Production-grade GPU inference engine, composable across domains — from medical imaging to broadcast and forensics.
If your inference path runs through a hospital, a broadcast control room, or a forensic lab — the cost of "almost real-time" is too high.
FrogRT is built for these teams.
Building diagnostic tools where clinicians expect zero perceptible latency, often under regulatory timelines (FDA / CE / KFDA).
Operating SDI/IP pipelines that need AI overlays, automation, or analytics without dropping a single frame.
Processing high-volume video evidence on-prem or air-gapped, with chain-of-custody and reproducibility constraints.
Not built for: batch-only workloads, prototype-only ML teams without production targets, or teams who can wait for "the next quarter".
Most teams rebuild their inference stack for every new domain. We didn't.
FrogRT is a composable engine: pick the modules you need, deploy from edge to multi-GPU server with the same codebase.
Replaces ad-hoc inference servers with a TensorRT-optimized engine you can drop into existing pipelines.
I/O: Frame in → detection / segmentation / tracking out
Replaces glue-code FFmpeg setups with a unified ingestion layer for SDI, RTSP, files, and live broadcast feeds.
I/O: SDI / RTSP / file in → decoded frames out
Replaces hand-rolled GPU schedulers with adaptive batching and queue control across heterogeneous GPU pools.
I/O: Inference requests in → batched, scheduled jobs out
Replaces parallel cloud-vs-edge codebases with a single artifact retargeted from RTX 5090 down to Jetson Orin.
I/O: Trained model in → optimized edge runtime out
Domain-specific inference systems are expensive to build and even more expensive to maintain.
We learned this the hard way — building real-time inference for medical imaging, video forensics, and broadcast. The patterns repeated. The code didn't have to.
FrogRT is the result: a modular engine where each module has been hardened in production before being made composable.
Validated components, no rebuilding from scratch.
Production-tested in the most demanding domains.
Edge to cloud, same code, same engineering team.
Each has been deployed in production, in domains where failure is not an option.
Customer names are confidential. Domains and module usage are public.
| Medical AI | Real-time endoscopy lesion analysis | Vision · Stream · Orchestration |
| Video Forensics | GPU-accelerated forensic video processing | Vision · Stream |
| Broadcast | Real-time SDI capture & analysis pipeline | Stream · Orchestration |
Our own products run on the same engine.
Content automation pipeline
Creator analytics platform
We use our own engine. It's the most honest validation we can offer.
FrogRT runs on a wide hardware matrix — from Jetson Orin at the edge to multi-GPU RTX 5090 servers.
We chose Rust for orchestration after benchmarking three alternatives — predictable memory, no GC pauses under load. TensorRT is non-negotiable for latency-critical paths. FFmpeg + NVDEC stays for one reason: it is the most battle-tested decode stack in production. The point is not the stack itself — it is that every choice has a measurable reason behind it.
20+ years in full-stack engineering. Deep domain experience in GPU computing, AI inference, and real-time media systems across medical, forensics, and broadcast.
We work with teams building real-time AI products where latency, reliability, and domain depth matter.
For pilots, partnerships, and engineering discussions:
jason@bigfrog.krBigFrog Inc. (주식회사 빅프로그) · Seoul, South Korea