Potamus | Making heavy operations feel light

Problems I solve

"Our data scientists can't deploy without data engineering help"

I build self-service model lifecycle frameworks. At Bolt, the DS team doubled their project count in three months without DE involvement -- because the platform replaced the gatekeeper pattern with a shared interface. Good tooling multiplies everything your team does.

Result: DS team experiments freely. DE team focuses on infrastructure, not babysitting.

"Our builds take forever and break constantly"

I've cut build times from 40 minutes to 5, optimised Docker images, fixed flaky CI pipelines, and built autoscaling build clusters. Whether it's ML training jobs, C++ compilation, or Gradle builds -- slow builds are a solvable problem.

Result: 8x faster builds. Engineers ship instead of waiting.

"Our infrastructure costs are growing faster than revenue"

I systematically audit and optimise compute, storage, and processing costs. RAM reduced by 90%. Cloud spend per workload cut 30-90%. Most companies are massively over-provisioned and don't know it.

Result: Typical 30-90% cost reduction without degrading performance.

"We built fast. Now the shortcuts are strangling us."

You grew on MVPs and duct tape. It worked. Now you have 50 models, 3 CI systems, and tests nobody trusts. I've been through the 10-to-1,000 journey. I know which foundations to lay before the next 10x, and which shortcuts to keep.

Result: Architecture that survives the next 10x without a rewrite.

"Our engineers spend more time on operations than engineering"

Automated training pipelines, self-service deployment, reliable CI, proper test suites. Retraining goes from quarterly manual babysitting to daily automated runs. Engineers get back to building.

Result: One pricing model went from half a dozen cities to several hundred, with daily retraining instead of quarterly. 9-figure revenue impact.

How I work

Engineering Operations Review

1-2 weeks

How your team builds, tests, deploys, and operates -- assessed against what works at scale. Whether you have too much tooling or none at all, you get a prioritised roadmap of what to fix first.

Architecture Review

2-3 weeks

Pipeline and build system design, build vs buy analysis, implementation plan. Get the architecture right before committing.

Cost Optimisation Sprint

1-2 weeks

Quick-win cost reductions in compute, storage, and builds. The 30-90% savings usually have equivalents in your setup. ROI within the first month.

Hands-On Retainer

3+ months

Embedded alongside your team: pipeline design, build tooling, CI/CD, cost optimisation, and knowledge transfer. I build the foundations, then your team owns them.

About

I'm Mark Cowan, founder of Potamus OÜ, based in Estonia.

For nearly 5 years at Bolt (one of Europe's largest tech companies), I built the ML pipelines, automation, and data-science tooling that were part of their Model Lifecycle platform -- scaling the operation from a handful of people manually deploying a few models to 30+ data scientists independently managing 200+ ML projects. I also built the CI infrastructure, optimised build pipelines, and made engineering teams more efficient across the stack.

Before Bolt, I built satellite flight software at Open Cosmos (code that flew in space) and led teams of up to 11 engineers. As CTO at a defence tech startup, I architected flight software running real-time computer vision on hardware 80% cheaper than competitors, supporting multiple hardware platforms. I'm an Entrepreneur First alumnus (London, Cohort 5) and hold a patent for satellite payload development.

I focus on making engineering operations work: the pipeline, the build, the deployment, the cost, the reliability. From cloud ML platforms to hardware-in-the-loop test systems -- the heavy stuff that slows everything else down.

Your product grew faster
than your operations
can handle.