DeepVision: Enhancing image recognition with Neural Networks
Design
Artificial Intellegance
Challenge
Our client was struggling to scale their image recognition capabilities to meet real-time customer demands across global retail environments. Their legacy models performed inconsistently and required significant manual tuning to maintain accuracy.
Solution
Seawolf AI embedded a team of AI engineers to design and deploy DeepVision, a production-grade neural network stack optimized for complex image classification and object detection tasks.
Key deliverables included:
Model re-architecture using transfer learning and vision transformers (ViT)
End-to-end integration into the client’s digital product suite (mobile + edge devices)
Real-time inference optimization using TensorRT and ONNX on NVIDIA hardware
Agentic workflows for automated labeling and drift monitoring using active learning loops
Results
✅ 87% reduction in manual tagging effort ✅ +38% improvement in recognition accuracy ✅ Real-time inference on edge devices in under 150ms ✅ Embedded orchestration workflows for continuous improvement
What Made It Different
We didn’t just drop in a model. We engineered a self-adapting vision system — tightly integrated with the client’s infrastructure and team — built to evolve and scale autonomously.
“The Seawolf team didn’t just deliver models. They delivered an operating system for vision.” — VP of Product, Client (Retail Tech)
