Applied AI systems for enterprise marketing workflows.

Translation AI

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

AI-assisted translation workflow for enterprise marketing assets. Provider-agnostic orchestration layer with protected terminology, structured payloads, QA gates, review status, and human approval before output is treated as final.

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

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.

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

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.

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.

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.

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

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.

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

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.

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

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.

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