Working with Google’s Gemini and Gemma models, InstaLILY engineered a multi-stage, teacher-student pipeline that generated and refined synthetic data with remarkable efficiency. Using Gemini 2.5 Pro as the “teacher” and fine-tuned Gemma models as the “students,” the system achieved high labeling precision and enabled scalable, low-cost deployment.

The results speak for themselves:

• Latency: Reduced from 2 minutes to 0.2 seconds (-99.8%)
• Serving cost: Lowered by 98% to $0.002 per 1,000 queries
• Accuracy: ~90% F1 score on a blind test set

The project went from prototype to production in just four weeks, a process that would normally take months. Through the Google for Startups Accelerator, InstaLILY gained early access to Gemini 2.5 Pro, technical mentorship, and cloud resources that made this speed possible.

Next, the team is expanding into multimodal and continuous-learning capabilities: from image-based diagnostics to live model retraining.

Together, InstaLILY AI and Google are showing how agentic architectures can transform enterprise systems into truly intelligent, high-performance operations.

The full case study is featured on Google DeepMind’s AI Showcase: “InstaLILY: Accelerating Agentic AI at Scale”