Custom AI
Model Development
Off-the-shelf AI models are built for everyone — which means they're optimised for no one. We build and fine-tune LLMs, computer vision systems, predictive analytics engines, and domain-specific models trained on your data, calibrated to your industry, and built to perform where generic AI falls short.
generic foundation models
data to production
AI that is built for your data — not everyone else's.
Foundation models like GPT or general-purpose vision APIs are powerful starting points — but they carry the biases, gaps, and generalisations of the internet-scale data they were trained on. When you need AI that understands your specific domain, your terminology, your edge cases, and your risk thresholds, custom model development is the answer.
From fine-tuning existing foundation models on proprietary datasets to building bespoke architectures from the ground up, we design every model for production performance in your specific operational context.
"A model trained on your data, in your domain, with your performance requirements is not just better than a generic model — it operates in a fundamentally different category of capability and reliability."
Domain-specific accuracy generic models cannot match
Custom models trained on industry-specific data consistently outperform general-purpose models by 30–50% on domain tasks — because they understand the vocabulary, patterns, and edge cases that general training data never adequately represents.
Full ownership of your model and intellectual property
Every model we build is fully owned by your enterprise. Your training data, model weights, fine-tuning methodologies, and performance benchmarks are entirely proprietary — with no third-party dependency, no usage limitations, and no data sharing with external providers.
Designed for explainability and regulatory compliance
Custom models can be architected with interpretability requirements built in from the start — essential for regulated industries where black-box AI is not acceptable. Every model we build includes explainability documentation and auditability features appropriate to your regulatory environment.
Optimised for your infrastructure and latency requirements
We build and optimise models for your specific deployment environment — whether cloud-hosted, on-premise, edge devices, or embedded systems — with quantisation, pruning, and distillation techniques that maximise performance within your infrastructure constraints.
Four classes of AI models. One specialised team.
Large Language Models & NLP
Custom LLMs and fine-tuned language models built for domain-specific text understanding, generation, and reasoning. From enterprise document intelligence to specialised customer communication AI — trained on your proprietary data for performance that generic models cannot replicate.
Computer Vision Models
Custom vision systems trained on your specific visual data — from manufacturing defect detection to medical imaging analysis. Built for the lighting conditions, object classes, and accuracy thresholds that matter in your operational environment, not benchmark datasets.
Predictive Analytics Engines
Tabular and time-series models that extract predictive intelligence from structured enterprise data — forecasting demand, detecting anomalies, predicting churn, and scoring risk with accuracy calibrated specifically to your historical patterns and business context.
Domain-Specific AI Systems
Bespoke AI architectures purpose-built for highly specialised industry applications — combining multiple model types, expert knowledge injection, and domain-specific evaluation frameworks to deliver AI that operates with genuine domain expertise rather than generalised capability.
How we build production-grade models
A rigorous ML engineering process that takes your raw data through to a production-deployed model — with quality gates and validation at every stage.
Data Audit & Preparation
We assess your available data — volume, quality, labelling, and representativeness. We define data collection strategies for gaps, build preprocessing pipelines, and establish the data foundation that determines model quality.
Architecture Design
We select and design the optimal model architecture — whether fine-tuning a foundation model, building a custom architecture, or combining multiple model types — based on your task requirements, data characteristics, and deployment constraints.
Training & Experimentation
Iterative model training with systematic hyperparameter optimisation, experiment tracking, and performance benchmarking across multiple approaches — selecting the configuration that delivers the best performance on your specific evaluation criteria.
Evaluation & Validation
Rigorous model evaluation against business-relevant metrics — not just academic benchmarks. Bias testing, edge case analysis, adversarial testing, and performance validation across the distribution of inputs your model will encounter in production.
Deployment & Monitoring
Production deployment with inference optimisation, API integration, monitoring dashboards, and automated alerting for performance degradation. We establish retraining schedules and data drift monitoring to keep your model performant over time.
The tools and frameworks behind every model.
LLM Fine-tuning & Adaptation
We work across the full landscape of foundation models — selecting and fine-tuning the right base for your use case, data size, and deployment constraints.
Computer Vision Frameworks
From classification and detection to segmentation and generative vision — built with the frameworks that deliver production-grade reliability at scale.
ML & Time-Series Models
Classical ML and deep learning approaches for structured data — selected based on data volume, interpretability requirements, and performance targets.
