Building AI models for your business
General-purpose AI models are powerful, but custom-trained models deliver superior performance for your specific use cases. We design and train specialised AI models that understand your domain, your data and your business requirements.
What we offer
Fine-tuning services
Optimising pre-trained models for your specific needs:
- Large language models: fine-tuning Llama, Mistral, Qwen and other open-source models.
- Vision models: image classification, object detection, segmentation.
- Multimodal models: combined understanding of text and images.
- Specialised models: speech, time series, recommendation systems.
Custom model development
Building models from scratch when needed:
- Designing new architectures.
- Creating domain-specific models.
- Specialised embedding models.
- Custom tokenizers and vocabularies.
Data engineering
Preparing and optimising data for model training:
- Data collection and annotation.
- Dataset curation and cleaning.
- Synthetic data generation.
- Data augmentation strategies.
Model optimisation
Ensuring production-ready performance:
- Quantisation and compression.
- Inference optimisation.
- Multi-platform deployment.
- Continuous improvement pipelines.
Key benefits
Superior accuracy Achieve higher performance on your specific tasks than general-purpose models.
Domain expertise Models that understand your industry terminology, context and requirements.
Data efficiency Better results with less data through transfer learning and smart fine-tuning.
Cost optimisation Use smaller, faster models that outperform larger general models.
Competitive advantage Proprietary AI capabilities that differentiate your products and services.
Technologies we use
- Frameworks: PyTorch, TensorFlow, JAX, Hugging Face Transformers.
- Training: DeepSpeed, FSDP, Parameter-Efficient Fine-Tuning (PEFT).
- Techniques: LoRA, QLoRA, Prefix Tuning, Adapter Layers.
- Platforms: AWS SageMaker, Google Vertex AI, Azure ML, on-premise infrastructure.
- MLOps: Weights & Biases, MLflow, DVC, Kubeflow.
Training approaches
Full fine-tuning
Complete model adaptation for maximum customisation:
- Updates all model parameters.
- Ideal for significant domain shift.
- Requires substantial compute resources.
- Best for critical applications.
Parameter-efficient fine-tuning
Efficient training with minimal resources:
- LoRA: low-rank adaptation for efficient training.
- QLoRA: quantised LoRA for reduced memory.
- Prefix Tuning: optimising only the prompt parameters.
- Adapters: adding small trainable modules.
Few-shot learning
Training from limited examples:
- Effective with small datasets.
- Fast iteration and testing.
- Reduced annotation costs.
- Quick deployment cycles.
Continual learning
Keeping models current with new data:
- Incremental training pipelines.
- Prevention of catastrophic forgetting.
- Online-learning capabilities.
- Automated retraining workflows.
Use cases
- Legal tech: contract analysis and extraction, legal document classification, precedent search and matching, and compliance checks.
- Healthcare and biotech: medical image analysis, clinical note processing, drug-discovery models and patient-risk prediction.
- Finance and insurance: fraud detection, credit-risk scoring, market-sentiment analysis and document-processing automation.
- E-commerce and retail: product recommendation engines, visual search, demand forecasting and customer-service automation.
- Manufacturing: defect detection, predictive maintenance, quality-control automation and process optimisation.
How we work
- Requirements and data assessment: define success metrics, evaluate available data, identify base models and determine resource needs.
- Data preparation: data collection and annotation, train/validation splits, data augmentation and quality checks.
- Model development: select base models, design the training strategy, experiment with architectures and optimise hyperparameters.
- Training and validation: train models on your data, validate performance, benchmark against base models, and iterate.
- Deployment and monitoring: optimise for production, deploy to your infrastructure, set up monitoring and establish a retraining pipeline.
Model performance metrics
We track and optimise the metrics that matter:
- Accuracy metrics: precision, recall, F1-score, accuracy.
- Ranking metrics: NDCG, MAP, MRR.
- Generation quality: BLEU, ROUGE, perplexity.
- Business metrics: inference cost, latency, throughput.
- Domain-specific: custom metrics aligned with your goals.
Training infrastructure
Cloud training: scalable GPU/TPU resources, cost-effective spot instances, managed training services and multi-region deployment.
On-premise training: private infrastructure setup, GPU-cluster management, data-privacy compliance and full control over training.
Hybrid approach: data stays local, training runs in a secure cloud — the best of both worlds with flexible scaling.
Ready to build a custom AI model for your business? Get in touch to discuss your requirements, data and success metrics.