Software Development
Software Development
Production software built with agentic orchestration — spec-first, adversarial-reviewed, quality-gated. The same methodology applied to your product.
Free 30-minute assessment. No commitments.
100%
Test coverage on delivery
5
Quality gates per feature
0
Lines shipped without review
30+
Years engineering discipline
The Problem
AI wrote the code. Nobody's sure it's correct.
Teams using AI coding assistants move faster — but faster gets you to wrong faster if there’s no verification layer. The output looks plausible. It compiles. It passes a quick review. And it ships with logic errors, security gaps, and no record of how any decision was made.
I build software using AI assistance with the same discipline I’d apply to any production system: formal specifications before code, test-driven development, multi-stage adversarial code review, and full audit trails.
The Methodology
What disciplined AI-augmented development actually requires
Not “AI writes code and we ship it.” Five things that separate verifiable software from plausible-looking output.
01
Formal Specifications
Define what the system must do in unambiguous terms before any code is written. Ambiguity in the spec becomes ambiguity in the code — and usually a bug.
02
Test-Driven Development
Every feature starts with a failing test — constraining AI output and making verification automatic.
04
Audit Trail
Every AI-generated block, gate result, and spec deviation is logged — a complete record when something breaks.
03
Multi-Gate Code Review
Structured review stages — logic, security, integration — with defined criteria and adversarial checking.
05
Delivery Validation
The complete system tested against original specifications. Evidence-based sign-off and a clear statement of what was built.
Common Mistakes
What teams get wrong with AI development
The five failure modes we see in every engagement.
Vibe coding without specifications
“Build a system that does X” is not a specification. AI fills ambiguity with plausible-looking guesses.
No test coverage
Each untested block is a bet that the model got it right. Those bets accumulate until something breaks.
Self-review
The same model that produced a logic error will explain why the logic is correct. Review must be adversarial.
No audit trail
“The AI wrote it” is not a useful post-mortem. You need decision records.
Skipping integration testing
Components working in isolation doesn’t mean they work together.
Deliverables
What you get at the end of an engagement
Software you can maintain, audit, and extend — not assembled AI output. Scope is defined against your specification.
Formal specification document
What the system must do, verified before development begins
Working software
Passing all specified tests, reviewed through all gates
Complete test suite
Full coverage, automated, runnable by your team
Code review records
Gate results documented for every significant component
Audit trail
Decision log from specification through delivery
Handoff documentation
Enough for your team to maintain and extend without me
Getting Started
Start with a conversation.
Describe what you’re trying to build. You’ll get an honest assessment of where you are, what the right approach is, and what it would take to ship software you can actually trust.
Complimentary 30-minute technical assessment. No commitments.
