From samples to explainable models
Machine learning, computer vision and natural language processing
Beyond large language models, industrial and business scenarios still rely heavily on specialised models: defect detection, OCR, text extraction, forecasting and classification. We do feature engineering, training and tuning with explainability analysis, across computer vision (detection, segmentation, OCR) and NLP (extraction, Q&A, speech recognition and synthesis), delivering deployable, explainable models.
Our approach
One team scopes, executes and retests; conclusions are delivered as evidence, not checklists.
Sample quality before model architecture
Sample count, distribution and annotation consistency are assessed before modelling; shortfalls are addressed with more samples or synthetic data before training.
Metrics defined by the business
Beyond accuracy, miss rate, false alarm rate or latency thresholds are set per use case and serve as acceptance criteria.
Explainability is a deliverable
SHAP / LIME analysis ships with the model, so business users understand its basis and can review and trust it.
Deliverables
Data and modelling plan
Sample assessment, feature design, model selection and evaluation metrics.
Trained model and evaluation report
Model artefacts, validation and backtest results, explainability analysis.
Deployment package and inference service
Model serving, edge deployment plan, monitoring metrics.
How we deliver
Four stages, each with defined inputs, outputs and a client sign-off.
Feasibility
Sample review, metric definition, technical route.
Data preparation and modelling
Annotation and augmentation, training and tuning, validation.
Deployment and trial
Serving or edge deployment, business validation.
Iteration
Retraining on new samples, metric review.
Customer story
GIA and Reallysec Partner to Drive Digital Transformation in the Diamond Industry
Cloud AI and deep learning models replace manual clarity grading, running across development, test, production and disaster-recovery environments.
Frequently asked questions
Notes on scope, execution and delivery standards. Contact us for anything not covered here.
Structured or image inputs, deterministic outputs, sensitivity to latency and cost, or edge deployment: in these cases a specialised model is usually more accurate and cheaper. The two can also be combined.
Start from where you stand
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.