction
6 – 8 weeks with NayaOne
vs 6 – 12 months traditionally
Time Saved in Vendor Evaluation
84% faster vendor evaluation
Reduced Validation Costs
85 – 92% lower
KPIs
- Query Latency (ms): Average response time per semantic search.
- Throughput (queries/sec): Number of vector queries processed simultaneously.
- Scalability Index (%): Performance consistency as data volume grows.
- Semantic Accuracy (%): Relevance and precision of vector-based results.
- Integration Time (days): Time required to connect and validate each database
Validate Vector Databases for GenAI
Compare and test vector database solutions in a secure sandbox to identify the fastest, most scalable, and compliant foundation for GenAI workloads.
