Scientific software & hardware R&D that ships.
O'Rourke Research blends applied machine learning, edge and IoT engineering, and cloud-scale data systems. We turn research-grade ideas into reliable products that respect hardware constraints, latency budgets, and business timelines.
ML/AI · Edge & embedded · IoT · Data/Cloud · Pipelines · Computational engineering
We pair research fluency with production discipline to reduce risk early.
Core services
Integrated software + hardware delivery
We enter at the point of highest uncertainty: translating research into code, pushing models onto new hardware, or re-architecting data flows so teams can iterate safely.
ML & AI Systems
Architectures, evaluation, and applied research that connect algorithms to measurable outcomes.
- Model design, simulation, and benchmarking
- Multimodal perception, signal + vision pipelines
- Model eval, guardrails, and responsible deployment
Edge, Embedded, IoT
Firmware-to-cloud fluency for latency-sensitive, power-aware, and mission-critical systems.
- TinyML + on-device inference under resource constraints
- RTOS, C/C++, Rust, and secure connectivity
- Sensor fusion, hardware-in-the-loop testing
Data, Cloud, Pipelines
Resilient data flows and compute fabrics that keep research moving from prototype to production.
- High-throughput ingestion and feature stores
- Streaming + batch orchestration with observability
- MLOps, reproducibility, and cost-aware scaling
Engagement modes
From applied research sprints to production launches.
We build blended teams that pair senior researchers with engineers who have shipped on bare metal and in the cloud. Each engagement starts with a decision memo outlining risks, constraints, and measurable outcomes.
Assurance
Build with evidenceEvery prototype and deployment ships with metrics and observability to keep loops tight.
We balance compute budgets, memory limits, and on-device reliability without compromising UX.
Clear decision records, risk registers, and handoff docs ensure continuity for your team.
Process
How we de-risk delivery
A pragmatic lifecycle keeps research momentum aligned with production timelines. Each phase produces artifacts your team can keep: proofs, benchmarks, and playbooks.
Constraint mapping, technical due diligence, and feasibility modeling.
Build and instrument fast experiments to validate algorithms or hardware targets.
Harden research code, close performance gaps, and prepare for deployment.
Launch to edge, cloud, or hybrid environments with observability and handoff.
Start a dialog
Tell us about the challenge.
We respond with a short perspective, risks we see, and suggested next steps.
RFP submission
Share the specs.
We align on scope, timelines, and the technical artifacts you need to evaluate us. Accounts are required for RFP submissions.