Validating a Governed Natural-Language Layer Over Bank Data
The bank prototyped a governed natural-language layer over internal data, moving from open-source to commercial LLMs and into GCP in one production-like sandbox to evidence upsell, cross-sell and operational efficiency.
Outcomes
70%
Faster Insights
100%
Vendor Benchmarked
60%
Analyst Handoffs Reduced
85%
Evaluation Timeline Reduced
Technology Vendors Suited to Evaluation
Business Problem
The bank needed a safe, repeatable way to prototype natural-language access to internal data, compare open-source and commercial LLMs, and prove a path to GCP deployment that would increase cardholder lifecycle value through upsell and cross-sell while improving operational efficiency.
Challenges
- Decision Latency – reliance on data specialists slowed day-to-day operational decisions.
- Fragmented Ownership – start–stop approvals across phases impeded delivery velocity.
- ROI Opacity – no apples-to-apples comparison of open-source, commercial, and native options, which clouded investment choices
From Idea to Evidence with NayaOne
Through NayaOne’s Enterprise Gateway and Workspaces, the bank evaluated multiple vendors in one governed environment:
- Prototyped open-source options (e.g. Defog) safely.
- Tested commercial copilots like OpenAI, Claude, and Gemini.
- Seamlessly transitioned into a Gemini-native path within GCP.
This continuous, comparable evaluation process validated risks and behaviours in context and mapped a clear integration path for enterprise adoption.
Impact Metrics
PoC Timeline Reduction
6 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
10 - 16 months
Decision Quality
Continuous evaluation with contextual risk validation and a bank-native deployment path.
KPIs
- Query Accuracy
- Latency
- Compliance Alignment
- Time to Insight
- Deployment Readiness
Validate Natural-Language Prototyping in Weeks
Discover how to move from fragmented data systems to governed, AI-driven access. Run side-by-side evaluations of LLMs and prove revenue impact before making costly investment decisions.
