Overview
- Major retail and commercial bank
- Assets: $900B+
- Branches: 500+
Challenge
A leading bank needed a way to systematically assess GenAI models for accuracy, compliance, and ethical standards before scaling them into sensitive, customer-facing operations.
Solution
NayaOne’s sandbox enabled a secure, production-like PoC where the bank and a leading AI vendor developed a RAG pipeline, evaluated multiple LLMs, and validated outputs against internal compliance and business standards.
Results
- 30% reduction in factual inconsistencies.
- 25% increase in regulatory-aligned outputs.
- 70% faster validation of GenAI responses.
- Data engineering team
- Data science teams
- CTO
An API-driven software layer providing risk mitigation for LLMs, addressing inaccurate outputs, data leakage, and compliance within Generative AI applications.
Overcoming the Trust Barrier in GenAI Deployment
While eager to unlock the efficiency and innovation promised by GenAI, a leading financial institution encountered a critical barrier: the absence of a systematic, reliable way to assess model trustworthiness. Despite the transformative potential of GenAI, there remained serious concerns around accuracy, regulatory compliance, and ethical alignment – concerns that, if left unaddressed, could undermine customer trust and operational integrity.
The inability to consistently evaluate GenAI systems for hallucinations, bias, and adherence to strict industry regulations created significant risk, particularly when considering deployment in sensitive, customer-facing scenarios. Without a clear, standardised evaluation framework, scaling GenAI across the enterprise would remain an aspiration rather than a reality.
Recognising the urgency, the institution sought to establish a rigorous blueprint – a repeatable process to validate and monitor GenAI models before integrating them into critical workflows, ensuring that innovation could move forward without compromising trust, compliance, or reputational safeguards.
Structured Validation of GenAI Models in a Secure Environment
To bridge the gap between GenAI potential and enterprise-grade readiness, NayaOne facilitated a structured Proof of Concept (PoC) between the financial institution and a leading AI vendor, all within its secure, production-grade sandbox environment. This setup enabled the bank to safely and systematically address trust, compliance, and operational challenges before broader deployment.
The PoC was designed with three critical components:
- RAG Pipeline Development: A custom Retrieval-Augmented Generation (RAG) pipeline was built using domain-specific prompts and curated grounding datasets. This significantly improved the reliability, factual accuracy, and contextual relevance of model outputs – reducing the risk of hallucinations and misinformation.
- Secure Sandbox Environment: Testing took place in a highly controlled, cloud-based environment that closely simulated real-world production conditions. This approach ensured that models could be evaluated against operational realities without exposing the institution to compliance or reputational risks.
- Comprehensive LLM Evaluation: Multiple large language models (LLMs) were rigorously assessed against a set of enterprise-specific criteria, including output reliability, business use case fit, tonality appropriateness, and strict alignment with the bank’s internal compliance and risk management frameworks.
Through this approach, the institution gained a repeatable methodology to validate GenAI models and build a foundation of trust, confidence, and control — essential for responsible scaling across sensitive and high-impact use cases.
Real-World Impact of the PoC
The Proof of Concept delivered measurable progress toward establishing a robust GenAI evaluation blueprint. By implementing a structured approach to model assessment and validation, the institution achieved a 30% reduction in factual inconsistencies, significantly enhancing the reliability of AI-generated outputs. This improvement was critical for building internal confidence and reducing the risk of misinformation reaching customer-facing or decision-critical processes.
Additionally, the PoC drove a 25% increase in regulatory-aligned outputs, demonstrating the system’s ability to meet strict compliance standards without sacrificing efficiency. Perhaps most notably, the institution achieved a 70% faster validation cycle for GenAI responses, dramatically accelerating the speed at which models could be tested, approved, and prepared for deployment. Together, these outcomes positioned the bank to move from cautious experimentation to confident scaling, armed with a repeatable, enterprise-grade framework for safe GenAI adoption.