Progressive Quality in Action: 52% Better Context Recall with Zero Privacy Compromise

Real Results with Opik

With Opik we measure the quality of our responses and used the results to drive improvements to our solution. The outcomes of this iteration were highly impressive:

• 52% improvement in context recall - retrieving more relevant information for each query

• Enhanced measurement across different models - allowing precise performance comparisons

• Clear visibility into system strengths and weaknesses - identifying exactly what works and what doesn't

The significance? We achieved these improvements in a fully self-contained, on-premises environment. No external services, complete privacy protection, and measurably better performance.

Data-Driven Improvement Process

Using evaluation tools and our extensive datasets, we've replaced guesswork with measurement. This is particularly crucial for private AI systems that can't leverage external services or third-party data.

Our approach is straightforward:

1. Measure performance precisely

2. Target specific weaknesses

3. Implement focused changes

4. Verify results with hard data

The 52% improvement didn't come from a single breakthrough but from this methodical process—each iteration building on lessons from the last.

What This Means for Your Organization

For organizations implementing private AI solutions, these improvements translate to concrete value:

• More accurate information retrieval

• Better utilization of your knowledge base

• Higher user satisfaction and adoption

• Reduced time spent refining queries

The privacy challenge makes these achievements even more significant. Building accurate systems that maintain complete data security requires exceptional methodical development—a challenge we've embraced through our partnership with Opik.

Next Steps

We're already working on the next iteration with clear targets based on what we've learned. The intersection of privacy and performance doesn't have to be a trade-off—with the right approach to Progressive Quality, you can achieve excellence in both.

Want to learn more about our methodology? We're planning a technical deep dive on this work soon. Let us know what aspects interest you most.

Author: Daniel Gallego Vico, PhD, Co-Founder & Co-CEO at Zylon
Published: March 2025
Last updated: Feb 2026
Daniel specializes in secure enterprise AI architecture, overseeing on-premise LLM infrastructure, data governance, and scalable AI systems for regulated sectors including finance, healthcare, and defense.