AI systems that earn their place

Most AI projects fail because they start too complex. We take a different approach: start simple, prove value, then scale. Over a decade of engineering experience applied to building systems that actually ship.

Let's understand

How We Think

What we've learned from seeing AI projects succeed and fail

Simple is better

Most teams reach for agents when RAG would work. Or RAG when search would work. We start with the least complex solution that solves the problem.

Data quality

The latest model won't save a bad dataset. We fix the data pipeline first, then choose the model that fits—not the other way around.

Production thinking

Demos are easy. Production is hard. We design for cost, latency, failure modes, and observability before writing the first line of code.

What We Build

Intelligent systems designed for real-world use, not demos

RAG & Knowledge Systems

From simple lookups to multi-stage reasoning pipelines. We design retrieval systems that surface the right information at the right time.

Examples
  • Internal knowledge assistants
  • Document Q&A systems
  • Decision-support pipelines

Agentic Workflows

Task coordination and autonomous decision-making. Agents that know when to act and when to ask—introduced only when they provide clear benefits.

Examples
  • Ops automation agents
  • Multi-step task orchestration
  • Human-in-the-loop workflows

Backend & APIs

Robust infrastructure for intelligent applications. Clean APIs, reliable data pipelines, and systems designed for scale from day one.

Examples
  • AI-ready API design
  • Data ingestion pipelines
  • Production observability

How We Work

Engagements delivered on a project or retainer basis

01

Collaborative from day one

We work closely with product and engineering teams to ensure solutions are well understood, appropriately scoped, and designed to evolve over time.

02

Right-sized complexity

We design AI systems across the full complexity spectrum—choosing the minimum complexity required to solve the problem well.

03

Production-ready by default

All systems are built with production constraints in mind: performance, cost control, observability, and failure handling from day one.

04

Built to last

We prioritise designs that can be reasoned about and evolved over time, favouring clarity and maintainability over novelty.

Who This Is For

We work best with teams who share these priorities

Engineering-led teams building AI into their products
Product organisations past the experimentation phase
Teams who value clarity and maintainability over novelty

How Engagements Work

A clear path from first conversation to production

Ready to build something real?

Initial conversations are exploratory and obligation-free.

Book a discovery call