Deep Learning & AI
Deep Learning & AI Model Development in PyTorch
We build custom deep learning models in PyTorch that turn images, sensor data and text into reliable predictions. We handle the whole process: preparing the data, designing and testing the model, and explaining in plain language what it gets right, where it struggles and why.
What We Can Do for You
Computer Vision for Quality Control
Image classifiers for inspection and grading. For example, we built a model that sorts export fruit into conforming and non-conforming, using a fine-tuned ResNet-18. Its heatmaps show which parts of each image drove the decision.
Sensor & Time-Series Analysis
We detect activities and events in accelerometer data. For example, we built fall detection and 17-class activity recognition using LSTM and Transformer models.
Sequence & Text Models
Recurrent networks that classify or generate sequences, such as predicting a name's language of origin or composing new melodies.
Models Tuned to What Matters
We choose metrics and decision thresholds around your real costs, whether that means catching every defect or keeping false alarms low.
Built from First Principles
We've written neural network layers, backpropagation and optimisers from scratch. That lets us debug and adapt models instead of treating them as black boxes.
Reproducible by Default
Every project comes with reproducible code and a clear summary of the results, so you're never locked in.
Common Use Cases
Built for South African SMEs across industries, with real business problems to solve.
- //Visual inspection and product grading
- //Explaining model decisions with heatmaps
- //Fall and activity detection from wearables
- //Event detection in sensor data
- //Text and sequence classification
- //Tuning models around the real cost of errors
Start with one problem
Every business runs differently, so each setup is built around your team, your processes and your systems. Book a free 30-minute consultation. It's a straight conversation about what you need, with no obligation.
Book a Free ConsultationFrequently Asked Questions
What's the difference between deep learning and data science?
Deep learning is a branch of machine learning that uses neural networks. It shines on images, sensor signals and text, where hand-crafted rules fall short. Data science is broader and includes statistics, forecasting and analysis, and we'll recommend whichever approach fits your problem best.
How do we know why the model made a decision?
For image models we produce heatmaps that show which parts of each image drove the decision. For every project we explain in plain language what the model gets right, where it struggles and why.
How much labelled data do we need?
It depends on the task. Fine-tuning a pre-trained model, as we did with ResNet-18 for fruit grading, usually needs far fewer labelled examples than training from scratch. We assess your data during discovery and tell you honestly what's achievable.
Do we get the code?
Yes. Every project comes with reproducible code and a clear summary of the results.
Let's build something worth talking about
No jargon, no hard sell. Just an honest conversation about your business and how we can help.
Book a Free Consultation