AI that ships
Transform prototypes into production. Expert consulting for on-device AI, productionizing vibe-coded apps, and enterprise ML adoption.
What We Do
End-to-end AI consulting from prototype to production. Build custom SaaS solutions, deploy on-device ML, and transform your AI infrastructure.
On-Device AI
Deploy ML models directly on edge devices. Privacy-first, offline-capable, and lightning-fast inference for mobile, IoT, and embedded systems.
- Local inference
- Privacy & security
- Offline capability
- Low latency
Productionize Vibe Apps
Transform rapid prototypes and vibe-coded AI apps into production-ready systems with proper architecture, testing, and deployment pipelines.
- Code refactoring
- CI/CD setup
- Performance optimization
- Scalable architecture
Custom SaaS Development
Stop paying for dozens of SaaS seats. Build a custom AI-powered tool that consolidates multiple SaaS capabilities into one solution perfectly tailored to your workflow.
- Consolidate SaaS tools
- Cost reduction
- Custom workflows
- AI-powered features
AI Adoption Strategy
Comprehensive consulting for enterprise AI integration. From evaluation to implementation, enabling your team for AI-first development.
- Technology assessment
- Team training
- Implementation roadmap
- Best practices
Model Optimization
Optimize AI models for production deployment. Quantization, pruning, and distillation for faster inference and reduced resource usage.
- Model compression
- Inference optimization
- Hardware targeting
- Benchmarking
Private AI Solutions
Build AI systems that respect user privacy. Local-first architectures, federated learning, and secure model deployment strategies.
- Privacy by design
- Federated learning
- Secure enclaves
- Compliance
Common Questions
How much does a custom SaaS cost to build?
A custom SaaS typically costs $50,000–$250,000 for a one-time build, depending on complexity. Most teams break even on their investment within 18–24 months through reduced SaaS subscription costs and increased team productivity.
What is on-device AI and when should I use it?
On-device AI runs machine learning models directly on the user's device instead of sending data to the cloud. Use it when you need real-time speed, data privacy, offline capability, or lower cloud costs.
On-Device AI in 2026: Sub-20ms Inference Is Here →How long does it take to go from prototype to production?
Timelines vary by complexity. Small projects take 2–4 weeks, medium projects 1–3 months, and large enterprise transformations 3–6+ months. For custom SaaS, our typical timeline is 16 weeks from kickoff to deployment.