Methodology

AI-accelerated development grounded in engineering discipline and modern delivery practices.

Four-phase process

1

Discovery

Clarify objectives, constraints, existing systems, and risks with stakeholders and teams.

2

Architecture

Define system boundaries, data flows, and integration patterns, balancing speed and maintainability.

3

Implementation

Hands-on development using AI-assisted workflows for code, tests, and documentation, with reviews and pairing.

4

Delivery & observability

CI/CD pipelines, automated tests, monitoring, and post-launch improvements.

AI-augmented engineering practices

AI accelerates work but never replaces engineering judgment.

Architecture & design

Use AI to explore design options, highlight trade-offs, and generate documentation while validating decisions manually.

Code & refactoring

Generate scaffolding, suggest refactors, and keep code consistent across services, always reviewed by humans.

Testing

Generate tests from specifications and existing code to increase coverage without slowing down delivery.

Documentation

Keep architecture diagrams, ADRs, and API docs synchronised with the implementation.

Technical foundations

Microservices and APIs Cloud-native infrastructure Infrastructure as code CI/CD and automated tests Security and compliance Observability and metrics

Working with teams

I collaborate with existing teams or act as the first technical hire. The approach is transparent and hands-on, with explicit knowledge transfer so you do not become dependent on a single individual.

  • Pairing and design sessions with engineers.
  • Architecture and code reviews.
  • Short training modules on AI-augmented development.
  • Pragmatic process improvements, not heavy frameworks.

Risk & quality management

Security & compliance

Apply secure design, threat modeling, and compliance-aware practices from fintech and payments projects.

Testing & automation

Build automated tests and pipelines to reduce regressions and manual overhead.

Observability

Implement logging, metrics, and alerts so issues are visible early rather than discovered by users.

Apply this methodology to your product

We can start with a short architecture or delivery review before committing to a larger engagement.