Computer Vision — ViT · Edge · OCR
Vision systems must run where the photons land. We engineer pipelines from sensor to decision — calibrated, monitored, and built to survive grease, glare and gigabits.
Everything that ships
- Annotation pipelineActive learning loop, quality audits, dataset versioning.
- Model training stackViT/YOLO/SAM fine-tunes, hyperparameter sweeps, eval suite.
- Edge runtimeONNX/TensorRT packaging, OTA updates, fleet telemetry.
- Drift monitoringEmbedding drift alarms, shadow inference, rollback triggers.
- Inspection UIOperator console with overrides, audit trail and KPI rollups.
- CV Engineer
- Edge SRE
- Annotation Lead
- Quality SME
fleet: line-7.cams
model: defect-vit-2.1.trt
device_class: jetson-orin-nx
slo:
p95_latency_ms: 38
recall_min: 0.98
ota:
channel: stable
rollback_on: drift>0.15Weeks 1–8 · first inference live by week 4
- 1Week 1-2Site survey + dataset
Sensor placement, lighting, first labelled set with QA.
- 2Week 3-4Model + edge runtime
Train, benchmark, package for target device, ship to one line.
- 3Week 5-8Fleet rollout
OTA pipeline, drift monitoring, operator console, scale to N.
Things prospects ask
Edge by default for latency and cost; cloud for retraining and aggregate analytics.
We bring active learning + auto-label models so you label the hard 5%, not the easy 95%.
Bring your own or we spec, source and provision Jetson/Coral/Hailo-class devices.
Stand up Computer Vision in Weeks 1–8.
We'll respond within one business day with a scoping note, a fixed-price outcome contract, and a named principal. Your details sync straight into our concierge queue.
- • Outcome-priced — no T&M.
- • Sovereign by default — your data, your region, your keys.
- • Wired into the Fuel Pressure gauge from day one.