While 80% of AI projects fail, we make it reliable.
Empower your data scientists to build production-ready models 10x faster
Traditional AI development is slow, expensive, and unreliable
High costs of hiring specialized AI talent put projects out of reach for many companies. With median total compensation at $170,500 and top performers commanding $237K-$325K+, the financial barrier is significant.
Typically need $180k+ salaries plus equity to attract top Data Scientists
Traditional AI development cycles are painfully slow with months of iteration.
18-24 month timelines before seeing production results
Production models degrade over time without proper monitoring and validation.
Models can lose 40% accuracy within 6 months without detection
Automated intelligence that handles the entire ML lifecycle
Your domain experts define what matters. Add custom dimensions, create business-specific patterns, and encode your unique knowledge directly into the model—empowering collaboration between domain experts and data scientists.
Our AI analyzes your data across thousands of dimensions, automatically discovering hidden patterns and cohorts. Then your experts can refine and add their own insights.
Every model is rigorously tested and validated before deployment. No more guessing if your AI will work in production.
Automatic drift detection and model retraining keeps your AI reliable. We handle the maintenance so data scientists can focus on innovation.
Unlike traditional ML platforms that force your data into pre-built models,
we build models specifically for your data patterns
We extract meaningful patterns from your raw data that matter for predictions but might be hard to spot manually. Our platform identifies data dimensions and understands patterns the way humans do.
We identify meaningful cohorts and their contribution to predictions, helping you understand your data better. The model learns your unique business context instead of fitting into generic templates.
Before deployment, we rigorously test your model with augmented validation data. This ensures your model handles real-world variations and edge cases, not just clean training data. We stress-test against real-world scenarios.
We continuously monitor for pattern drift—changes in how your data behaves over time—and automatically show retrain triggers to maintain accuracy. This ensures your model stays relevant as your business evolves.
Join companies turning data into production-ready models in days
Built by ML veterans from Amazon & Google
Solving real problems we've lived