Turn every pixel into operating signal.
Vision models trained on your cameras, parts, and documents — shipped to the edge or the cloud with the evaluation, retraining, and integration layers that keep them honest in production.
Cameras everywhere. Analysts nowhere.
Your facilities, storefronts, and fleets already generate more visual data than a human team could review. Computer vision closes that gap — reading pixels at stream speed so the operator downstream gets a signal, not a video wall. It sits alongside our wider artificial intelligence practice and plugs directly into the integration layer you already run.
From pixel to operator event, at the speed the moment demands.
Samples are enough for production accuracy on most detection tasks — fine-tuned from foundation models.
On-device when latency or privacy demand it. Elastic GPU when they don't.
From pixels to production.
Image classification, video analytics, facial recognition, visual search, line-side defect detection, and document intelligence — the full vision stack under one roof, with the MLOps discipline to keep it working after launch.
Image recognition and object detection
Classify, localize, and track objects across photos and live feeds. Retail shelf audits, safety-gear detection, vehicle tracking, and document intelligence — all wired into the systems your teams already use.
Video analytics at stream speed
Parse hours of footage in minutes. Anomaly detection, dwell-time analysis, license-plate recognition, and motion fingerprinting on RTSP, Hls, or native camera SDKs.
Facial recognition and identity
Frictionless access, attendance, and verification with liveness checks built in. Tunable confidence thresholds, on-device inference, and GDPR-aligned templates — never raw biometrics at rest.
Visual search and retrieval
Let customers point instead of type. Embedding-based visual search across catalogs, floorplans, or medical archives — ranked by similarity, filtered by business logic.
Defect detection on the line
Vision models trained on your parts, installed on your line. Sub-second pass/fail decisions at the PLC layer, with the reject reason captured for continuous retraining.
Document intelligence
OCR, layout parsing, and multimodal extraction across invoices, contracts, clinical notes, and claims. Structured JSON out, with confidence scores per field for human-in-the-loop review.
Patterns with receipts.
We lead with outcomes, not demos. These patterns have shipped across retail, manufacturing, public safety, and infrastructure — and they rhyme enough to scope a POC in a single call. Related reading: our case studies and rapid POC model.
- 01 · USE CASE
Retail personalization
Heat-mapped store traffic, planogram compliance, and pose-aware recommendation engines that lift engagement without intruding on shopper experience.
+25% engagement · +15% conversions - 02 · USE CASE
Smart surveillance
Real-time detection of loitering, intrusion, and abandoned objects across camera meshes — with alerts routed to the humans who can act.
Sub-second anomaly latency - 03 · USE CASE
Quality control in manufacturing
Defect classification on conveyor speed, trained from as few as 500 labeled examples. The model keeps learning from every reject.
−20% error rate - 04 · USE CASE
License-plate recognition
Automated gate access, toll collection, and fleet movement tracking. Works in rain, at night, and at highway speed with adaptive thresholds.
99%+ read accuracy - 05 · USE CASE
Customer sentiment in-store
Anonymous facial expression analytics for reaction studies and merchandising A/B tests. No identity stored — just signal.
Live dashboards - 06 · USE CASE
Safety and PPE compliance
Helmet, harness, and high-vis detection on construction and industrial sites — with automated reporting to EHS systems.
Shift-level audits
Vision is already earning its keep.
Each vertical has its own cadence, compliance posture, and camera density. We map the vision opportunity to those realities — not the other way around.
- 01 · INDUSTRY
Manufacturing
Defect detection on the line, worker safety analytics, and PLC-layer pass/fail decisions.
SEE MANUFACTURING WORK → - 02 · INDUSTRY
Retail
Shelf compliance, dwell-time heat maps, and anonymous sentiment for merchandising A/B tests.
SEE RETAIL WORK → - 03 · INDUSTRY
Healthcare
Imaging triage, document intelligence for claims, and access control for restricted zones.
SEE HEALTHCARE WORK → - 04 · INDUSTRY
Logistics
License-plate recognition, yard tracking, and container damage audits at dock speed.
SEE LOGISTICS WORK → - 05 · INDUSTRY
Construction
PPE compliance, site progress from drone footage, and equipment utilization.
SEE CONSTRUCTION WORK → - 06 · INDUSTRY
Finance
Document intelligence across KYC, claims, and mortgage workflows — structured JSON out.
SEE FINANCE WORK →
Delivery discipline, vision-first.
Fine-tuned models are the easy part. Everything around them — data pipelines, eval harnesses, edge deployment, and retraining — is where vision projects live or die.
Models trained on your data
Frontier vision architectures fine-tuned on the parts, faces, and environments that actually matter to your business — not a stock demo.
Ship to the edge or the cloud
NVIDIA Jetson, Coral, and RK3588 for on-device inference; GPU fleets for batch and training. We choose the layer that fits your latency and privacy budget.
Evaluation baked in
Every build comes with a labeled test set, a confusion matrix, and a retraining schedule. We know where the model is weak before you do.
Integration, not islands
Vision output lands in your ERP, WMS, POS, or CRM — as events, not dashboards. The insight reaches the operator who can act on it.
Privacy by default
On-device processing where possible, tokenized embeddings where not, and documented retention policies that pass legal review on the first pass.
Twenty-seven years of delivery
7,000+ projects, 3,000+ clients, 90+ countries. Vision sits inside that delivery discipline — not adjacent to it.
Model-agnostic by design.
Open-weight architectures, frontier APIs, and the edge runtimes that make them deployable. We pick the layer that fits the problem.
What leaders ask before they start.
01What is computer vision and how does it fit our operations?
02How much training data do we actually need?
03On-device or cloud — which is right for us?
04How do you handle privacy, consent, and bias?
05How long does a computer vision project take?
06Do you work on-prem and in air-gapped environments?
Your first vision POC, scoped this week.
Book a consultation. We'll map the cameras, the signal, and the operator workflow — and hand back a scoped POC before a contract is signed.