Data Flywheel Operations
Continuous Model Improvement That Cuts Inference Costs By Up To 98.6%
Standing up the flywheel: telemetry, preference signals, human feedback loops, and automated re-training that can unlock up to 98.6% inference cost reduction without losing accuracy targets.
Key Capabilities
Feedback Collection
Capture user ratings, corrections, and implicit signals to identify model improvement opportunities.
Automated Evaluation
Continuous assessment of model outputs against quality, safety, and performance benchmarks.
Intelligent Routing
Route prompts to optimal models based on complexity, reducing costs without sacrificing accuracy.
Continuous Retraining
Automated pipelines that distill and retrain models based on production feedback data.
Technology Stack
Use Cases
- ✓Reducing LLM inference costs while maintaining quality
- ✓Building specialized models for common query patterns
- ✓Improving model accuracy through production feedback
- ✓Optimizing model selection based on request characteristics
- ✓Scaling AI systems cost-effectively
Key Benefits
Up to 98.6% reduction in inference costs
Improved model accuracy through continuous learning
Lower latency with optimized model selection
Better user experience with personalized responses
Sustainable AI economics at scale
Ready to Transform Your AI Infrastructure?
Let's discuss how Data Flywheel Operations can accelerate your AI initiatives.
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