Fractional CTO / Paul Bradbury

I build AI that works in production. Not just in the demo.

I help businesses design, build, and govern AI agent infrastructure. With the same engineering rigour you'd give any critical system. Not a consultancy. Not a dev shop. One senior technical person embedded in your business.

You know AI matters.
You just can't build it properly yet.

Most businesses don't have a model problem. They have an infrastructure problem, a governance problem, and a "who actually owns this" problem.

01

It works in the demo, breaks in production

You've seen the demos. Maybe you've even built a prototype. But nobody on the team can take it from proof of concept to something the business can rely on every day.

02

No one internally who can design it

You have budget and intent, but no senior technical person who understands how to design AI agent systems with proper standards, governance, and auditability from the start.

03

The second build is as hard as the first

Without standards and infrastructure underneath, every AI initiative starts from zero. The first build should make the next one easier. Most don't.

Assess. Build. Stay.

Three phases. Each one stands on its own. No obligation to continue, but each makes the next more valuable.

Phase 1

AI Operations Assessment

2 weeks

Two weeks embedded in your business. I map your operations, find where AI fits, identify the human middleware holding things together, and build a concrete plan.

Prioritised roadmap with quick wins, meaningful changes, and structural shifts.
Phase 3

Fractional CTO Retainer

Ongoing, monthly

Stay in the business after the build. Review what's running, plan what's next, and be the person you call when something comes up.

Monthly health review, roadmap progress, quarterly architecture review.
AI doesn't create fragility.
It reveals it.

Most AI failures aren't model problems. They're architecture decisions that were already fragile but hadn't been stress-tested yet.

Radiate B2B

A B2B advertising platform with a managed service layer that included campaign reporting and optimisation. The service worked, but it was labour-intensive. Each report took a full day to produce, limiting how many clients the team could support.

I stabilised the platform architecture, rebuilt the team's capabilities, and then designed an AI agent that automated the campaign analysis. The service offering scaled without scaling the headcount. Same quality, fraction of the time.

<1hr
Per campaign report, down from a full day
Scaled
Service capacity without adding headcount

From Synaptic Pixels

Pattern recognition from real systems, applied to decisions leaders are making now.

Automation & Control

If Your AI Guardrails Live in the Prompt, They Aren't Guardrails

Effective AI guardrails must be enforced through system architecture and access controls, not prompt instructions that can be bypassed.

Read article
Delivery & Governance

Adding Controls To AI Isn't Governance

Four architectural properties that must be designed in, not bolted on after something goes wrong.

Read article
Delivery & Governance

When AI Stops Being a Tool

When AI acts rather than suggests, responsibility becomes undeniable. The AI Employee Maturity Model.

Read article

Paul Bradbury

The pattern across every role is consistent: walk into a situation where the technology isn't working, work out why, fix the foundations, then build what the business actually needs.

Tigerspike: scaled EMEA technology from 10 to 70+, delivering for The Economist, Shell, and Emirates Airlines. Radiate B2B: rebuilt the platform and team, then built an AI agent that transformed campaign operations.

Binary Glide applies that pattern to AI agent infrastructure for SMEs. Based in West Sussex, working across the UK.

Paul Bradbury

Start with a conversation

30 minutes. No pitch. No obligation.

Let's talk about
your AI challenge.

Whether you've got a specific problem or want to explore whether this makes sense for your business.