The right engine is the one that meets your requirements.
We build Smart Data Engine to separate your application's data model from its physical storage. Our work starts with access patterns, business constraints and hardware. We use existing engines where they fit, and build specialised ones where a measured requirement calls for it.
Tell Us About Your WorkloadOur L2 orderbook engine is public: C++20 source, tests, benchmark tools and documented limitations. Inspect the implementation, reproduce the measurements and see where the trade-offs are. That is the standard we bring to a scoped client project.
Results depend on hardware and workload. The public comparison documents its methods and limitations; it is not a performance guarantee for your application.
Read the code on GitHub (opens in a new tab)To cut the hardware and the energy it takes to store and process data at scale. Most of that waste comes from running a workload on an engine that was never meant for it. Sometimes the fix is moving it to the right engine and running that one properly. Sometimes it is a new engine. Either way the goal is the same: the same data, fewer servers, less power.
And when you need neither, you hear that in the first call. If your bottleneck is a missing index, or one PostgreSQL instance still has years of room in it, saying so costs us a project and saves you a bad one.
We start from your data and your hardware, not from a template. AI helps us find optimisations a person would miss, and every claim is checked against a benchmark you picked.
Before anything is deployed or written, we look at the shape of your data, your access patterns and the hardware you run on. If you need neither product, we say so.
Throughput, latency percentiles, storage footprint, energy draw. You pick the numbers, on your workload, so the result can be checked instead of argued about.
Either the right existing engine, deployed and tuned on your infrastructure, or a new one written for your workload. Both are verified against the benchmark from step two.
Managed means we keep it fast, patched and backed up, and you get the latency graph. Handed over means you get the engine, the benchmarks, the test suite and the documentation.
Each one is checkable. That is the point of writing them down.
We let our work speak for itself. Deep technical excellence is our foundation, not flashy promises.
The engine is public. Judge the code, not the copy.
Every optimization we deliver can be benchmarked and verified. Performance gains you can measure, not just claims on a slide deck.
We agree the benchmark with you before anything is deployed or written.
Nothing here starts from a template. Which engine you get is decided by your data and your access patterns, whether we choose it or write it.
If your workload does not need us at all, you hear it in the first call.
Engineering-led, based in Warsaw. A direct conversation about the system you need to build.
CEO & Founder
I build data systems for trading workloads, where latency and correctness need evidence. Smart Data Engines applies that approach to storage: define the workload, measure the baseline, then prove the change. The public orderbook engine is one example of that work.
