Many clinicians have quietly asked this question: how can we use technology to see what our eyes cannot, and do it faster without losing accuracy? Bionova’s founder faced this same struggle again and again, a process involving hours spent entering biomarkers from lab results, converting between units, checking for errors, and generating reports that still needed review. This intensive work meant it could take as long as eight hours for a single patient.

In response, the vision was simple yet ambitious: to build an intelligent system that could read laboratory results, interpret them with clinical reasoning, and generate reliable insights in minutes. But to make that possible, a fundamental shift was required, as artificial intelligence had to become clinical intelligence.

That was where Slaine Labs came in.

The challenge was not just time. It was trust.

Bionova wanted to automate report generation, but automation alone could not interpret medical data safely.

They needed a system that could

• Extract data accurately from laboratory results.
• Convert between international and US units without error.
• Provide medically sound insights while keeping the clinician in control.

The question was not if it could be done, but how to do it safely and responsibly.

At its core, Bionova's challenge was precision.

AI could process numbers, but it lacked clinical context with things like gender, reference ranges, and disease patterns. The real solution, therefore, required merging AI's speed with human oversight.

Ultimately, we needed to design a workflow that understood, validated, and adapted through every lab report processed.

We treated Bionova like a clinical system, not a piece of software. Every layer was built around accuracy, accountability, and ease of use.

• The backend handled extraction, matching, and conversions with built-in safeguards.
• The frontend gave clinicians full control to review and approve results before final reports.
• AI served as a support system, while the clinician remained the decision-maker.

The outcome was a product that was intelligent, reliable, and safe for real-world healthcare use.

Biomarker Processor

Extracted up to 95 percent of biomarkers accurately from raw lab results, resolving duplicates and missing values automatically.

Matcher Engine

Matched biomarkers across inconsistent formats, names, and units, reducing correction time by more than 80 percent.

Conversion Module

Converted biomarkers between international and US units, flagging uncertain conversions for clinician review.

Human in the Loop Control

Gave clinicians the power to verify, edit, or add biomarkers at any stage, ensuring the report always reflected medical judgment.

After launch, Bionova's transformation was clear.

Generating a health insights report dropped from eight hours to eight minutes, with accuracy reaching 95 percent. Clinicians now process patient data faster and with greater confidence, knowing every insight is backed by structure and logic.

For the founder, it proved one thing: with the right design, AI does not replace medical judgment. It strengthens it.

Bionova taught us that the future of medicine is not machine-driven but human-led.

When AI respects context and clinicians trust the process, technology stops feeling artificial and starts feeling alive.

This is what we build for, precision that serves people.