Matching & optimisation
Turn a messy matching or allocation problem — inventory, logistics, scheduling — into a model that makes a decision.
← ML & data platforms portfolio
·ML & data platforms · reference example
Media AI at scale — like Sieve
Sieve is video and media AI processing infrastructure. It's a recognisable benchmark for a system class I build — here's what it does, and how I'd build yours.
Reference example — not my client. Sieve is an independent product I'm not affiliated with, shown as a benchmark for this kind of build.
Live preview of www.sievedata.com — their site, shown as reference.
01What a Sieve-class system does
The shape of the build
At its core, Sieve is video and media AI processing infrastructure — the kind of system you reach for when you have media pipelines and batch inference at scale. Here is how I'd build one for you.
Fraud & identity scoring
Risk and verification models tuned for the false-positive / false-negative trade-off that fits your business.
Calculation & data engines
The pipelines and calculation logic behind analytics and reporting platforms — correct, auditable, repeatable.
Batch inference at scale
Run models over large volumes of media or records reliably, with the infrastructure to make it cheap.
02More ML & data platforms examples
Other benchmarks in this class
Same system class, different products. Each opens a page like this one.
Want something like Sieve?
Tell me what it needs to do and where it's getting stuck. I'll tell you honestly whether I'm the right person and what it would take — no pitch deck, no discovery call to book a discovery call.