Research
Research Lab
Internal applied research that ships into production. Research artifacts improve evaluation, reliability, cost efficiency, and domain adaptation across all deployments.
Mission
Our research program focuses on applied problems that directly improve client outcomes. We don't train foundational models—we build evaluation frameworks, reliability patterns, cost optimization methods, and domain adaptation techniques that accelerate delivery and improve quality for every deployment.
Research Principles
- Research must produce deployable artifacts, not just papers
- Focus on problems that impact multiple client deployments
- Measure impact in terms of speed, quality, cost, and risk reduction
- Continuously integrate research findings into client work
Research Tracks
Evaluation Science & Benchmarking
Developing evaluation harnesses, benchmark suites, and quality measurement frameworks that catch regressions and validate improvements.
- Task-specific evaluation suites
- Regression test frameworks
- Quality metric standardization
- Red-team playbooks
Reliability & Cost Efficiency
Patterns and methods for improving system reliability, reducing costs, and optimizing model selection and routing.
- Routing and caching patterns
- Failure-mode catalogs
- Cost optimization strategies
- Model mix optimization
Domain Adaptation & Data Strategy
Techniques for adapting AI systems to specific domains, improving data efficiency, and accelerating fine-tuning when needed.
- Fine-tune feasibility methods
- Synthetic data protocols
- Domain-specific evaluation
- Data labeling strategies
Research Artifacts We Maintain
These artifacts are continuously improved and integrated into client deployments:
Evaluation & Testing
- Evaluation harness templates
- Benchmark suites for common use cases
- Red-team playbooks and safety tests
- Regression test frameworks
Reliability & Performance
- Routing and caching patterns
- Failure-mode catalogs and mitigation strategies
- Cost optimization playbooks
- Model selection frameworks
Domain Adaptation
- Fine-tune feasibility assessment methods
- Synthetic data generation protocols
- Domain-specific evaluation approaches
- Data labeling and curation strategies
Operations
- Monitoring and alerting patterns
- A/B testing frameworks
- Incident response playbooks
- Executive readout templates
How Research Improves Client Outcomes
Speed
Pre-built evaluation harnesses, benchmark suites, and patterns accelerate delivery. Clients don't wait for us to build evaluation frameworks from scratch.
Quality
Research-backed evaluation methods and red-team playbooks catch issues early. Quality gates prevent regressions and ensure consistent standards.
Cost
Cost optimization patterns, routing strategies, and model selection frameworks reduce operational costs while maintaining quality.
Risk
Failure-mode catalogs, mitigation strategies, and safety testing reduce risk. Clients benefit from lessons learned across all deployments.
Interested in our research approach?
Book a call to learn how our research program can accelerate your AI deployment.