Computer Vision Engineering
From real-time defect detection on the factory floor to medical-grade diagnostic assistants, we build vision systems that meet the precision, latency and compliance bars enterprises demand.
Vision systems that earn their place on the assembly line.
Computer vision in a deck looks easy. Computer vision in production has to handle bad lighting, dirty cameras, novel SKUs, edge-case failures and a regulator who wants an audit trail. Our approach treats those realities as design constraints, not afterthoughts. We build pipelines that hold their accuracy under field conditions, run offline on cost-constrained hardware and respect privacy by processing on-device whenever the use case allows.
Engineered for the field
We benchmark on your actual cameras and conditions, not on academic datasets. The number that matters is the one in production.
Edge-first when it matters
For privacy, latency and cost, we deploy on Jetson, Coral, NPUs and ARM CPUs, not in the cloud by default.
Active-learning built in
Every uncertain frame becomes tomorrow's training data. The model gets smarter the longer it runs.
Why CV moved from R&D to operations.
Foundation models, cheap edge accelerators and post-training quantization have collapsed the cost of production CV by an order of magnitude. The use cases are no longer experimental.
Jetson Orin and similar accelerators now run real-time CV models at watts that would have required a server farm three years ago.
Annotation, historically the chokepoint, is now bootstrappable with foundation models. Cold-start projects ship in weeks, not quarters.
McKinsey 2025. From defect detection to safety analytics, CV has moved from "innovation lab" to "operations roadmap".
Computer Vision services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Object detection & segmentation
YOLO, DETR, SAM-based pipelines tuned on your data, counting, tracking, defect-detection, all with the precision/recall trade-off explicit and auditable.
OCR & document AI
Layout-aware extraction from PDFs, forms, scanned receipts, multi-language documents, with confidence scores you can route on.
Video analytics
Real-time inference on RTSP/WebRTC streams: footfall counting, queue analytics, safety violations, anomalous behavior detection.
Medical & life-sciences imaging
Compliant pipelines for radiology triage, pathology slide analysis and clinical trial endpoint quantification, built with HIPAA/GDPR/MHRA in mind.
Edge & embedded deployment
Quantized, pruned, distilled models targeting Jetson, Coral, NPUs and ARM CPUs, running offline at the camera, not in the cloud.
Active-learning loops
Confidence-based human review with auto-labeling assist, every uncertain frame becomes tomorrow's training data.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Defect detection at 60 FPS on the assembly line
Replaced a 12-person QC station with edge-deployed CV catching 99.4% of defects at 4× the throughput, humans now triage edge cases only.
Six reasons enterprises run Computer Vision with Infivit.
Built for the 2026 reality of Computer Vision: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic AI talking points.
Sub-30ms inference on Jetson, Coral, or x86.
Models quantized, pruned and compiled for the hardware you actually deploy on. Frame drops become a memory, not a metric your QA team tracks.
99%+ accuracy, including the hard 1%.
The hard 1% is where projects fail. We build active-learning loops that find your model's blind spots and fix them iteratively, every week.
Labelling spend cut 70% with foundation models.
Use SAM, Grounding DINO and CLIP as the first-pass annotator. Humans review only edge cases. Dataset costs collapse, accuracy doesn't.
Generate the failures real data never captured.
Synthetic data pipelines for safety-critical and rare-class scenarios. Train on the 1-in-10,000 events you can't afford to wait around for.
On-device inference, blur-at-source.
PII never leaves the camera. Federated training and on-device inference for healthcare, retail and consumer applications, with regulatory paperwork to match.
Coordinate identity across 100+ feeds.
Re-identification, trajectory prediction and event correlation across hundreds of cameras with sub-second cross-stream latency. Built for real deployments, not lab demos.
The questions you were already going to ask.
Got a computer vision problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.