We build custom computer vision systems for object detection and classification, quality control and defect inspection, anomaly detection, real-time monitoring, document intelligence (OCR and form extraction), predictive maintenance through visual inspection, and safety compliance monitoring.

If your problem involves analysing visual data — images or video — we can likely help. Our technical assessment defines the right approach, architecture, and accuracy expectations specific to your operational environment before any development begins.

Production accuracy depends on your specific use case, data quality, and operating conditions. Our deployed systems typically achieve high accuracy for classification tasks and strong performance across complex detection scenarios. We design specifically for real-world challenges — poor lighting, occlusion, camera angle variations, and environmental factors that cause demo systems to break down in production.

Every project includes accuracy benchmarking against your actual conditions, not curated lab datasets. Clear performance thresholds are established during the assessment phase and validated through proof-of-concept testing before full development commits.

Data requirements vary by problem complexity. Simpler binary classification tasks require fewer labelled images, while multi-class object detection with rare edge cases requires significantly more. The key principle is quality over quantity — images must represent the actual scenarios your system will encounter, including edge cases and failure modes that matter most in production.

We can work with your existing image datasets, help design data collection protocols from scratch, or use synthetic data augmentation to reduce labelling effort where appropriate. Exact data requirements for your specific use case are defined during our technical assessment — we do not prescribe generic volumes before understanding your problem.

We deploy CV systems on edge devices (NVIDIA Jetson, Coral TPU, Intel Movidius, Raspberry Pi Industrial), cloud infrastructure (AWS, Azure, GCP), or hybrid architectures — the choice depends on your latency, privacy, and connectivity requirements.

Edge deployment is ideal for real-time monitoring with minimal latency, offline or air-gapped operation, and applications where data cannot leave the facility. Cloud deployment works well for batch processing, centralised analytics, and systems where sub-second response time is not required. Most industrial deployments use a hybrid approach — edge inference for real-time decisions, cloud for aggregation, analytics, and model management. We recommend the right architecture during the technical assessment.

Off-the-shelf tools — cloud APIs, SaaS platforms — work well for common use cases with standard datasets: general object detection, facial recognition, or basic OCR. They are the right starting point when your problem fits their training data and accuracy thresholds are not demanding.

Custom development becomes necessary when your use case is specialised (detecting specific defects, rare objects, or industry-specific scenarios), you need to operate under challenging conditions (poor lighting, extreme angles, unusual environments), you have strict latency or data privacy requirements, or existing tools simply do not achieve the accuracy your operation demands. We evaluate both options honestly during technical assessment and recommend the most cost-effective path — including recommending off-the-shelf where it genuinely fits.

Both timeline and investment depend on problem complexity, data availability, integration requirements, and your internal review and sign-off processes. A targeted pilot validating a single inspection task has a very different scope from an enterprise-wide multi-camera deployment integrated into SCADA and ERP systems.

Our technical assessment produces a fixed-price proposal with a detailed project roadmap and realistic milestones specific to your requirements — before any development commitment is made. We work with organisations of all sizes, from focused proof-of-concept projects through to enterprise-scale rollouts. We do not publish generic price ranges because scope variation makes them misleading; accurate figures require understanding your use case first.

Our assessment covers problem definition and feasibility analysis, data requirements review (volume, quality, labelling needs), technical architecture design (model selection, infrastructure, deployment strategy), accuracy and performance benchmarking, integration and deployment planning, and risk analysis with mitigation strategies.

You receive a complete project roadmap with a fixed-price proposal — not a directional estimate — before committing to development. The assessment is designed to surface integration challenges, validate technical feasibility, and align all stakeholders on scope and expected outcomes. Where critical uncertainties exist, we include proof-of-concept testing as part of the assessment to de-risk the development decision.

Every deployed system includes active performance monitoring to track accuracy, detect model drift, and flag when retraining is required. This is built into the deployment, not offered as an optional add-on. You get performance dashboards, automated alerts for degraded accuracy, and data collection pipelines that continuously capture edge cases for future model improvement.

When operational conditions change — new product variants, lighting upgrades, camera repositioning, new defect types — we provide model updates and retraining services to restore and maintain accuracy. Ongoing support agreements include regular model health reviews and performance reporting so degradation is caught before it impacts operations.

Yes. We specialise in integrating CV systems into existing infrastructure rather than requiring you to replace it. We work with standard protocols (RTSP, MQTT, WebRTC, REST APIs), connect to most IP cameras and video management systems, integrate with SQL and NoSQL databases and data warehouses, and push results to your business systems — ERP, CRM, SCADA, MES — in the format they expect.

We deploy on your preferred infrastructure: cloud, on-premise, or edge. Integration planning is part of every technical assessment, not an afterthought. We map integration points, identify protocol translation requirements, and validate connectivity before development begins to prevent integration surprises late in the project.

Security is implemented at every layer: data encryption in transit (TLS) and at rest (AES-256), role-based access controls, full audit logging, and compliance with GDPR, HIPAA, or industry-specific regulations as applicable. For applications where data cannot leave your environment, we offer fully on-premise or private cloud deployment — no data touches external infrastructure.

For sensitive applications, we can train models using federated learning or differential privacy techniques where appropriate. All systems undergo regular security audits and penetration testing. Compliance requirements and data residency constraints are identified during the technical assessment and addressed in the architecture design before development begins.

Yes. We offer managed support agreements that include system monitoring and alerts, regular model performance reviews, retraining and model updates as operational conditions evolve, integration support for new cameras or data sources, security patches and infrastructure updates, and technical support with guaranteed response times.

Support packages are tailored to your uptime and accuracy requirements — a safety-critical production line has different support needs from a batch analytics system. We do not provide one-size-fits-all support tiers; scope, response time, and review frequency are agreed based on what your operation actually requires.

We establish clear, measurable accuracy benchmarks during the assessment phase and validate them through proof-of-concept testing before full development is approved. Our contracts include performance guarantees tied to agreed accuracy metrics — these are not aspirational targets but contractual commitments.

If a deployed system does not meet defined benchmarks, we provide additional model tuning, targeted data collection, and retraining at no extra cost until the agreed accuracy is achieved. This accountability is why we insist on rigorous POC validation before moving to production — we do not proceed to full deployment until performance is proven on your actual data and conditions.