Embedded AI-first SaaS products for research and revenue.
We build AI-native SaaS platforms with production-grade machine learning infrastructure. Our systems handle model versioning, A/B testing, and automated retraining while maintaining multi-tenant isolation and sub-second inference latencies.
Core Capabilities
Scalable model serving with TensorFlow Serving, MLflow, and Kubeflow. Automated retraining pipelines triggered by data drift detection. Versioned model registry with lineage tracking and rollback capabilities.
Isolated data pipelines and compute resources per tenant. Shared model infrastructure for cost efficiency. Row-level security in databases and namespace isolation in Kubernetes clusters.
RESTful and GraphQL APIs with comprehensive documentation. Rate limiting, authentication, and webhook integrations. SDK generation for Python, JavaScript, and Go with type safety.
Usage analytics and model performance dashboards. A/B testing framework with statistical significance testing. Feature stores with online and offline serving for consistent inference.
Technical Foundation
TensorFlow Serving and Ray for model deployment. MLflow for experiment tracking and model registry. Kubeflow for orchestrating training pipelines. ONNX for cross-framework compatibility and inference optimization.
FastAPI for high-performance Python APIs, Node.js for real-time services. PostgreSQL with TimescaleDB for time series. Redis for caching and session management. Kafka for event streaming and inter-service communication.
React and Next.js with TypeScript for type-safe development. TailwindCSS for rapid UI iteration. D3.js and Plotly for interactive data visualizations and model performance dashboards.
Kubernetes for container orchestration with GPU node pools. Terraform for infrastructure-as-code. GitHub Actions for CI/CD. Prometheus and Grafana for metrics and alerting with custom model performance tracking.
Discuss your AI SaaS platform requirements with our engineering team.
Contact Engineering