AI delivery systems for engineering teams

Ship software faster without scaling the chaos.

I help engineering teams move from scattered AI tools to a governed product factory: specification, architecture, build, tests, deployment, and feedback loops working as one system.

Book a discovery call Explore AxiaCraft

30xfaster delivery from months to hours

~90%lower engineering and delivery cost

4 weeksto install the first governed workflow

Radical Geek engineer orchestrating an agentic product factory — Agents help most when the delivery system is built around them.

01Book a discovery call.

02Install the factory around real work.

03Train the team and hand over.

Current problems 01 / 04

Remediation pressure

Mass exploitation of legacy code is coming. What are you doing about it?

As models get better at finding weaknesses, old estates become easier to attack at scale. The answer is not another policy document. It is governed remediation capacity: agents, tests, review gates, deployment evidence, and release control.

Engineering skills shift

Most teams are still learning how to move from software engineer to agentic engineer.

The path is not just learning new prompts. Engineers need to design work for agents: context, task boundaries, tests, review gates, deployment evidence, and feedback loops they can trust.

Token economics

Token costs are rising. Sending every task to the biggest model is not a strategy.

Agentic engineering needs routing: small models for simple checks, stronger models for judgement-heavy work, specialist models for scan, review, memory, and deployment tasks. Cost control becomes part of the architecture.

Factory infrastructure

I do not just install agent factories. I build the infrastructure they run on.

Useful agentic delivery needs more than agents. It needs context, model routing, memory, review gates, deployment paths, evidence, and people who know how to operate the system.

AxiaCraft

The governed product factory.

AxiaCraft turns a real workflow into a repeatable delivery path: specification, agents, tests, review, deployment evidence, release control, and handover.

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Rembr

The memory and context layer.

Rembr gives agents durable context across sessions: decisions, handoffs, contradictions, temporal recall, and the working memory needed for longer-running engineering work.

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Free resources

Guides for turning AI use into measurable engineering capability.

Guide

Measuring AI Adoption And Impact

Measure workflow change, process impact, quality, risk, people impact, and AI economics.

Guide

Agentic Engineering Patterns

Practical patterns for planning, verification, model routing, CI, review, and multi-agent delivery.

Case study

ProductFoundry AI product factory

How work changes when it is decomposed, routed, checked, reviewed, and moved through gates.

Field notes on agentic engineering, architecture, and delivery.

Field Notes