COMPUTER VISION

FAQ

• What’s your approach to optimizing hardware and deployment?

  We match model complexity (CNN, YOLO, ViT) to your hardware — CUDA GPUs, edge TPUs, or cloud clusters. Then we benchmark for latency, throughput, and power. Our deployment uses containerized microservices, autoscaling, and real-time monitoring for low-latency, cost-effective inference.
• Will I need to manage development process day-to-day?

  Not at all. We structure work in clear milestones — R&D, POC, model training, integration, QA — providing digestible progress updates. Our autonomous engineers handle the technical details, while a dedicated manager (or CTO on request) oversees quality and timeline adherence.
• How do you ensure model quality and maintainability over time?

  Each milestone includes cross-QA, code reviews, and performance validation on hold-out sets. We deliver documented code, CI/CD pipelines for retraining, and drift-detection hooks. Ongoing support ensures your models evolve with new data and use-case shifts.
• How do you handle privacy and security of visual data?

  We enforce end-to-end encryption (AES-256) for data in transit and at rest, anonymize sensitive features (faces, license plates) on demand, and comply with GDPR and other regional regulations. Models can be deployed on-premises or in private VPCs to ensure full data sovereignty.
• Do you assign project managers to keep things on track?

  Yes, we assign a manager who can either communicate with you directly or work behind the scenes. For clients who prefer direct communication with the team, the manager ensures consistency while allowing you to interact directly with developers.
• Will I understand your updates if I’m not a technical expert?

  Yes. We translate complex technical details into accessible language and provide regular, digestible updates. This ensures you’re always informed without needing to go deep on the technical details.
• How do you ensure the project stays on budget and on schedule?

  ML projects carry uncertainty in model accuracy. We mitigate this by defining success metrics (precision, recall, latency) during Discovery and validating against hold-out datasets at every milestone. If a model isn’t converging, we flag alternatives early — not after six weeks of training.
• How has your process transformed a complex project?

  We built a real-time computer vision pipeline processing video at under 250ms per frame across 4 simultaneous feeds. The system reduced operator cognitive load by 75% while logging 10,000+ analysed frames — all running on commodity hardware without cloud GPU dependency.