We build and train custom models for your business needs, or retrain existing ones — open-source and proprietary — for better efficiency and scalability.
Generic AI models are trained on generic data. When your use case involves specialist domain knowledge, non-standard language, unusual data formats, or very specific accuracy requirements — a custom model delivers results that no off-the-shelf product can match.
We build models from scratch, fine-tune foundation models on your proprietary data, or retrain existing open-source models to outperform on your specific task. Every model is documented, version-controlled, and designed for long-term maintainability.
We translate your business need into a precise ML problem definition — and audit your data for sufficiency, quality, and bias before any modelling begins.
Transforming raw data into the signals your model actually needs — often the highest-value step in the entire process, and where domain expertise matters most.
We evaluate multiple algorithms and architectures, train with rigorous cross-validation, and select the approach that best balances accuracy, interpretability, and computational cost.
We test model performance across subgroups — especially important in health, finance, and humanitarian applications where model errors affect real people's lives.
We package models as APIs or batch pipelines, deploy to your chosen environment, and set up monitoring for data drift, performance degradation, and prediction quality.
Take GPT, Llama, BERT, or Whisper and fine-tune it on your proprietary data — medical records, legal documents, agricultural databases, local language corpora.
If you already have a model in production that's drifted or underperforming, we retrain it on fresh data — improving accuracy without rebuilding from scratch.
For regulated environments — health, finance, government — we build models with interpretability tools (SHAP, LIME) so decisions can be explained and audited.
Tell us your use case and data — and we'll tell you exactly what's achievable and how we'd approach it.