Research
Our research is applied engineering investigation. We study real delivery constraints and turn findings into reusable tools, patterns, and operational guidance.
Methodology
- Problem definition — Document the operational context and what “better” means.
- Hypothesis — Make a testable claim about improvement.
- Prototype — Build a small implementation to learn quickly.
- Evaluation — Measure against defined criteria with clear baselines.
- Iteration — Apply learnings and repeat with controlled changes.
Metrics Framework
- Accuracy — Correctness of outputs for target inputs
- Consistency — Stability across runs and conditions
- Traceability — Ability to explain why an output was produced
- Latency — Time from input to usable output
- Cost — Compute, time, and human review requirements
Research Directions
- Workflow orchestration and recovery patterns
- Document understanding and structured extraction
- Evaluation and benchmarking for AI-assisted systems
- Human-in-the-loop quality assurance
- Edge and on-prem deployment patterns
Outputs
- Internal tools and reusable components
- Evaluation harnesses and benchmark datasets
- Operational templates and implementation notes
- Delivery guidelines and guardrails