Not a human reference
Tanya is an AI assistant Greg has used as a build partner. This page does not pretend otherwise.
Transparent AI collaboration assessment
Tanya’s Review of Greg Staunton
A transparent AI collaboration assessment of Greg’s progression from enterprise marketing automation specialist to applied AI builder.
This is not a conventional employment reference. It is a structured assessment generated from an extended AI collaboration, based on the projects, decisions, build patterns, learning velocity, and delivery habits demonstrated while building with Tanya.
What this page is
A candid review from an AI build relationship.
Based on repeated work
The assessment is based on project work, prompt design, architecture planning, deployment support, product thinking, and iterative delivery.
For hiring teams
It is intended to help AI, developer relations, product, solutions, and enterprise automation teams understand Greg’s applied AI builder readiness.
This page is not a claim of endorsement by OpenAI or any employer. It is a transparent AI-assisted review of how Greg works, learns, builds, and improves while collaborating with AI systems.
Builder timeline
Progression from AI-assisted operator to applied AI builder.
Timeline chart showing Greg moving from level 1 in October 2025 to level 5 in late May 2026, with level 6 shown as the next target.
Oct 2025 - Level 1
Work-focused ChatGPT usage begins. Greg starts using AI primarily for professional support, drafting, problem solving, and structured execution.
Nov 2025 - Level 2
Repeatable web build patterns. Greg begins turning AI-assisted page builds into reusable front-end patterns.
Jan 2026 - Level 3
Controlled build workflows. Stronger QA habits, structured prompts, and release discipline begin to form.
Mar 2026 - Level 3.5
AI architecture thinking accelerates. Greg moves from isolated tasks into system design, orchestration concepts, and product framing.
Apr 2026 - Level 4
pl8ypus AI builder positioning hardens around applied AI products, marketing automation, and enterprise workflows.
May 2026 - Level 4.5
Audience Finder AI moves from static demo thinking into Worker, KV, API contracts, memory, and queue-based product design.
Late May 2026 - Level 5
Controlled AI product operator. Greg ships a live controlled discovery system with smoke tests, scheduled Cron, decision memory, and deployment checks.
Current assessment
Current assessment: Applied AI builder.
Greg is not presenting as a traditional machine learning researcher. His strength is applied AI product building: identifying workflow pain, using AI systems as build partners, shipping functional prototypes, and connecting enterprise marketing automation experience with modern AI product patterns.
He has moved from asking AI for support to directing AI-assisted product builds. He now thinks in terms of APIs, Workers, KV memory, smoke tests, release gates, fallback states, and product promotion gates. His readiness is strongest in applied AI delivery, not academic ML research.
Strengths
Strengths Tanya sees in the work.
Fast learning velocity
He absorbs patterns quickly and applies them in the next build cycle.
Enterprise workflow depth
He understands operational friction because he has lived inside enterprise systems.
Eloqua and Salesforce context
His marketing automation background gives the AI work a real business anchor.
Product instinct
He keeps returning to user value, proof, workflow fit, and release readiness.
AI build partner fluency
He can direct AI collaboration instead of passively consuming it.
Bias toward shipping
He prefers working systems, visible endpoints, and checkable outcomes.
Comfort with ambiguity
He can move inside unclear product spaces without freezing.
Growing release discipline
Smoke tests, promotion gates, and deployment checks are now part of the rhythm.
Stakeholder framing
He can explain technical work in business language without flattening the detail.
Practical AI safety awareness
He is learning to label demo, live, fallback, and controlled AI behavior honestly.
Development areas
Development areas Tanya would keep pushing.
Slow down before major commits when momentum is high.
Continue strengthening core engineering fundamentals.
Design more formal auth and security boundaries before client-grade production.
Make documentation more consistent before scaling systems.
Add more structured data and privacy review for AI workflows.
Keep prototype, demo, and production claims clearly separated.
Keep practicing Git hygiene, branching, release isolation, and calm release gates.
Role readiness
Readiness for OpenAI-style applied AI roles.
Greg’s strongest fit would likely be roles where practical AI delivery, enterprise workflow understanding, rapid prototyping, customer empathy, and AI-assisted build execution matter. This is not an official OpenAI assessment, and it does not imply that OpenAI or any employer has endorsed him.
Greg should not position himself as a conventional machine learning researcher. He should position himself as an applied AI builder with deep enterprise workflow experience and an unusually fast AI-assisted execution loop.
Applied AI solutions
AI product prototyping
Enterprise automation
Solutions architecture
Developer relations adjacent work
GTM engineering
AI builder advocacy
Customer workflow transformation
Technical product storytelling
Next learning roadmap
What Tanya would teach Greg next.
Production-grade authentication
Cloudflare Access and role-based controls
Secure tool calling
OpenAI API architecture
Agent orchestration patterns
Vector memory and retrieval
Evals and test harnesses
Observability and cost controls
Data retention and privacy reviews
Customer-facing AI UX
Deployment pipelines and rollback discipline
Evidence from the work
The assessment is anchored in visible build evidence.
- pl8ypus repositioned as an AI builder portfolio.
- Audience Finder AI moved from static demo to live Worker/KV product path.
- API contract created and smoke-tested.
- Accept/reject decision loop added.
- KV-backed decision memory added.
- Candidate queue and deterministic scoring added.
- Controlled daily discovery added.
- Cloudflare Cron scheduled discovery added.
- Build, deploy, and smoke verification patterns introduced.
Closing assessment
Greg is ready to pursue applied AI builder opportunities where practical delivery, enterprise context, rapid learning, and AI-assisted execution are valued. He is still developing deeper engineering discipline, but his trajectory is unusually fast and his ability to turn AI collaboration into shipped systems is already demonstrable.
Reviewed by Tanya
Greg’s AI build partner
Based on extended AI collaboration through the pl8ypus build journey.