craft ai
Industrialize Generative AI with enterprise-grade MLOps and LLMOps governance
About craft ai
Challenges It Solves
- Organizations struggle to move generative AI models from research to production at scale
- Lack of centralized governance frameworks for responsible and compliant AI deployment
- Difficulty tracking, versioning, and managing multiple large language models across teams
- Complex MLOps infrastructure requirements delay innovation and increase operational costs
- Insufficient monitoring and governance of AI model performance and ethical outcomes in production
Proven Results
Key Features
Core capabilities at a glance
End-to-End MLOps & LLMOps
Complete AI lifecycle management from development to production
Unified platform eliminates fragmented tooling and reduces deployment cycles
Model Versioning & Experiment Tracking
Comprehensive tracking of model iterations and performance metrics
Teams maintain full reproducibility and audit trails for all model changes
Responsible AI & Governance Framework
Built-in compliance, fairness, and explainability monitoring
Ensures ethical AI practices and regulatory compliance across deployments
Automated Model Testing & Validation
Continuous quality assurance before production deployment
Reduces model failures in production and ensures consistent performance
Production Monitoring & Observability
Real-time tracking of model drift, performance, and anomalies
Enables rapid response to model degradation and data drift issues
Collaborative Model Development
Multi-team coordination and knowledge sharing across AI projects
Accelerates innovation through standardized workflows and best practices
Ready to implement craft ai for your organization?
Real-World Use Cases
See how organizations drive results
Integrations
Seamlessly connect with your tech ecosystem
Kubernetes
Deploy and orchestrate AI models on Kubernetes clusters for scalable, containerized production environments
MLflow
Integrate with MLflow for experiment tracking and model registry management
TensorFlow
Support for TensorFlow model training, versioning, and deployment workflows
PyTorch
Native support for PyTorch models across development and production pipelines
AWS SageMaker
Seamless integration with AWS SageMaker for cloud-native ML operations
Hugging Face
Direct integration with Hugging Face model hub for pre-trained generative AI models
Apache Airflow
Orchestrate complex MLOps workflows and automated model retraining pipelines
Prometheus & Grafana
Monitor model performance, infrastructure metrics, and operational health in real-time
A Virtual Delivery Center for craft ai
Pre-vetted experts and AI agents in the loop, assembled as a delivery pod. Pay in Delivery Units — universal pricing across roles, seniority, and tech stacks. No hiring, no contracting, no procurement cycle.
- Plans from $2,000 — Starter Pack, 10 Delivery Units, 90 days
- Refundable on unused Delivery Units, anytime — no questions asked
- Re-delivery guarantee on acceptance miss
- Pre-flight delivery sizing — you see the plan before you commit
How a Virtual Delivery Center delivers craft ai
Outcome-based delivery via AiDOOS’s VDC model. Why VDC vs traditional consulting? →
Outcome-Based
Pay for results, not hours
Milestone-Driven
Clear deliverables at each phase
Expert Network
Access to certified specialists
Implementation Timeline
See how it works for your team
Alternatives & Comparisons
Find the right fit for your needs
| Capability | craft ai | Readvox - Natural v… | Atomicwork | Riku |
|---|---|---|---|---|
| Customization | ||||
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| Enterprise Features | ||||
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| Quick Setup |
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