Practical AI features recommendation engines, fraud detection, document processing and LLM assistants designed to run reliably in production.
A lot of AI work never leaves the notebook. We specialise in the unglamorous half of machine learning: getting a model into production, monitoring it for drift, and building the guardrails that keep an LLM-powered feature useful instead of embarrassing. If it ships, it's judged on real outcomes.
Support bots and internal copilots grounded in your own data.
Personalisation models that lift engagement and conversion.
Document processing, defect detection and image classification.
Real-time scoring models for transactions and user behaviour.
Retrieval-augmented generation over your internal knowledge base.
Model versioning, drift detection and automated retraining.
Prompt engineering and guardrails tuned to your brand voice.
Bias testing and data privacy considerations built into every model.
We assess whether your data can actually support the AI feature you want.
Choosing between a custom model, fine-tuning, or an LLM + RAG approach.
Iterative development against a clear accuracy or quality benchmark.
Wiring the model into your existing product with proper fallbacks.
Ongoing drift detection and scheduled retraining as data evolves.
ML-driven demand forecasting that cut inventory waste by 22%.
AI support agent resolving 65% of tickets without human handoff.
Real-time fraud detection layered into a 2M+ transaction platform.
No — many clients rely on us end-to-end, though we're also happy to embed alongside an existing data team.
Yes, this is one of our most common engagements — adding a specific AI feature into a codebase we didn't build.
Through retrieval grounding, output validation and clear fallback behaviour when the model is uncertain.
No — we default to private, access-controlled pipelines and only use third-party APIs under terms that exclude training on your data.
Tell us the goal we'll map out scope, timeline and cost within days.