Six engineering capabilities that we combine and tailor for every vision AI system — from the model that sees to the dashboard that decides. Each one is a discipline we practice end to end, not a product we resell.
- 01
Computer Vision & Deep Learning
Perception models built for your scenes, not a benchmark.
Custom detection, classification, segmentation, recognition and tracking systems.
Computer vision is the discipline of turning raw images and video into structured facts: where an object is, what class it belongs to, which pixels it occupies, whether it is the same object seen a moment ago. Deep learning supplies the models that make this possible under the viewpoint, lighting and clutter variation that defeats hand-written rules.
Explore Computer VisionCore capabilities- Object detection
Single- and multi-class detectors tuned for your object scale, camera geometry and latency budget, from real-time YOLO-family models to higher-accuracy two-stage architectures.
- Image classification
Fine-grained classifiers for product variants, surface conditions, scene states and other categories where the difference is subtle and the examples are limited.
- Semantic and instance segmentation
Pixel-level masks for measurement tasks: defect area, spill extent, lane boundaries, free space and anything else where shape carries meaning.
- Recognition and re-identification
Embedding models that match faces, products, vehicles or people across cameras and time, with privacy-preserving options where identification is not required.
- Multi-object tracking
Trackers that combine motion prediction with appearance features so identities persist through occlusion, crowding and camera handoff.
- 02
Intelligent Video Analytics
Turn hours of video into events people can act on.
Real-time video understanding, event detection, behavioral analysis and multi-camera analytics.
Intelligent video analytics is the layer above raw perception. Detection tells you a person is in the frame; analytics tells you that person crossed a restricted line at an unusual hour, lingered longer than expected, or appeared on three cameras in sequence. It combines detection, tracking and scene logic into events with business meaning.
Explore Intelligent Video AnalyticsCore capabilities- Real-time event detection
Line-crossing, zone intrusion, loitering, abandoned objects and direction-of-travel rules evaluated on tracked trajectories rather than raw pixel motion.
- Behavioral analysis
Dwell time, path analysis, group formation, running, falling and other patterns derived from how tracked objects move and interact over time.
- Crowd and occupancy analytics
Density estimation and occupancy counting for spaces where individual tracking breaks down, with threshold alerts for safety and capacity management.
- Multi-camera correlation
Re-identification and spatial calibration link observations across overlapping and non-overlapping cameras into a single site-level picture.
- Event pipeline and deduplication
Temporal smoothing, cooldown windows and cross-rule logic that prevent a single incident from generating dozens of alerts.
- 03
Edge AI & Embedded Intelligence
Inference at the camera, not in a distant data center.
Deploy optimized AI models directly on edge hardware for responsive local inference.
Edge AI means running inference on devices located where video is captured: a compute module in a cabinet beside the cameras, an industrial PC on a production line, a ruggedized box at a roadside. The alternative, streaming raw video to central servers, costs bandwidth, adds latency and moves sensitive footage off site.
Explore Edge AICore capabilities- Model optimization for edge accelerators
Export through ONNX, INT8 and FP16 quantization with calibration, layer fusion and pruning, with accuracy measured before and after so speed never silently costs correctness.
- Multi-stream inference pipelines
Hardware video decode, batched inference and per-camera scheduling that let a single device process many streams within its thermal and power budget.
- Embedded system integration
Integration with industrial I/O, PLCs, relays, serial devices and local networks so the edge device participates directly in the operation it observes.
- Local event processing and buffering
Rules, alerting and evidence capture run on the device, with store-and-forward buffering so events survive connectivity loss.
- Fleet deployment and remote management
Containerized services, over-the-air model and configuration updates, health telemetry and remote diagnostics for distributed deployments.
- 04
AI & Machine Learning
Learning systems that turn your data into decisions.
Develop custom machine learning pipelines, predictive systems and intelligent data solutions.
Machine learning is the broader discipline of building systems that improve from data. In a vision context it extends beyond perception: forecasting footfall from historical counts, predicting equipment issues from inspection trends, classifying events by risk, or recommending actions from patterns across sites.
Explore AI & Machine LearningCore capabilities- Custom ML pipeline development
Reproducible pipelines from raw data to trained model, with versioned datasets, feature stores and automated evaluation on every change.
- Predictive modeling and forecasting
Time-series and tabular models for demand, occupancy, throughput, maintenance and risk, built on both visual and operational data sources.
