We help ML engineers overcome a critical but rarely discussed problem when fine-tuning LLMs on synthetic data: silent model collapse.
Everything looks fine during training — the loss curve decreases smoothly and the job completes without errors. However, once deployed, the model starts producing repetitive, low-diversity outputs or completely ignores the asked question and answers something else.
The real issue almost always lies in the synthetic dataset itself: low-entropy responses, near-duplicate examples, poor instruction-response alignment, and generation loops carried over from the data generation process.
Most existing tools can only detect these problems. They don’t fix them. As a result, teams end up manually filtering thousands of rows, wasting valuable compute time and significantly reducing their dataset size.
Miniclay AI fixes this gap with SynthFix Pro.
Our solution automatically detects and repairs problematic rows in your synthetic dataset — without dropping any data, without sending anything outside your infrastructure, and with complete transparency. Every single change is logged with a confidence score and clear explanation.
Built specifically for ML teams that fine-tune LLMs on synthetic data, including those in regulated industries where data privacy and compliance are critical.
✅ Free tier available for issue detection
✅ Pro and Enterprise tiers for full automated repair
If your team is generating synthetic data for fine-tuning, I’d love to show you what hidden issues exist in your current datasets and how we can fix them.
Drop a comment or send me a DM to schedule a quick demo.

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