Quality is a system.
Not a final inspection.
Clear requirements, sound architecture, controlled changes, and visible delivery. The development ecosystem is the foundation of my work. Prototyping and owner's rep support are independent offers, not steps in a funnel.
Put every change through one pipeline.
Code and declarative changes belong in the same controlled delivery process. People should be able to contribute without knowing an IDE or version control inside out, but their changes should still be versioned, tested, and released through the same gates.
I've designed an ecosystem around Salesforce base packages, GitHub Actions, agent skills and prompts, and automated testing. Architecture, coding practice, DevOps, and governance are designed to work together.
- Requirements and specifications that preserve business intent
- Version control for code and configuration
- Automated tests and quality gates before production
- Change management and a shared CI/CD pathway
- Observability and DORA metrics to make delivery visible
Agent-driven actuation depends on the quality gates and test automation being in place. Generating a change and safely releasing it are different capabilities.
The aim: software you can keep changing, trusting, and growing.
Prototype the requirement.
Business users know what they need to accomplish. Translating that into a specification is where expensive back-and-forth and rework often begin.
My AI-driven prototyper builds in a Salesforce scratch org. A business user can describe the idea, see a working version, and refine it. The result includes structured requirements and specifications, so the prototype becomes a shared reference rather than a vague handoff.
A prototype makes the conversation concrete. It does not replace the engineering and testing required for production.
Level the playing field.
You should not have to take the seller's word for the quality of the work. Yet Salesforce customers often lack the technical information needed to evaluate a firm, contractor, or employee, or to turn a business objective into a clear requirement.
AI can make the supplier faster and cheaper to operate without making the client's outcome better. Savings may stay with the supplier, while the client inherits the code, the maintenance, and the risk.
As your owner's rep, I bring independent engineering and business judgment to your side of that conversation: what to build, how it connects to the objective, who can deliver it, and how to evaluate the result.
Fixed-scope support includes project feasibility studies, RFP writing, evaluation rubrics, and technical scoring. The aim is to replace information asymmetry with decisions you can understand and defend.
Define
Business needs turned into requirements.
Assess
Resources and proposed approaches evaluated against those needs.
Set standards
Clear criteria and ways to verify the work.
Evaluate
Delivered work judged against the requirement and standard.
Decide
Cost, quality, and risk made clear for your next decision.
Contractors alone
Bridging the gap
With your judgment in the room
Hiring resources that may not have the experience or competency to take on their project but may initially appear as if they do.
Technical scoring for RFP submissions
Competently created and delivered work that delivers on the expected requirements.
Work, produced with AI or without, that passes the initial smell test but is fundamentally rotten: bugs, data corruption, technical debt, unmaintainable code.
Observability tools on the progress and quality of the work
Work you're not afraid to change as you scale or onboard/offboard products and services.
The meter running with no real relationship to the value of the work being produced.
Incentives are aligned through the RFP, the observability metrics and the controls that enforce standards. It makes it harder, if not impossible, for them to ship junk, which forces them to design properly so they can ship, and then you're benefiting from that proper code.
Predictable costs where increases are driven by additional identified functionality, not devs spinning wheels.
Inconsistent or missing coding standards and practices as a source of technical debt.
Evidence driven
Contractor/firm onboarding
Code that any resource from any team, now or in the future, can pick up and reason about due to its internal consistency and adherence to best practices and clean design.
AI tooling lets firms write code faster and cheaper, but not necessarily pass all those savings onto the client, while the client is inherently absorbing all of the risk of AI.
Primarily the CI and coding standards ones
Protecting the client from absorbing all the risk of AI.
The fundamentals work
with or without AI.
Human developers can produce fragile systems too. The controls belong around the work, not around a particular kind of worker.