We like to bring to life ideas through working prototypes, rather than stale powerpoint presentations. As evidenced by our online Ideas Generator:
https://ai-ideas.arundeladvisers.com
At Arundel we blend deep technology experience with operational know-how. We apply AI as a tool to improve business performance when and where it makes sense. In the AI lab we are always experimenting and testing with the latest tools and models to explore what’s possible, what’s beneficial and find the most fruitful areas for value creation.
When a client’s business need is a genuine fit with an AI opportunity we can help align the latest tools with real data and motivated owners to create high value outcomes. We partner with our clients to co‑create and scale solutions moving from prototype to pilot to production.
Our philosophy
- Start with the problem. Define the desired outcome and how we’ll measure it.
- Show, don’t tell. Demos over decks. We focus on building a working solution, not writing about it.
- Adapt to what works. Exploring the boundaries of what the latest technology can deliver. Small bets, measured, with the courage to stop.
- Vendor‑agnostic, focused on fit. We choose models and frameworks for the job, not the logo.
- Safety is a feature. Privacy, audit trails, evaluation and human‑in‑the‑loop where it matters.
- Solid foundations. Great AI fails on poor data. We design for availability, quality and access from day one.
How we build AI solutions
Context engineering and prompt tuning.
Large language models (LLMs) perform best when you control what they see and how you ask. We structure inputs, include worked examples, set constraints and style. That’s how we get stable, repeatable outputs.
Retrieval‑augmented generation (RAG).
Rather than expecting a model to know everything, we retrieve only what’s relevant from your sources, then ask it to answer with citations. That keeps answers current, reduces hallucinations and respects data boundaries.
Tool‑using agents.
We design lightweight agents that call tools (APIs, databases, spreadsheets, search) with tight budgets, rate limits and guardrails.
Careful orchestration.
We use modern orchestration to map those steps, manage state and retries, and separate “thinking” from “doing”. Where simple code is better than a framework, we keep it simple.
Continuous evaluation.
We test offline, spot‑check with humans, monitor cost per task and latency, and compare versions so improvements are proven.
Interested?
Bring us a problem, we’ll bring you a working example. If it delivers value, we’ll help you take it live.