Enhancing Developer Experience with AI Copilots
A financial institution wanted to evaluate AI-powered coding assistants that could accelerate developer productivity and elevate developer experience.
Outcomes
50%
Developer Efficiency Gain
30%
Risk Reduction
15%
Quicker Security Issue Resolution
70%
Reduction in Development Time
Technology Vendors Suited to Evaluation
Business Problem
Developers were slowed by repetitive coding tasks, fragmented tools, and limited access to AI assistants due to network and compliance restrictions. The lack of audit trails raised policy concerns, while disconnected environments reduced morale and innovation.
The organisation needed a secure way to evaluate copilots that enhanced developer flow, improved code quality, and aligned with governance and architectural standards.
Challenges
- Limited Audit Trail: Incomplete traceability of AI interactions raises compliance and policy concerns.
- Access Barriers: External tool integration into secure dev environments hindered by network restrictions, lengthy approvals, and vendor risk protocols.
- Need for Strategic Alignment: Platform choice requires cross-functional buy-in across architecture, risk, and engineering, with lasting impact on dev enablement and governance.
From Idea to Evidence with NayaOne
Using NayaOne’s secure platform, the institution ran a controlled PoC to compare multiple AI copilots in real-world development conditions.
- AI Copilot Selection: Shortlisted enterprise-ready copilots for code, documentation, and testing support.
- Sandbox Testing: Deployed secure, pre-configured IDEs, mock repositories, and Jira integrations to replicate authentic developer workflows.
- Developer Experience Benchmarking: Measured satisfaction, adoption intent, and perceived friction across teams using structured surveys and telemetry data.
- Performance Tracking: Captured key KPIs such as feature velocity, bug detection, and onboarding speed.
- Cross-Functional Validation: Enabled architecture, risk, and engineering leads to align on security, policy, and developer enablement goals.
Impact Metrics
PoC Timeline Reduction
8 weeks with NayaOne vs 12 – 18 months traditionally
Time Saved in Vendor Evaluation
1+ year
Decision Quality
The PoC gave the organisation clear, evidence-based insight into how each AI copilot performed across productivity, governance, and developer experience.
KPIs
- Feature Velocity (%): Increase in code delivery speed.
- Bug Detection Accuracy (%): Rate of accurate issue identification.
- Audit Coverage (%): Extent of traceable AI outputs and user actions.
- Integration Success Rate (%): Compatibility across IDEs and workflow tools.
Validate AI Copilots for Better DevEx
Test and compare AI copilots in a secure sandbox to enhance developer experience, speed up delivery, and ensure enterprise compliance.
