pl8ypus

Applied AI Build Studio / Greg Staunton

Applied AI systems for enterprise marketing workflows.

pl8ypus is Greg Staunton's applied AI build studio - focused on production-shaped systems for marketing automation, translation, customer operations, and workflow control.

Greg's take

Most AI projects do not fail at the model call. They fail in the workflow around it - bad handoffs, unclear review points, dirty data, weak evidence, and nobody owning what happens when the output is wrong.

pl8ypus is where I build the control layer around that problem. Translation, form protection, visitor identity, client operations, campaign learning, audience selection, and build orchestration are now different surfaces of the same operating stack.

The model matters. The system around it matters more.

What changed recently

The builds are becoming one operating ecosystem.

Translation AI became product-shaped

Now positioned as a protected enterprise translation workflow with provider evidence, protected terms, glossary handling, human review flags, request logging, and language-specific quality agents.

Turnstile moved beyond POC

The Eloqua form protection layer now has clean direct forwarding, exception review, accept/reject handling, audit retention, and dynamic field preservation.

Eloqua identity capture became a pattern

Visitor GUID capture is now documented as a form identity stitching layer using hidden fields and a SYSTEM - Eloqua ID CDO downstream.

Client Portal became a delivery asset

A lightweight Cloudflare-backed portal for client-specific documentation, requests, email PIN access, and durable delivery records without heavy Jira or Confluence overhead.

Campaign Copilot is emerging

The learning layer is being shaped around historic emails, landing pages, outcomes, blog posts, and project memory for future campaign briefs and reporting.

Tanya moved toward cockpit-led orchestration

Tanya 2.0 is the active cockpit. Tanya 3.0 is the quiet architecture track behind controlled autonomous build loops with human checkpoints.

Background and capability

Enterprise marketing automation

20 years building and operating enterprise marketing stacks in B2B and global enterprise environments.

Oracle Eloqua / Salesforce workflows

Deep platform experience in Eloqua campaign and data flows, SFDC integration, consent management, and reporting.

Cloudflare Workers, D1, R2 and Access

Production builds on Cloudflare infrastructure: Workers, D1 databases, R2 storage, and Access/JWT identity layers.

AI-assisted architecture and implementation

End-to-end delivery with AI assistance at architecture, implementation, QA, and documentation layers.

Human-in-the-loop review systems

Review queues, accept/reject controls, audit trails, and manual override paths built into every AI output layer.

Production-minded QA and deployment

QA gates, acceptance reports, controlled slice-by-slice delivery, and hardening passes before deployment.

Applied AI Systems

Production-shaped builds.

View all systems
Build 01 Active

Translation AI

Problem: Marketing translation done manually, inconsistently, with no protected-term enforcement or review trail.

Protected enterprise translation workflow for regulated marketing assets. Live translation, provider evidence, protected terminology, glossary handling, request logging, QA gates, review status, and human approval before output is treated as final.

Anthropic API Protected terms QA gate Human review Language agents

Proves repeatable AI workflow design with control layer and evidence capture - not just a prompt.

View system →
Build 04 Architecture

Eloqua Visitor Identity Layer

Problem: Eloqua form submissions often arrive without a durable visitor/contact stitching key.

A page-level identity capture pattern that reads the Eloqua visitor GUID where available, injects it into a hidden form field, submits it with the form, and stores it in a SYSTEM - Eloqua ID CDO for downstream matching and reporting.

View identity layer →
Build 02 Intelligence layer

Marketing Intelligence AI

Problem: Marketing teams need campaign, competitor, channel, and website intelligence pulled into one decision layer.

Campaign and market intelligence system for analysing LinkedIn activity, search signals, website behaviour, campaign performance, and cross-channel opportunities. Built as a boardroom-style intelligence layer for marketing teams that need evidence before campaign decisions.

Market intelligence Campaign analysis Competitor signals Dashboard UX

Shows how AI can support marketing strategy, not just production tasks, by turning scattered signals into operator-ready insight.

View system →
Build 03 Migration in progress

Client Portal

Problem: Client support workflows live across email, files, invoices, status updates, and memory.

A portal sounds simple until it has to behave like a real support system. Requests, updates, documents, billing visibility, attachments, admin controls, audit trails, identity, and multiple users attached to the same client account.

That is where the toy version breaks. This build is about earning production one layer at a time: prototype the workflow, prove the client model, move the writes to D1, add identity, harden the access path, and keep every slice testable.

Cloudflare D1 Workers API Access/JWT Multi-client Request lifecycle Audit logging

Proves Greg can build the operational layer around client work, not just AI demos. This is the kind of system that forward deployed engineers design inside real customer environments.

View full case study →
Build 04 Enterprise used

Marketing Form Protection

Problem: Enterprise form spam pollutes Eloqua data, damages campaign reporting, and wastes sales time across marketing automation workflows.

Cloudflare Turnstile and Eloqua protection layer with exception queue, direct Eloqua forwarding, accept/reject handling, and multi-form architecture. Applied to a real enterprise Eloqua marketing environment, with client details anonymised.

Cloudflare Turnstile Workers KV storage Eloqua API

Enterprise-used workflow. Proves Greg can build applied AI-adjacent protection around real marketing operations, not just isolated demos.

View system →
Build 05 Active

Audience Finder AI

Problem: Audience targeting decisions made without structured evidence, scoring criteria, or review controls.