Training & Deployment Platform
End-to-end ML infrastructure for experiment tracking, model versioning, deployment pipelines, and production monitoring across cloud and on-premise environments.
Data Pipeline & Preparation
Scalable data preprocessing, labelling, augmentation, and quality assurance pipelines that ensure your training data meets the quality bar your model requires.
Model Testing & Explainability
Comprehensive evaluation frameworks covering performance benchmarking, bias detection, adversarial testing, and explainability tools for regulatory and stakeholder requirements.
Every model. Every asset. Fully yours.
Production-Ready Model
A fully trained, validated, and optimised model — complete with inference API, integration documentation, and deployment configuration for your target environment. Ready for production from day one.
Performance Evaluation Report
Detailed benchmarking across all relevant metrics — accuracy, precision, recall, latency, and business-specific KPIs — with comparison against baseline and alternative approaches, and documentation of evaluation methodology.
Training Data Pipeline
Fully documented and reproducible data preprocessing and augmentation pipelines — enabling your team to retrain models on new data, extend training sets, and maintain data quality standards independently over time.
Model Card & Documentation
Comprehensive model documentation covering architecture decisions, training methodology, known limitations, intended use cases, and out-of-distribution behaviour — providing the transparency required for responsible deployment.
Monitoring & Drift Detection
Production monitoring dashboards with automated alerts for performance degradation, data drift, and distribution shift — ensuring you know immediately when your model requires retraining or recalibration.
Retraining Playbook
A structured process for ongoing model maintenance — covering data collection requirements, retraining schedules, evaluation criteria for new versions, and the deployment process for updating models in production without service disruption.
What custom models deliver that generic AI cannot.
Higher accuracy on domain tasks
Custom models trained on proprietary domain data consistently outperform general-purpose foundation models on industry-specific tasks by 30–50%.
Average time to production
From data audit to live deployment — our structured ML engineering process delivers production-ready models in 4–8 weeks depending on complexity.
IP and data ownership
Every model, weight, and training artefact is fully owned by your enterprise — with no external data dependencies or third-party usage limitations.
Lower inference cost at scale
Purpose-built and optimised models run at a fraction of the cost of large general-purpose APIs when deployed at enterprise volume — compounding savings over time.
Questions about custom model development.
Data requirements vary significantly by model type and approach. Fine-tuning an existing foundation model may require as few as 500–2,000 labelled examples for domain adaptation. Training a custom vision model for quality control typically requires 5,000–50,000 labelled images. Predictive analytics models depend on historical data volume. We conduct a data audit at the start of every engagement to assess what you have, identify gaps, and design the right approach given your data reality.
Unlabelled data is extremely common and entirely manageable. We design data labelling pipelines using a combination of active learning, semi-supervised techniques, and — where appropriate — human-in-the-loop annotation workflows. In many cases, we can achieve strong performance through fine-tuning and transfer learning with significantly less labelled data than a from-scratch training approach would require.
Yes — entirely and unconditionally. All model weights, training data pipelines, evaluation frameworks, and associated intellectual property developed during the engagement are transferred to your enterprise in full. We operate under a comprehensive IP assignment agreement as a standard component of every engagement. Your competitive data is never used in any other context.
Yes. We regularly work with enterprises that have strict data residency or on-premise requirements — particularly in financial services, healthcare, and government sectors. We design training and deployment architectures that keep sensitive data entirely within your controlled infrastructure, using open-weight models and on-premise compute where cloud training is not permissible.
All models we deploy include monitoring infrastructure that tracks data drift, prediction distribution shift, and business metric degradation in real time. We establish automated alerting thresholds and provide a retraining playbook so your team knows exactly when and how to update the model. For clients on a support retainer, we handle monitoring and proactive retraining as part of the ongoing engagement.
AI Strategy & Consulting is the upstream work — identifying what to build, why, and in what order, with validated business cases and a deployment roadmap. Custom Model Development is the execution of a specific AI capability identified in that strategy. Many clients engage us for strategy first, then proceed directly to model development. Others come with a clear technical brief and engage us directly for development. Both paths are valid, and the strategy cost is credited against any subsequent development engagement.
Ready to build AI that actually knows your domain?
Tell us about your use case. We'll assess your data, define the right approach, and show you what's possible — at no cost.