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.
• How do you ensure the project stays on budget and on schedule?

  ML projects carry uncertainty in model accuracy. We define success metrics (precision, recall, latency) in the Diagnostic Architecture Audit and validate against hold-out data 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 pose-mapping tool that turns a photo of a person into a matching 3D character pose — MediaPipe detection plus combined CCD and FABRIK inverse kinematics — and shipped it into the client’s production React app in about three weeks.
• Do you sign NDAs?

  Yes. We sign NDAs before any technical discussion begins, on request.
• Who owns the code after delivery?

  You do. IP, source code, architecture docs, and deployment runbooks are fully assigned to you on completion — no vendor lock-in.
• Do you work with formal contracts?

  Yes. Signed contracts with fixed scope per phase. Wyoming LLC — you’re contracting with a registered entity, not an individual.