AI/ML Solutions that ship to production — not just to slides
From generative AI copilots to computer vision and forecasting, we design, train, and operate machine learning systems with the same rigour as mission-critical software.
Six pillars of applied AI
Generative AI & LLM Engineering
Copilots, chat assistants, and document intelligence built on top of leading foundation models with retrieval-augmented generation, evaluation harnesses, and hallucination guardrails.
- RAG pipelines over private data
- LLM fine-tuning & prompt systems
- Agentic workflow automation
- Evaluation & guardrail frameworks
Natural Language Processing
Understand and act on text at scale — classification, entity extraction, summarisation, sentiment, and multilingual pipelines integrated into your workflows.
- Document classification & extraction
- Semantic search
- Chatbots & voice interfaces
- Text analytics dashboards
Computer Vision
Vision systems for inspection, recognition, and automation — object detection, segmentation, OCR, and video analytics tuned to your accuracy and latency budgets.
- Object detection & tracking
- Image segmentation
- OCR & document digitisation
- Edge deployment
Predictive Analytics & Forecasting
Demand forecasting, churn prediction, risk scoring, and recommendation engines that convert historical data into forward-looking business decisions.
- Demand & revenue forecasting
- Churn & risk models
- Recommendation systems
- Anomaly detection
MLOps & AI Infrastructure
The unglamorous work that makes AI dependable: versioned data and models, automated retraining, drift monitoring, and cost-controlled serving infrastructure.
- Model CI/CD pipelines
- Drift & performance monitoring
- Feature stores
- GPU cost optimisation
AI Strategy & Advisory
A pragmatic roadmap for AI adoption: use-case prioritisation, build-vs-buy analysis, data readiness assessment, and responsible-AI governance.
- Use-case discovery workshops
- Data readiness audits
- Responsible AI governance
- Team enablement & training
How we keep AI dependable
Start from the metric
Every model has a business KPI and an accuracy/latency budget agreed before training begins.
Private by design
Your data never trains third-party models. PII redaction, access controls, and audit logs are standard.
Human in the loop
Confidence thresholds and review queues keep people in control of high-stakes decisions.
Monitor forever
Drift detection and automated evaluation keep production models honest long after launch.
Have data? Let's turn it into advantage.
Bring us a use case — or let's find one together in a discovery workshop.