As the power of artificial intelligence permeates our lives, organizations are looking for innovative ways to leverage the power of new and evolving breakthroughs. Bridging the gap between pioneering research and operationally useful AI solutions that drive real business value remains a significant hurdle.
At the foundation of AI is a mountain of open source innovation. With our roots in open source scientific computing, Quansight understands the complexities of transitioning advanced AI models from conceptual innovations into production-ready, scalable systems. Our AI engineering solutions provide the robust infrastructure, sustainable software practices, and integration capabilities to transform theory into real-world impact across your organization.
Establish Sustainable AI Software Foundations
Rapidly Deliver Production-Grade AI Capabilities
Tailor infrastructure for Compute-Intensive Inference or Training Workloads
Responsibly use the latest Innovations both Open Source and Proprietary
Bridge the AI Innovation-Execution Divide
Utilize existing infrastructure and mitigate AI Engineering Risks
Whether you need environments tailored for generative AI pre-training, optimization, distillation or fine-tuning; orchestration of multi-model and/or agent AI workflows; or seamless deployment of optimized inference engines including RAG, Quansight has the experience to make AI’s potential a reality. We can help you bring the “magic” you see in public demos into your infrastructure, on your data, without sacrificing security or privacy.
We guide companies through the AI transformation, distilling research into reliable, integrated, and governed AI applications that will truly support your mission and transformation.
The upfront effort to create a solid packaging and environment foundation will pay off immensely in maintainability and avoiding technical debt down the line.
- Robust, reproducible environments using proper package managers (conda, Nix, etc)
- Pre-configured for AI workflows like model fine-tuning and optimized inference
- Sustainable foundations to avoid Python packaging issues
- Tooling for multi-model orchestration and task decomposition
- Integration with knowledge sources and external tools
- Long-term memory management for AI agents
- Reproducibility, scalability, performance optimization
- Hardening research code for production operations
- AI/ML model deployment, monitoring, governance
- Leverage open source for cost-effective, flexible AI
- Expertise in open AI frameworks and tools
- Commercial support and services around open source
