Deep Learning
Defect detection vision for a law firm in Multan
Deep learning model for images, text or audio, fine-tuned and deployed for a law firm.
Inference 3x faster
Deep learning for vision, language and complex patterns
Custom neural networks for images, video, text and audio. We fine-tune state-of-the-art models with PyTorch and TensorFlow and deploy them to the cloud or the edge.
At a glance
97%accuracy on recent vision projects
10xfaster inference after optimisation
15+deep learning systems shipped
Overview
Some problems are too complex for traditional models
Recognising defects in product photos, reading handwritten forms, understanding customer sentiment or transcribing calls all involve unstructured data. Deep learning excels exactly there.
Thanks to transfer learning and foundation models, you no longer need millions of examples. We fine-tune proven architectures on your data to reach production accuracy quickly.
We handle the full lifecycle: data labelling, training on GPUs, optimisation for speed and deployment on cloud servers, mobile devices or edge cameras.
This service works best alongside our Machine Learning Development and Artificial Intelligence Solutions services. For a deeper introduction, read our guide: AI vs machine learning vs deep learning.
What's included
- Feasibility study and data strategy
- Data collection, labelling and augmentation
- Computer vision: detection, segmentation, OCR
- NLP: classification, NER, sentiment, summarisation
- Speech recognition and audio analysis
- Fine-tuning transformers and LLMs
- Model compression for edge and mobile
- GPU deployment, APIs and monitoring
accuracy on recent vision projects
0
%
faster inference after optimisation
0
x
deep learning systems shipped
0
+
Features
What you get with our deep learning service
Everything below is delivered by specialists who focus on deep learning every day.
Computer vision
Object detection, quality inspection, counting and face blurring.
Document OCR
Read printed and handwritten text from scans and photos.
NLP
Classify, extract and summarise text in English and Urdu.
Speech AI
Transcribe calls and meetings and analyse what was said.
Fine-tuned LLMs
Domain-specific language models trained on your data.
Edge deployment
Fast, compressed models that run on cameras and phones.
Our process
Our Deep Learning process
A clear, repeatable process with a checkpoint at every stage, so you always know what is happening and why.
1
Feasibility
Test the idea on sample data and estimate accuracy.
2
Data
Collect, label and augment a quality training set.
3
Train
Fine-tune and evaluate architectures on GPUs.
4
Deploy
Optimise and ship to cloud, mobile or edge.
Deep Learning tools and platforms we use
PyTorchTensorFlowHugging FaceYOLOOpenCVONNXNVIDIA CUDALabel StudioPyTorchTensorFlowHugging FaceYOLOOpenCVONNXNVIDIA CUDALabel StudioPyTorchTensorFlowHugging FaceYOLOOpenCVONNXNVIDIA CUDALabel StudioPyTorchTensorFlowHugging FaceYOLOOpenCVONNXNVIDIA CUDALabel Studio
Portfolio
Deep Learning projects we have delivered
Browse 50 recent deep learning projects. Click any project to see the details.
Showing 12 of 50 projects
Deep Learning
Deep Learning
Defect detection vision for a hotel in Islamabad
Deep learning model for images, text or audio, fine-tuned and deployed for a hotel.
Detection accuracy 99.1%
Deep Learning
License plate recognition for a pharmacy chain in Sydney
Deep learning model for images, text or audio, fine-tuned and deployed for a pharmacy chain.
Inspection time cut by 73%
Deep Learning
Call transcription AI for a construction firm in Toronto
Deep learning model for images, text or audio, fine-tuned and deployed for a construction firm.
Inference 6x faster
Deep Learning
Handwriting OCR for a law firm in Multan
Deep learning model for images, text or audio, fine-tuned and deployed for a law firm.
Detection accuracy 99.1%
Deep Learning
Urdu sentiment analysis for a real estate developer in Karachi
Deep learning model for images, text or audio, fine-tuned and deployed for a real estate developer.
Detection accuracy 96.1%
Deep Learning
Urdu sentiment analysis for a private school network in New York
Deep learning model for images, text or audio, fine-tuned and deployed for a private school network.
Detection accuracy 92.1%
Deep Learning
Urdu sentiment analysis for an insurance broker in London
Deep learning model for images, text or audio, fine-tuned and deployed for an insurance broker.
Inspection time cut by 30%
Deep Learning
Urdu sentiment analysis for an immigration consultancy in Sydney
Deep learning model for images, text or audio, fine-tuned and deployed for an immigration consultancy.
Inference 7x faster
Deep Learning
License plate recognition for a logistics company in Multan
Deep learning model for images, text or audio, fine-tuned and deployed for a logistics company.
Inference 11x faster
Deep Learning
License plate recognition for a law firm in Peshawar
Deep learning model for images, text or audio, fine-tuned and deployed for a law firm.
Detection accuracy 96.4%
Deep Learning
Defect detection vision for a furniture retailer in Karachi
Deep learning model for images, text or audio, fine-tuned and deployed for a furniture retailer.
Inspection time cut by 41%
FAQ
Deep Learning questions, answered
Still have a question? Our team replies within one business day.
When do I need deep learning instead of machine learning?
Deep learning is the right choice for unstructured data such as images, video, audio and free text, or when patterns are too complex for traditional models.
Do I need a huge dataset?
Not usually. With transfer learning we can fine-tune pre-trained models on a few hundred to a few thousand labelled examples for many tasks.
Can models run without the internet?
Yes. We compress and optimise models to run on edge devices, local servers and mobile phones.
Do you help with data labelling?
Yes. We set up labelling workflows, label data ourselves or train your team, with quality checks throughout.
Which frameworks do you use?
Mainly PyTorch and TensorFlow, with Hugging Face for language models and YOLO and OpenCV for vision.
From the blog
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