Custom deep learning services
Our deep learning team includes researchers who have published at NeurIPS, ICML, and CVPR. They bring cutting-edge techniques from academia into production-ready solutions....
We select and customize architectures — CNNs, RNNs, Transformers, GANs — based on your data type, performance requirements, and deployment constraints.
We adapt models like BERT, GPT, ResNet, and CLIP to your specific use case, reducing training time and data requirements by up to 90%.
We develop production-grade vision systems using YOLO, Detectron2, and custom architectures for applications from quality inspection to autonomous navigation.
We build audio ML pipelines using Whisper, WaveNet, and custom models for transcription, voice cloning, and real-time audio analysis.
We implement mixed-precision training, model parallelism, quantization, and distillation to reduce training costs and enable edge deployment.
We fine-tune and deploy diffusion models, GANs, and large language models with safety filters, content moderation, and cost-efficient inference.
"Their team built a real-time defect detection system that catches issues our human inspectors missed. Product quality improved by 35%."
Yuki Tanaka
VP Manufacturing, PrecisionTech
Case Studies
Real results from real projects. See how we've delivered transformative deep learning solutions.
Built a Real-Time Defect Detection System with 99.5% Accuracy
Deployed a CNN-based visual inspection system processing 1,000 parts per minute on the production line.
Created a Multilingual Voice Assistant Supporting 20 Languages
Built end-to-end speech recognition and synthesis using Transformer models with real-time processing.
Developed a Medical Imaging System for Early Cancer Detection
Trained deep learning models on 500K+ medical images achieving radiologist-level diagnostic accuracy.
Tools & Technologies
We combine industry-standard frameworks with modern tooling and proven internal processes to accelerate delivery.
Frameworks
- PyTorch
- TensorFlow
- JAX
- ONNX
Training
- NVIDIA A100
- TPU v4
- DeepSpeed
- Horovod
Vision
- YOLO
- Detectron2
- OpenCV
- Hugging Face
Deployment
- TensorRT
- Triton
- Core ML
- TFLite
Frequently Asked Questions
Have more questions? Talk to an expert — we're happy to help.
Deep learning excels with unstructured data (images, text, audio) and large datasets. Traditional ML often works better for tabular data with limited samples. We evaluate both approaches.
Training requirements vary widely. We optimize for cost using spot instances, efficient architectures, and transfer learning. Inference can often run on CPUs after optimization.
Yes. We use model quantization, pruning, and distillation to compress models for deployment on mobile devices, IoT hardware, and edge servers.
We implement attention visualization, SHAP values, GradCAM, and other interpretability techniques to provide explanations for model predictions that stakeholders can understand.
Related Services
Cloud Computing
Enhance data storage and processing with scalable and efficient cloud infrastructure tailored to your needs.
Learn MoreCloud Migration
Migrate on-premise infrastructure and applications to the cloud, increasing scalability and reducing costs.
Learn MoreMachine Learning
Develop, train, and deploy ML models that enhance prediction, automate processes, and drive innovation.
Learn MoreComputer Vision
Enable machines to interpret and understand visual data to automate tasks like image recognition and analysis.
Learn More