Practical GenAI, grounded in your data and your rules.

AGS builds practical generative AI grounded in your data and your rules — systems that cite their sources and respect your guardrails. Work spans LLM integration matched to your latency and cost, RAG and knowledge systems, systematic prompt engineering and evaluation, and custom model fine-tuning where off-the-shelf accuracy isn't enough. These are the same foundations our agents are built on.

RAG, or retrieval-augmented generation, grounds AI answers in your own documents and data instead of a model's generic training. AGS builds retrieval pipelines so outputs cite their sources and stay inside your guardrails. It is how we move GenAI past novelty to real value, and the same grounding underpins the autonomous agents we deploy.

We evaluate output quality against your accuracy bar, not a polished demo. Our four-step method builds RAG pipelines around your data, tests them systematically through prompt engineering and evaluation, then runs continuous evaluation as models and data change. That discipline keeps answers reliable long after launch, when models quietly shift underneath you.

Fine-tune when off-the-shelf accuracy isn't enough for your domain — specialised terminology, formats, or reasoning that retrieval alone can't reliably supply. AGS builds domain-tuned models for those cases, and often pairs them with RAG so answers stay grounded in current data. We start by identifying where each approach adds real value rather than defaulting to the heavier option.