AI Computer Vision SaaS Application Development

An effective MVP is: a focused risk zone instrumented with 2–6 cameras, a validated PPE detector for the target classes, edge inference for real-time alerts, a simple alerting channel (SMS/email/Slack) and a logging dashboard for review. It must include a labeling loop and process for rapid retraining. Keep scope tight: one line or risk type, measurable acceptance criteria, and a rollout plan.

Ask for case studies and references in industrial or regulated environments, request architecture diagrams showing security and failover, verify certifications (ISO 27001, functional safety if relevant), review their testing and validation process (stress, edge cases, adversarial tests), and request a small technical pilot or code sample. Confirm they understand relevant safety standards (OSHA, industry-specific rules) and can produce traceable test evidence.

Ongoing costs include edge/cloud compute, storage, networking, model maintenance, and support. Rough estimate: $3k–$20k/month depending on processing approach (edge-heavy is cheaper cloud costs), retention policy (video storage is expensive), and SLA/managed-service level. Budget for periodic model retraining and 10–20% of initial project cost per year for maintenance.

Design for failure: implement graceful degradation (fallback to simpler heuristics or alerts), human-in-the-loop escalation, redundant edge nodes, and health-checks with heartbeat monitoring. Define SOPs for supervisors when the system is offline (manual observation, increased signage), and have on-call support and an incident playbook. Keep a local buffer (ring buffer) so recent footage is available for postmortem when systems recover.

Start with transparency and policies: communicate goals (safety, not productivity surveillance), define strict access controls, anonymize/blur faces in analytics outputs, store minimal metadata, and limit retention. Engage unions and legal/HR early, show mockups and privacy impact assessments, and offer opt-in pilots or union representatives visibility into results. Clear governance and documented purpose are essential.

Use event-driven integration: publish alerts/events via MQTT, Kafka, webhooks, or REST to the MES/ERP. Map CV events to MES alarm types and ensure timestamps/IDs align. Work with the MES team to define workflows for automated responses (stop line, notify supervisor, log incident). Standard industrial protocols (OPC-UA) may be needed for SCADA/PLC integration.

If your use case is standard (hard hat, vest), off-the-shelf models/APIs can accelerate pilots. For factory-specific needs (special PPE, unique angles, integration, compliance), custom models are usually required to reach acceptable accuracy and explainability. A hybrid approach — start with prebuilt models, then fine-tune with your labeled data — is common and efficient.

Yes, but success requires engineering: choose appropriate cameras (higher dynamic range, IR), use protective housings, place cameras to minimize direct steam/dust paths, apply preprocessing (contrast/denoising), and collect training data with those conditions. Sometimes combining sensors (thermal, proximity) with vision improves reliability.

Minimum viable models can be trained with a few thousand labelled instances per class (hard hat on/off, vest on/off) if the images are diverse. For robust, generalizable systems across different lines and lighting, aim for 5k–20k+ labeled images per class including edge cases, plus synthetic augmentation and video-frame sampling.

In controlled conditions, PPE detectors (hard hat, high-vis vest) commonly hit 95%+ precision/recall. In real factory conditions—occlusion, poor lighting, dust, covered helmets—expect 85–95% after careful data collection and augmentation. Accuracy improves with more diverse training data, proper camera placement, and periodic retraining.

There’s no single answer — it depends on coverage density, line layout, occlusions, and task. A typical 50,000 sq ft facility may need dozens to low hundreds of cameras to cover multiple lines and blind spots. Start mapping critical risk zones first: entry/exit points, high-hazard machines, material transfer areas, and then pilot camera density for one line to learn exact counts.

Industrial PoE cameras with IR/low-light capability, rugged housing, appropriate lenses (wide or narrow depending on coverage) and fixed mounts. Edge inference appliances like NVIDIA Jetson family, Intel NCS2, or small form-factor servers for on-prem inference; optional NVRs for archival. Choose cameras and edges rated for temperature/dust/EMI appropriate to your environment.

Core team for delivery and ops: ML engineer (model training & evaluation), CV/data engineer (labeling pipelines, augmentation), software/backend engineer (APIs, integrations), edge/embedded engineer (hardware, inference), DevOps/SRE (deployment, monitoring), QA/test engineer, product manager/project manager, and a domain SME (safety manager/industrial engineer). For pilots, roles can be combined (3–6 people); production needs a dedicated team or vendor.

A pragmatic MVP can be delivered in 6–12 weeks if requirements and access to the floor (video, annotations) are ready. That covers camera placement, a basic model (hard hat/high-vis detection), edge proof-of-concept, and alerting. More complex rules, custom models, or strict compliance may extend timelines to 3–6 months.

Ballpark depends on scope. A lightweight pilot/MVP (single line, a few cameras, basic PPE/zone breach detection) typically runs $50k–$200k including hardware, software development, initial model training, and deployment. A full plant rollout with custom models, integration to MES/ERP, industrial hardware, redundancy, and regulatory work commonly runs $250k–$1M+. Ongoing ops, cloud/edge hosting, maintenance and model updates are extra (see ongoing cost answer).