Design Scalable R&D Workflow Architecture
Budget: $30 – $250 USD
Our deep-tech effort blends Monte Carlo physics simulations, custom detector hardware, data acquisition electronics, and reconstruction algorithms into a single product line. To move smoothly from R&D prototypes to production deployments, I need a well-structured pipeline that everyone can understand and extend.
The immediate focus is stage definition and documentation. I want each step—simulation, hardware build, bench testing, data collection, algorithm refinement, validation, and release—clearly mapped with entry/exit criteria, interfaces, and expected artifacts. The resulting blueprint must let engineers trace every revision, and let investors or partners grasp the big picture without wading through code.
System snapshot for context
• Simulations generate virtual datasets and design hints.
• Hardware detectors are iterated, characterised, and calibrated.
• Data flows into analytics and reconstruction code for verification.
• Feedback loops drive the next simulation or hardware spin.
What I need from you
- A high-level-to-low-level architecture diagram that captures the entire workflow.
- A stage & artifact catalogue (Markdown or similar) detailing purpose, inputs/outputs, format specs, and versioning strategy.
- Guidance on automation hooks—e.g., how CI/CD, Docker, DVC, or hardware-in-loop tests can sit at each gate—so we minimise manual hand-offs.
Acceptance criteria
• Every decision path is represented both visually and textually.
• Interfaces state data, format, or protocol expectations unambiguously.
• Reproducibility covers code, simulation parameters, hardware revisions, and test results.
• Non-technical stakeholders can follow the high-level flow in under five minutes.
This foundation should scale cleanly as new use cases arrive, sparing us ad-hoc redesigns while preserving traceability from concept to deployed device.
The immediate focus is stage definition and documentation. I want each step—simulation, hardware build, bench testing, data collection, algorithm refinement, validation, and release—clearly mapped with entry/exit criteria, interfaces, and expected artifacts. The resulting blueprint must let engineers trace every revision, and let investors or partners grasp the big picture without wading through code.
System snapshot for context
• Simulations generate virtual datasets and design hints.
• Hardware detectors are iterated, characterised, and calibrated.
• Data flows into analytics and reconstruction code for verification.
• Feedback loops drive the next simulation or hardware spin.
What I need from you
- A high-level-to-low-level architecture diagram that captures the entire workflow.
- A stage & artifact catalogue (Markdown or similar) detailing purpose, inputs/outputs, format specs, and versioning strategy.
- Guidance on automation hooks—e.g., how CI/CD, Docker, DVC, or hardware-in-loop tests can sit at each gate—so we minimise manual hand-offs.
Acceptance criteria
• Every decision path is represented both visually and textually.
• Interfaces state data, format, or protocol expectations unambiguously.
• Reproducibility covers code, simulation parameters, hardware revisions, and test results.
• Non-technical stakeholders can follow the high-level flow in under five minutes.
This foundation should scale cleanly as new use cases arrive, sparing us ad-hoc redesigns while preserving traceability from concept to deployed device.