Building production AI systems that extract real value.
GinkgoQ focuses on the engineering work around modern AI models: extraction, retrieval, validation, agents, observability, and evaluation. We publish practical system design notes for teams moving from prototypes to reliable production workflows.
Our approach
We focus on the gap between benchmark performance and production reality. Building AI systems that work requires more than picking the right model—it demands extraction pipelines, validation layers, evaluation frameworks, and architecture decisions grounded in real-world constraints.
We share what we learn through detailed technical writing and partner with teams facing genuine complexity in their AI systems.
What we focus on
- Building validation pipelines
- Designing AI system architecture for production deployment
- Evaluation frameworks and reliability benchmarks
- Domain-specific intelligence and context integration
- Real-world challenges in retrieval, reasoning, and automation
How we share knowledge
Our blog offers in-depth technical guides and case studies from the field. We write about what works, what doesn't, and the engineering decisions behind production AI systems. Research Notes provide focused technical analysis on specific challenges and system design patterns.
Working together
We collaborate with teams solving real problems in production AI—whether that's designing extraction pipelines, building evaluation frameworks, optimizing system architecture, or developing datasets for specialized domains.
If you're working on a challenging AI problem that requires hands-on technical depth, let's connect.
Get in touch
For collaborations, technical discussions, or questions about our work.