- Anomaly detection
Unsupervised and semi-supervised methods that flag unusual patterns in events, sensor readings or imagery when labeled failure examples are scarce.
- Multimodal data fusion
Combining camera-derived signals with IoT, transactional and scheduling data into unified features that no single source could provide.
- Model serving and MLOps
Models deployed as versioned FastAPI services with input validation, performance logging, A/B evaluation and rollback.
- 05
Industrial AI & Quality Inspection
Consistent inspection at line speed, every shift.
Automate visual inspection, anomaly detection, product counting and manufacturing intelligence.
Industrial AI applies computer vision and machine learning to manufacturing operations: detecting defects on surfaces and assemblies, verifying packaging and labeling, counting products, and monitoring the state of the line itself. The goal is consistent, documented quality at the speed the process demands.
Explore Industrial AICore capabilities- Surface and assembly defect detection
Segmentation and classification models for scratches, dents, contamination, missing components and misalignment, trained on your parts and defect taxonomy.
- Unsupervised anomaly detection
Models trained on good parts alone that flag deviations from normal appearance, essential when defects are rare, varied or not yet catalogued.
- Product counting and throughput monitoring
Line-crossing counts on tracked items, with handling for overlaps, gaps and conveyor speed changes, feeding production dashboards in real time.
- Packaging and label verification
OCR and detection to confirm label presence, legibility, correct variant, date codes and seal integrity before product leaves the line.
- Production-line state monitoring
Detecting stoppages, jams, missing operators, blocked zones and equipment states from existing or purpose-installed cameras.
- 06
Custom AI Engineering
Complete intelligent platforms, from model to interface.
Build customized AI applications, APIs, dashboards and end-to-end intelligent platforms.
Custom AI engineering is the work of turning a capability into a system that an organization can run: APIs that other software can call, dashboards that operators can use, storage that supports audit and analytics, deployment that survives updates, and the glue that connects all of it to existing infrastructure.
Explore Custom AI DevelopmentCore capabilities- AI application architecture
System design covering model serving, data flow, storage, security, scalability and failure handling, documented so your engineers can own it.
- Inference APIs and services
FastAPI services exposing models with typed schemas, authentication, rate limiting, versioning and structured logging.
- Operator dashboards and review tools
React interfaces for live monitoring, event review, annotation, configuration and reporting, designed around how operators actually work.
- Data platform and storage
PostgreSQL schemas for events, detections and audit trails, with retention policies, indexing for analytics and export to BI tools.
- Integration with existing systems
Connectors and adapters for video management systems, ERP, access control, messaging platforms and webhooks.
One Process. Every Service.
Whatever we build, it moves through the same five stages — so scope, risk and quality stay visible from the first conversation to the live system.
- 01
Discover — Understand the business challenge.
We start with the operational question, not the model. Site walkthroughs, sample footage and conversations with the people who will use the system define what success looks like and what constraints exist: lighting, camera placement, connectivity, privacy and integration.
- Problem definition
- Feasibility assessment on sample data
- Success criteria and constraints
- 02
Design — Define the architecture and AI approach.
We select the perception approach, the hardware target and the integration surface. Edge versus server placement, model candidates, data strategy and the event model are decided here, so the build phase has a clear target.
- System architecture
- Model and hardware selection
- Data and annotation plan
- 03
Develop — Build and validate intelligent systems.
Models are trained or adapted on representative data, pipelines are built around them, and the whole system is validated against the success criteria using held-out footage from the real environment.
- Trained and optimised models
- Inference pipeline and APIs
- Validation report on real footage
- 04
Deploy — Integrate and launch in real environments.
Containerised services are installed on edge devices or servers, connected to cameras and downstream systems, and run in a supervised pilot before going live.
- Deployed edge or server stack
- Integrations with existing systems
- Pilot review and acceptance
- 05
Optimize — Monitor, improve and scale.
Live systems are monitored for drift, false alarms and new conditions. Feedback loops retrain models, rules are tuned, and the deployment is extended to new cameras, sites and use cases.
- Monitoring and drift reports
- Model and rule updates
- Scale-out plan
Not Sure Which Service Fits?
Describe the environment, the cameras you have and the decision you want to improve. We will tell you honestly whether vision AI is the right tool and which capabilities it would take.