De-Risking Conversational AI for Data Access
The bank wanted to make data easier to access, but early AI tools raised concerns about accuracy and trust - proving the need for validation.
Outcomes
5
LLMS Evaluated
2 weeks
Total Evaluation Period
95%
Query Accuracy
0%
Production Data Exposured
Technology Vendors Suited to Evaluation
Business Problem
Challenges
- Data Accessibility: Difficulty for non-technical users to access and analyse data efficiently.
- Real-Time Insights: Delays in obtaining actionable insights due to reliance on traditional reporting workflows.
- Accuracy Concerns: Risk of incomplete or inaccurate information from conversational AI tools.
From Idea to Evidence with NayaOne
The bank ran a proof of concept to validate the accuracy, usability, and scalability of conversational AI tools for data analysis.
LLM Testing: Enabled safe, rapid experimentation with large language models using realistic, governed data in a secure environment.
Model Access: Provided access to multiple LLMs and cloud-native tools to compare performance and refine conversational accuracy.
Simulated Testing: Used synthetic queries to measure precision, completeness, and reliability across varied data types and queries.
Prototype Workspace: Created a central, collaborative space for teams to iterate, test, and enhance conversational prototypes.
Stakeholder Visibility: Offered a governed environment for showcasing progress and results, building confidence across business and IT leaders.
The PoC gave the bank quantifiable evidence of LLM performance and a framework to safely accelerate conversational AI adoption across teams.
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
- Response Accuracy (%): Rate of correct or complete answers generated by LLMs during testing.
- Data Retrieval Latency (seconds): Average time for the AI to fetch and deliver insights.
- User Query Success Rate (%): Percentage of queries resolved without human or analyst intervention.
- Model Reliability Score: Combined benchmark of accuracy, coherence, and consistency across models.
- Adoption Readiness (%): Percentage of business teams able to use the conversational AI prototype confidently after testing.
Validate Your AI Safely Before Rollout
Run LLM proofs of concept in secure sandboxes to ensure accuracy, compliance, and trust before enterprise adoption.
