Synthetic Ground Truth: Mathematically Proving Reliability

"We want to measure accuracy, but we don't have a labeled dataset."

We hear this every week. Most companies have millions of documents, but zero "Question-Answer" pairs to test against. Without a test set, you are flying blind. You're guessing if your new prompt is better, but you can't prove it.

At OpsSolved, we don't guess. We engineer Synthetic Ground Truth.

Mathematically Prove Reliability

If you want to reach 98.4% accuracy, you need a way to measure it every single day. We don't wait for your team to manually label data. We use a multi-step AI pipeline to generate thousands of high-quality test cases based on your actual documents.

Our Pipeline:

1. Generation: A powerful model reads your documents and generates complex, realistic questions.
2. Extraction: The model finds the "Golden Answer" and the exact citation.
3. Critique Loop: A second, independent model reviews the pair. If the citation doesn't perfectly support the answer, the test case is rejected.
4. Final Set: You get 500-1,000 verified Q&A pairs that represent your "Ground Truth."

From "AI Vibes" to Engineering Metrics

Once we have the Ground Truth, we can mathematically prove your system's performance. We measure:

• Recall@k: Do we find the right documents 97% of the time? (Mafin 2.5 benchmark).
• Hallucination Rate: Does the system invent facts? (We aim for < 1%).
• Citation Accuracy: Every answer must have a citation you can verify.

Why This Matters

Regulators (DORA/KNF) and CTOs don't want to hear that the AI "feels good." They want to see the charts.

Synthetic Ground Truth allows us to run a "Needle In A Haystack" test on every deployment. We can prove—with math—that the system is stable, reliable, and ready for the "Adults in the Room."

Conclusion

We don't deploy until we can prove reliability. By engineering your test data first, we turn AI from a black-box mystery into an Industrial-Grade tool.

Measure what matters. Prove what works. OpsSolved.