AI-supported audience and targeting workflow with scoring, evidence trails, and manual accept/reject controls. Includes queue management, export readiness, Cloudflare Worker backend, and KV memory layer.

Cloudflare Worker KV memory Accept/reject Scoring

Demonstrates human-in-the-loop design with backend infrastructure, not just a frontend demo.

View system →
Build 06 Operational V0.1

Weekly Build Agent

Problem: Build progress disappears into chats, commits, and memory notes unless it is captured into reusable content.

A local agent that reads Tanya memory and build session records, then produces draft-only weekly outputs for blog posts, email updates, and LinkedIn posts. Human review stays in the loop. No auto-publishing, no external AI calls, and no live system changes.

Tanya memory Build sessions Draft generation Human review

Turns real build evidence into reusable marketing assets while keeping Greg in control of what gets published.

View system →
Build 07 Operational

Tanya Build Cockpit

Problem: AI-assisted build sessions drift without structure, memory, or consistent QA checkpoints.

AI-assisted build orchestration layer using structured prompts, project memory, QA gates, implementation packets, and agent role separation. The operational scaffolding behind every pl8ypus build.

Structured prompts Project memory QA gates Agent roles

Shows the build discipline and AI collaboration layer that makes the other systems production-shaped rather than experimental.

View system →

Featured case study

Client Portal: from lightweight support idea to production-shaped Cloudflare system.

This is not a UI prototype dressed up as infrastructure. It is a business system being progressively migrated from localStorage to a Cloudflare-backed architecture - with documented slice boundaries, QA acceptance at each layer, and clear distinction between what is prototype, migration-ready, and production-hardened.

Identity

Cloudflare Access / JWT

Secure client login without managing auth infrastructure from scratch.

Data layer

Cloudflare D1 + Workers API

Structured D1 database with Workers API routes added slice by slice.

Client model

Multi-client shared instance

Everyone in the same client org sees shared requests, billing, docs, and attachments.

Build method

Controlled slice-by-slice

Each migration slice has acceptance criteria, QA gate, and deployment verification.

Request creation, comments, status changes toward D1/API
Documentation metadata and body endpoints
Hours logging and billing visibility
Admin controls and audit/activity logging
Production hardening pass on portal.greg-staunton.com
R2 storage planned for file attachments

Operating model

How Greg builds.

A repeatable pattern for moving from workflow problem to deployed, operator-ready system - without skipping QA, documentation, or production-readiness checks.

01

Discovery and workflow mapping

Understand the existing workflow, the people in it, and where AI changes the decision boundary.

02

System architecture

Design the smallest complete system: inputs, outputs, review layer, data model, and deployment shape.

03

AI-assisted implementation

Build with structured prompts, project memory, implementation packets, and clear agent role separation.

04

Human review and QA

QA gates, acceptance criteria, test auth flows, and edge case verification before each slice is considered done.

05

Controlled deployment

Deploy slice by slice with verification at each step. No big-bang releases. Rollback path is always clear.

06

Evidence capture

Document what was built, what decisions were made, and what the system does at a level operators can trust.

07

Repeatable patterns

Every build produces reusable scaffolding: prompt patterns, QA checklists, architecture templates, and deployment scripts. The next build starts from a higher floor.

Marketing systems expertise

The domain knowledge underneath the AI layer.

Applied AI systems are only as good as the underlying domain knowledge. The marketing operations background shapes every system architecture decision.

Oracle Eloqua

Campaign setup, data processing steps, program canvas, form processing, API integration, and platform governance.

Salesforce campaign and data flows

Lead routing, campaign member tracking, CRM sync, data quality rules, and bidirectional Eloqua-SFDC integrations.

Consent and GDPR workflows

Subscription management, consent capture, preference centers, audit-ready data handling, and compliance workflows.

Email and landing page operations

Template production, asset governance, dynamic content, A/B testing, and campaign QA before deployment.

Form handling and spam protection

Form validation, spam review, Turnstile integration, exception routing, and CRM quality protection.

Translation workflows

Localisation planning, asset versioning, protected-term management, multi-language review, and platform-ready output.

Campaign execution support

Campaign brief to live management, stakeholder coordination, asset sign-off, scheduling, and post-campaign analysis.

Reporting and operational dashboards

Performance tracking, attribution models, stakeholder reporting, and dashboard design for non-technical operators.

Value positioning

Built for the gap between AI models and real business systems.

Most AI value is created after the model call: workflow design, data shaping, review controls, user adoption, deployment safety, and operational reliability. That is the layer pl8ypus is built around.

The systems in this portfolio exist because understanding how a marketing team actually works - the data flows, the approval steps, the form constraints, the platform limitations - is just as important as knowing how to call an API.

Customer workflow integration

Built to fit inside existing marketing operations - not to replace them.

Production applications

Not demos. Systems with data models, review layers, error paths, and deployment evidence.

Repeatable patterns

Build scaffolding, QA templates, and architecture patterns that transfer to new customer contexts.

Systems non-technical operators can use

Marketing teams drive the review queue. Operators control the output. AI does the heavy lift in the middle.

Contact

Discuss a build, project, or collaboration.

For applied AI project enquiries, marketing systems support, speaking requests, and collaboration around practical AI systems for enterprise environments.

About Greg
Applied AI project
Marketing systems support
Speaking or demo session
Collaboration or general enquiry