Academic Algorithm Productionisation
Productionising research means taking an algorithm or prototype that works in a paper or a lab and engineering it into software that runs reliably, at scale, in the real world.
The problem
Research code is built to prove a point, not to run every day. It assumes clean inputs, a patient operator, and a machine that never fails. Production assumes none of those. Closing that gap is its own discipline.
Our approach
We work alongside the researchers to keep the science intact while we rebuild the engineering around it: reliability, scale, deployment, and a workflow real users can operate. We turned a neuromorphic speech algorithm into the ReadUp platform, and Whisper into WombatWords on Cerebrium. Architecture led by Dawid Loubser, development led by Graham Withey.
What you walk away with
Your research, running as production software you can deploy, operate, and scale, with the engineering decisions documented.
Led by
- Dawid Loubser, Architecture
- Graham Withey, Development
Proof
Common questions
- Will you change our algorithm?
- Only where production demands it, and never without you. The science stays yours.
- Do you deploy it too?
- We can take it through deployment and MLOps, or hand off a clean, documented build.
- What about models that must run offline or on-device?
- That is core to how we work; several of our systems run offline-first.
- Who owns the result?
- You do.