AI systems that
run in production,
not just demos

The honest answer is: usually RAG first, fine-tuning if RAG isn't enough for your specific task. RAG is faster to prototype, easier to update with new data, and has lower upfront cost. Fine-tuning is the better call when your task requires consistent output format, domain-specific style, or performance on narrow tasks where in-context examples aren't sufficient. Many of our production systems use both - RAG for knowledge retrieval and a fine-tuned model for the final generation or classification step.

We run a structured evaluation before any production deployment: accuracy against a holdout set, behaviour on adversarial inputs and edge cases, latency under expected peak load, and failure mode analysis. For anything touching safety-critical decisions, we add a second evaluation pass from the relevant domain expert. The metric we care most about is performance relative to the human baseline you're replacing or augmenting - if the AI isn't meaningfully better, it shouldn't go live.

Every AI system we build has a defined failure mode. For high-stakes decisions, that's a human escalation path - the AI outputs a confidence score and routes anything below a threshold to a human reviewer. For lower-stakes applications, we design graceful degradation and build the logging infrastructure to catch and analyse errors in production. We also build retraining pipelines so errors that repeat can be addressed in the next model iteration rather than accumulating.

We work within your data residency and privacy constraints from the start. For organisations that can't send data to external model providers, we deploy open-source models in your private cloud infrastructure. For fine-tuning on sensitive data, we use differential privacy techniques and synthetic data augmentation where appropriate. We've built compliant AI systems for NHS trusts, financial regulators, and government departments - the privacy engineering is a solved problem; it just has to be designed for from day one.

Sometimes - it depends on what your data looks like and what you're trying to do with it. For document-based use cases (contracts, emails, knowledge bases), you often don't need a data warehouse - you need a well-structured RAG pipeline. For training custom models, you need labelled examples, and if your data doesn't have them, we scope a labelling programme first. We'll give you an honest assessment in the first technical session of what's feasible with your current data.

A production-ready AI system for a narrowly-scoped use case - document classification, entity extraction, search - typically takes 8–14 weeks from kickoff to deployment. More complex systems involving computer vision hardware, multi-step agent workflows, or custom training data collection take 16–24 weeks. We don't quote the demo timeline; we quote the production timeline.