Your CV PoC Works. We Turn It Into a Production System — and Leave Your Team Able to Run It.
You've got a working model. We build the glass-to-glass pipeline around it — live cameras in, optimized inference on edge and cloud GPUs, results out — fast, efficient, and built to scale. Then we hand it over so your own researchers can change and extend it. Fixed scope, one to two months, from the team behind the open-source Savant video-analytics framework.
Fixed scope · 1–2 months · from $12,000 · you own it and your team can run it.
Your PoC Works. It Just Doesn't Ship.
A working PoC and a production system are two different disciplines. The model your ML engineer validated runs on recorded clips, in FP32, on one stream, on a workstation — and that was the right way to prove the idea. Production is a different problem: live camera streams, latency budgets, many streams per GPU, quantized models on the actual target hardware, and a service that runs unattended for weeks.
Pulling your researcher onto that work stalls the research they're actually good at — and pipeline, GPU, and deployment engineering isn't the same skill set anyway. That's the part we do — and the part we teach your team to own.
From Notebook to Glass-to-Glass, in Production
Recorded clips→Live streams
A real-time pipeline ingesting RTSP/RTMP camera feeds, built on GStreamer/DeepStream — decode, batch, infer, track, output — inside a defined glass-to-glass latency budget.
FP32 PyTorch→Optimized inference
TensorRT / ONNX Runtime with INT8 or FP16 quantization, calibrated to hold accuracy within a target you agree up front.
One stream→Many
Batched, multi-stream execution with high, measured GPU utilization — so cost per stream drops instead of scaling linearly with cameras.
Works on my machine→Your target hardware
Deployed and benchmarked where it actually runs: Jetson at the edge, L4 / A10 / other GPUs in the cloud, or a hybrid of both.
A script you babysit→A service that runs itself
Stream reconnection, failure recovery, stable memory over long runs, and metrics you can watch.
Fixed→Dynamic
Add and drop streams at runtime; scale across GPUs and nodes as you grow.
Concrete Deliverables — Including the Knowledge to Own It
- A deployed glass-to-glass pipeline running your model on your target hardware.
- Optimized, quantized models with measured accuracy retention against your PoC baseline.
- A benchmark report: end-to-end latency, throughput, streams per GPU, and cost per stream.
- Containerized, reproducible deployment — not a one-off box we configured by hand.
- Knowledge transfer, built into the scope. Working sessions with your researcher through the build — so the pipeline architecture, the optimization and quantization workflow, and the deployment setup aren't a black box.
- Handover: architecture docs and an operations runbook, so your team can run, change, and extend the system without us.
Fixed scope. One to two months. From $12,000. You own everything we ship.
Your Team Leaves Able to Own It
You have a researcher for a reason — the point isn't to make you dependent on us. We build alongside your team and hand over a system they understand: how the pipeline is put together, how the models are optimized and quantized, how it deploys. Routine changes — new model versions, tuning, added streams — stay in-house, on your schedule.
Because we author the Savant framework the work is built on, we can hand your team the real thing — not a wrapper they can't see into.
Fixed Scope, Built With Your Team
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1
Technical scoping call
We review your PoC, target hardware, stream count, and latency and accuracy targets.
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2
Fixed-scope proposal
A defined deliverable, timeline, and price before you commit — no open-ended billing.
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3
We build — with your researcher
Pipeline, model optimization, deployment, benchmarking, in the open, with your team involved so the knowledge transfers as we go.
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4
Benchmarked handover
Deployed system, benchmark report against your targets, and the docs and sessions your team needs to run it themselves.
They Had a Model That Worked. It Kept Crashing.
A California retail loss-prevention startup had built a working shoplifting-detection pipeline around an action-recognition model. The model performed — but it wasn't a product. Running several cameras per store, around the clock, GPU memory grew until the system crashed. Fine for a demo; unusable in a shop.
We took their pipeline and made it production-grade — fixing the GPU-memory growth behind the crashes with practical heuristics and tighter memory management — so it now runs multiple cameras per store, 24/7, without falling over.
A working model that crashed under real load → multiple cameras per store, running 24/7, stable in production.
Your ML Engineer Built the Model. We Build the Machine That Runs It in Production.
Real-time production video pipelines are what we do. We build Savant, the open-source framework for exactly this — real-time video analytics — so you can read the code that underpins our work, and so we can teach your team the actual technology rather than a black box. We run computer vision in production across 600+ devices on four continents, on NVIDIA Jetson at the edge and GPUs in the cloud. The team is ~10 engineers, including PhDs, and our day job is the hard part of shipping CV: streaming pipelines, GPU throughput, model optimization, and systems that stay up.
Who It's For — and Who It Isn't
This is for you if
- You have a validated PoC or working model, and you need it as a production system.
- It has to run in real time, on live cameras, at more than one stream — on edge, cloud GPUs, or both.
- You have a researcher or ML engineer you'd like brought up to speed to own the result.
This probably isn't a fit if
- You haven't validated the approach yet — start with a feasibility sprint instead.
- It's a batch or offline job with no latency, throughput, or scale constraints — plenty of teams can do that; you don't need us.
For the Routine — Your Team. For the Hard Parts — Us.
Once your team owns the system, day-to-day evolution is theirs — that's what the knowledge transfer is for. But some work stays specialist: squeezing more streams onto a GPU, optimizing a new model architecture, re-architecting as you scale by an order of magnitude. We stay available for exactly that — as an on-call CV/ML engineering partner, not a dependency you're stuck with. You come back because it's worth it, not because you have to.
Questions Technical Buyers Ask Before the Call
Who owns the IP?
You do. You own the production system and optimized models we deliver. The work builds on our open-source Savant framework, which you keep using freely.
Will we be locked in?
No — the opposite is a deliberate part of the offer. We build alongside your researcher and hand over a system your team understands and can change. We stay available for specialist work, but you are never stuck with us for routine changes.
How does the knowledge transfer actually work?
Your researcher works with us through the build — not a lecture at the end. By handover they know the pipeline, the optimization and quantization workflow, and the deployment, and they have the docs and runbook to go with it.
Will it run on our target hardware?
That's the point of the scoping call — we confirm the target (Jetson, specific cloud GPUs, or hybrid) and design to it. We don't start until the target is defined.
How much accuracy do we lose to quantization?
We set an accuracy-retention target with you up front and calibrate to hold it. If a target isn't reachable, we tell you before you commit, not after.
Our PoC is messy research code — is that a problem?
No. We expect research-grade code. We rebuild the pipeline around your model; we don't need your PoC to be pretty, just to work and to be reproducible.
Is the fixed price actually fixed?
Yes, for the scope we agree. If you later expand scope — more streams, another hardware target, new features — we requote that separately.
What do you need from us to start?
Your model and weights, representative sample data, your target hardware and scale, and access to the researcher who'll own it afterward.
How much beyond $12,000?
$12,000 is the floor for a focused, single-model, single-target project. Most projects run $12,000–$30,000 depending on stream count, target hardware, and how deep the optimization goes. We quote a fixed number before you commit.
Get Your PoC to Production — and Your Team Ready to Run It
Book a technical scoping call. We'll review your PoC, your target, and your targets — and come back with a fixed scope, timeline, and price.
Not validated yet? Start with a CV feasibility sprint.