AI applications
Decide whether it is worth building,then decide how
Demos are easy; production is not. It fails on data supply, evaluation, cost and security boundaries. We start by testing whether a use case is feasible, then deliver an application you can audit, operate and budget for.
Assess before you build
Each candidate use case gets a feasibility and return assessment: is the data there, how will quality be measured, what does a wrong answer cost. Budget stops going to use cases that were never going to work.
Built on governed data
Knowledge bases and training data come from classified assets, and permissions follow the user, so a model never sees what the person asking cannot.
Quality and safety, both measured
An evaluation set and a baseline before launch; hallucination rate, unauthorised retrieval, cost and latency monitored after it. When something goes wrong the request can be replayed, traced to the step that failed and rolled back.
AI capability domains
Six domains from strategy through to production — a single use case, or a platform and the operations around it.
AI strategy & use-case assessment
Maturity assessment, use-case identification and prioritisation, ROI / TCO analysis, and a staged roadmap with technology and vendor recommendations.
Large models & agents
Model selection and benchmarking, prompt engineering, RAG knowledge bases, fine-tuning, distillation and quantisation, plus multi-agent orchestration and tool calling — self-hosted where required.
Machine learning & perception
Feature engineering, training, tuning and explainability, across computer vision detection, segmentation and OCR, and NLP extraction, Q&A, speech recognition and synthesis.
AI data engineering & MLOps
Training-data governance, annotation QA and synthetic data; feature stores, experiment tracking and CI/CD/CT pipelines; drift monitoring and GPU scheduling once it is live.
AI security & governance
Jailbreak and prompt-injection testing, AIGC content safety, federated learning and differential privacy, model watermarking and bias governance, aligned to ISO/IEC 42001.
Industry delivery & training
Intelligent service desks and copilots, document processing, recommendation and semantic search, predictive maintenance and fraud control — with training from executive briefings to hands-on engineering.
Frequently asked questions
Common questions on model choice, output quality and self-hosted deployment. If yours is not here, talk to the AI team directly.
Most enterprise use cases do not need a trained model. Fill the knowledge gap with retrieval first, use an evaluation set to confirm the ceiling is the model rather than the context, and only then consider fine-tuning. Whether to self-host depends on data sensitivity and regulatory scope; the assessment returns a cost comparison across hosted API, self-hosted inference and fine-tuning.
Start with an assessment, then talk scope
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.