We invest a portion of our time in applied AI research. Not because we sell research, but because the teams that ship the best production systems are the ones building research-grade tools internally.
Status across the lab: in development. Not products available for purchase.
What's in the lab
Multi-tenant AI orchestration with custom model routing.
Looks at how teams can run multiple AI workflows across model providers without re-architecting when a model is swapped, deprecated, or repriced. The patterns inform the production-engagement work we do for AI-native clients.
Collapses the gap between research papers and running ML prototypes.
Targets the workflow where a team reads a paper at 9am and wants a working baseline by end of day, without rebuilding the supporting infrastructure each time. Used internally to evaluate techniques before recommending them to clients.
An empirical benchmark for structure-finding in tabular data.
Designed for publication on arXiv. The proof that we don't just deploy AI for clients. We measure how it actually behaves under controlled conditions, and we publish the results so the field can build on them.
Why we invest in research
Centenum Labs is not a product line. It is the part of the firm that keeps client work ahead of the curve. The patterns we explore here (multi-tenant orchestration, paper-to-prototype workflows, evaluation benchmarks) show up in the production systems we ship for clients six to twelve months later.
Tell us what has to be true. We will tell you what we'd build, or whether we're the right team.
We respond within 24 hours.