AI & ML · Generative AI
LLMs shipped with adults in the room.
RAG systems, fine-tuning, agentic workflows and LLM evaluation — built with production safety in mind.
Generative AI is genuinely useful, and genuinely easy to ship badly. We build LLM systems — retrieval-augmented generation, fine-tuning, function-calling and multi-agent workflows — with the missing 20% enterprises usually skip: evaluation harnesses, guardrails, prompt versioning, cost controls and observability.
/ Capabilities
What's in scope.
Retrieval-Augmented Generation (RAG)
Chunking, embeddings, hybrid retrieval, re-ranking and context management tuned to your corpus and query patterns.
Fine-tuning & adapters
SFT, DPO and LoRA fine-tuning where prompt engineering and RAG hit their ceiling.
Agents & function-calling
Tool-using agents with clear boundaries, guardrails and human-in-the-loop for consequential actions.
Evaluation & guardrails
Golden-set evaluations, LLM-as-judge with human calibration, and input/output safety filters.
Retrieval is more than embeddings.
Production RAG systems combine BM25 and dense retrieval, use re-rankers, and manage context windows carefully. Good chunking and metadata beat any single model choice.
Evaluate every change.
Every prompt or model change runs against a golden set with regression thresholds. LLM-as-judge is calibrated against human labels — never trusted blindly.
/ Standards & Tooling
Common questions.
RAG or fine-tuning?
Start with RAG — it's cheaper, more transparent and easier to update. Fine-tune when you need style, format or task-specific behaviour that prompting can't reliably achieve.
Can we use open-weights models?
Yes. Llama-family and Mistral models are viable for many enterprise use cases, especially with domain fine-tuning and self-hosted inference where data-residency requires it.
/ Also under AI & Machine Learning
Ready to scope a generative ai & llm engineering engagement?
A senior practitioner — not a sales rep — will respond within one business day.
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