A precision approach to AI implementation
Build. Train. Enable. End-to-end AI adoption for regulated industries — with governance built in, not bolted on.
Founded by Arthur Ball
I designed and built a proprietary agentic coding system on Claude Code that orchestrates custom-built, multi-agent teams to deliver production-grade software end-to-end—with 100% test-driven development, multi-pass code review, and full audit trails. I configure it for each engagement, whether the target is a traditional application or an agentic system.
I teach teams how to work with AI agents effectively—from foundational concepts through hands-on building of custom agents, skills, and multi-agent architectures. My training programs are tailored to your domain and specific workflows, not generic exercises.
I work alongside your existing engineers to assess where agents add value, design agent architectures that fit your production stack, and get them deployed. This includes bridging the gap between AI prototypes and real systems with the governance your industry requires.
Arthur Ball spent 15 years building trading systems, portfolio accounting platforms, and IBOR engines for hedge funds and institutional investors. He's led teams through a $150 billion AUM asset servicing platform transition, implemented derivatives trading capabilities, and built test automation frameworks that achieved zero prior-detectable production incidents.
When he saw AI transforming software development, he didn't just take courses—he built a system that orchestrates custom-built, multi-agent teams to deliver production-quality software with 100% TDD compliance and enterprise audit trails. See an example project in the Live Demo below.
Now he's applying this methodology to help financial institutions adopt AI properly—with the governance, testing, and controls that regulated industries require.
This is the engine behind the Build capability. A configurable, multi-agent, AI-driven coding harness built on Claude Code that builds production-grade software end-to-end. Purpose-built agents and domain-specific skills are created for each project. Agents coordinate autonomously, enforce 100% test-driven development, and review each other's work across multiple quality dimensions—all governed by defense-in-depth security controls and a relentless commitment to token spend and context management.
A team of 11 custom agents built a full carbon-accounting application implementing ISO 14067 cradle-to-gate methodology end-to-end—handling architecture decisions, PostgreSQL database, REST API design, React frontend, LCA calculation engine, data pipelines, a 4,600+ automated test suite, and multi-pass code review across 6 quality dimensions. All powered by official government emission-factor data. Deployed as a methodology demonstration.
The PCF Calculator lets users model product carbon footprints by selecting materials, processes, and transport modes, using authentic EPA and DEFRA emission factors, then generates detailed breakdowns with Sankey diagrams. Take the Tour in the Live Demo to see it in action.
I don't teach generic AI courses. I build progressive, multi-level training curricula around your team's actual domain data and workflows—so every agent your people build solves a real problem from day one. Each level builds on the last, with real deliverables at every stage, not classroom exercises.
My progressive training curriculum takes non-technical domain experts—people with deep subject matter expertise but zero coding or AI experience—from first principles to independently designing and building multi-agent systems. The curriculum is structured as a progressive skill build: foundational agent concepts, hands-on agent construction, then advanced multi-agent architectures with custom subagents and domain-specific skills.
In one engagement, I built a reference implementation alongside the trainee, including a quantitative evaluation framework. The reference achieved 100% accuracy across all test assertions. During comparative analysis, I identified 3 systematic accuracy errors in the trainee's independently built system—including a calculation error that could have had significant financial consequences in production. The entire curriculum was itself built using Claude Code—a meta-demonstration of the methodology being taught.
I meet teams where they are. I don't rip out what they have—I show how agents integrate with existing systems and workflows, with the governance their industry requires. The goal is to get your engineers building real agent capability, not just taking courses.
I assessed a client's existing production architecture—a Java/Spring Boot backend using one-shot API prompts with no tool use—and identified concrete opportunities to integrate agent capabilities. I produced a comprehensive architecture guide that translated each agent concept from the training curriculum into the engineer's familiar patterns and stack, with working code examples.
The guide offered two implementation paths: a quick-win path adding tool use to existing API calls with minimal refactoring, and a full path adopting a native agent framework with compatible skills. I delivered a sequenced learning-plus-building roadmap—interleaved learning and implementation steps so the engineer was building real capability while learning, not waiting to finish courses first.
What happened when I tried to build enterprise software with AI agents—and the fundamental constraints that changed everything.
How I learned to work with AI agent limitations instead of against them—and the patterns that emerged.
I help organizations in regulated industries adopt AI with the governance and controls they actually need—from system design and agent orchestration to team training and enablement. Let's talk about what AI can do for your business.