Customers — Production Deployments
Four named enterprise deployments. Measured outcomes. No logo wall.
FGCV ships in production at global manufacturers and logistics operators where a missed defect or a silent model drift is unacceptable. Below: four deployments, each annotated with the stack used, the runtime region, and a single quantified result — then the cross-customer benchmarks that aggregate 312 deployments in 2024.
Four production deployments, indexed.
Each row is a single environment: the modules shipped, the runtime region, the headline metric. No marketing copy — the stack sidebar speaks for itself.
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Defect detection on PCBA lines
From stitched-together scripts to a single versioned pipeline.
Siemens’ CV team replaced an in-house stack of training scripts, a third-party label tool, and a custom inference service with FGCV’s label-to-edge pipeline. Defect taxonomy now lives in a versioned ontology, and the active learning loop surfaces only the ambiguous frames to human reviewers — cutting annotation spend across the PCBA lines.
Result 71% reduction in labeling cost on the manufacturing defect dataset. -
Weld inspection across 3 plants
One model, three plants — shipped in a quarter, not six months.
BMW’s quality group needed a single weld-inspection model deployed across three European plants with differing camera rigs and lighting. FGCV’s evaluation harness produced per-site regression scores before every rollout, and the edge runtime kept inference predictable on shared T4 hardware. Integration timelines compressed because the deployment artifact is the same one the team already reviews in staging.
Result Time-to-production cut by 4.8x versus the prior in-house MLOps stack. -
Container ID & damage OCR at terminal gates
38 ms median latency at the gate — on the same Jetson the budget already approved.
Maersk runs container-ID and damage-classification inference on edge hardware at terminal gates across Singapore. FGCV’s X1 runtime delivered the latency headroom the team needed to retire a separate TF Serving cluster; container ID reads dropped from a multi-stage pipeline to a single forward pass. Monitoring catches drift per gate so the ops team can re-route reviewers before errors compound.
Result 38 ms median inference latency on Jetson Orin, benchmarked against TF Serving on equivalent hardware. -
Planogram & shelf compliance at store edge
A versioned CV platform that store ops can actually trust.
7-Eleven’s retail-CV team needed shelf and planogram-compliance models that could be retrained weekly across thousands of stores without an integration project each cycle. FGCV’s dataset versioning, evaluation gates, and edge runtime made each release a routine change — not a six-month integration project. Drift monitors flag stores whose camera exposure has shifted before compliance scores degrade.
Result Weekly retraining cadence with zero dedicated integration engineering per release.
FGCV does not publish a logo wall. We publish the four named deployments we have full permission to detail, plus the aggregate benchmarks across all 2024 customers on the section below.
Quantified across 312 enterprise deployments in 2024.
Cross-customer proof points that aggregate the four deployments above — and the 308 behind them. Every number is sourced from the FGCV 2024 deployment review.
Want the stack sidebar for your environment? Bring a sample dataset — we’ll show you the pipeline.
What an FGCV production stack looks like from labeled dataset to monitored GPU inference.
Every case study below runs on the same four-stage topology. Read it once, then map the Siemens, BMW, Maersk, and 7-Eleven outcomes onto your own environment — data ingestion, training, edge runtime, and monitoring are the levers that determine your time-to-production.
Labeled dataset
Multi-modal annotation across detection, segmentation, tracking, and OCR — with built-in active learning loops that cut labeling cost by 71% on manufacturing defect sets.
Training & versioning
Versioned datasets, model lineage, and reproducible runs — measured at 4.8x faster time-to-production across 312 deployments in 2024 versus in-house MLOps stacks.
Edge runtime
FGCV-X1 edge runtime delivers 38 ms median inference latency on Jetson Orin and T4 hardware, benchmarked against NVIDIA Triton and TF Serving on equivalent targets.
Monitored inference
Drift detection, data integrity checks, and reviewer queues — designed for teams that cannot afford a model drift incident or a missed defect in production.
Get a 30-minute technical demo mapped to your stack, your region, and your latency targets.
Walk through your labeled dataset, target hardware, and inference budget with an FGCV solutions engineer. We'll come back with a written deployment plan, a reference architecture, and the measured latency you can expect on your own hardware — no slideware.
- Stack review with a solutions engineer (not an SDR)
- Region-specific deployment: cloud, on-prem, or air-gapped
- Latency benchmark on your Jetson Orin or T4 target hardware
Book a technical demo
Bring a model, a dataset, or just a hardware target. We'll show you the same four-stage topology running in your environment.