Overview
AI engineering is not prompt engineering. Building production-grade AI means fine-tuning models on domain-specific data, constructing retrieval pipelines that stay accurate at scale, and deploying systems that degrade gracefully under load. We have done this for defence clients with classified data and sports platforms with 1.4M active users. The engineering is the same. The stakes are different.
The Problem
Most AI projects stall at the prototype stage. A demo works in a Jupyter notebook but falls apart under real data, real users, and real latency requirements. Teams discover their RAG pipeline hallucinates on edge cases, their model drifts after a few weeks, and their inference costs are 10× the budget.
Our Approach
We design AI systems with production in mind from day one. Evaluation datasets and regression benchmarks before training. MLflow experiment tracking from the start. Quantisation (GGUF/ONNX) to hit latency targets. Staged rollouts with canary deployments. RLHF for models that need human preference alignment. For RAG systems, we benchmark retrieval accuracy at multiple chunk sizes and embedding models before committing to a stack.
Deliverables
- LLM fine-tuning (LoRA, RLHF)
- RAG pipeline architecture
- Evaluation dataset and benchmarks
- Model quantisation (GGUF/ONNX)
- Inference API (FastAPI)
- MLflow experiment tracking