Solo founders
Real product ideas, early customers, rough prototypes, or a workflow that needs to become a product surface.
I help founders and small teams turn messy AI product ideas, internal workflows, repo problems, and manual business processes into working prototypes, automation, and shipped product improvements.
Australia-wide and remote global.
中文也可以。
AI prototypes, internal tools, billing flows, n8n automation, and repo debugging.
Who this is for
I am not trying to teach generic prompting. I work best when there is a concrete product surface, internal workflow, codebase, customer problem, or launch pressure that needs practical implementation.
Real product ideas, early customers, rough prototypes, or a workflow that needs to become a product surface.
Internal tools, AI workflows, admin automation, content systems, and practical integrations that reduce repeated work.
Fast prototypes, unstable AI features, billing validation, model workflow fixes, or production-ready improvements.
Brisbane-based, Australia-wide, and remote global for focused product implementation work.
Selected work
I avoid invented growth metrics. The proof here is practical: a real problem, what shipped, and the public or inspectable surface you can use to judge the work.
My product build surface for AI image/video generation. Work includes generation flows, pricing and credits, public pages, deployment checks, and ongoing product operations.
A small-business ad generation workflow inside ReelRush. The goal is practical output creation, not a generic chatbot or prompt demo.
Internal automation around company work emails, project management, metadata, and structured operational data. Built to reduce repeated manual coordination.
B2B industrial website work across product pages, SEO structure, and sales-support pages for a more discoverable product catalog.
A practical loop for founder builds: brief, inspect, constrain, delegate, verify, and leave behind something usable.
The owner, user, repeated action, existing toolchain, failure mode, and definition of done matter more than the model name.
Runtime bugs often expose unclear scope, pricing truth, missing evidence, or a workflow that was never made inspectable.
A narrow surface is easier to inspect, ship, and learn from. Most useful AI systems start as one reliable loop.
Name the user, workflow, business outcome, and definition of done before choosing the AI stack.
Start from exact errors, logs, screenshots, rows, browser state, and deployed behavior before proposing fixes.
Inspect the actual codebase, live route, function, database, or local process instead of trusting assumptions.
Keep the build small enough to review and ship: one surface, workflow, integration, or agent lane at a time.
Leave behind working files, verification notes, known limits, setup details, and next actions.
Currently taking 1 build sprint and 2 audits per month so each engagement has enough attention.
Capability stack
The useful part is usually not the tool choice. It is turning a vague or unstable workflow into something a customer, operator, or developer can actually use and verify.
Using Codex and Claude Code against the real repo: inspect, change, test, verify, and leave a clear handoff.
Turning rough product ideas into working screens, model calls, data flow, and testable user paths.
Data models, auth-aware workflows, metadata pipelines, access rules, and production checks.
Checkout, credits, webhooks, billing validation, test-mode paths, and user-visible pricing truth.
Product flows around image/video generation: inputs, model routing, output review, credits, and retry paths.
Automation workflows for company emails, project management, metadata, and internal operations.
Tracing failures through code, logs, browser state, data rows, deployments, and exact runtime evidence.
Deployed systems, billing validation, working workflows, docs, and review-ready improvements.
中文也可以。你可以直接用中文说明你想做什么、现在卡在哪里、已有代码/流程是什么、预算和时间线大概是多少。
Services
If the notes and work above match what you are trying to do, these are the shapes I usually use. The outcome should be a shipped artifact, a verified workflow, or a clear handoff, not a strategy deck.
Turn one AI product idea, automation workflow, or agentic coding task into a working prototype or production-ready improvement.
Clarify the user, workflow, build path, tools, risks, and next practical implementation step.
Use coding agents with real repo inspection, debugging, tests, browser checks, and review-ready handoff.
Maintain automations, prompts, billing flows, internal tools, model workflows, and operating docs as the product changes.
| Engagement | Best fit | AUD |
|---|---|---|
| AI Product / Workflow Audit | When the build path, users, workflow, tools, or risk profile needs to be clarified first. | AUD $690-$1,500 |
| 10-Day AI Product / Agentic Coding Sprint | When one AI product surface, workflow, or prototype needs to move fast. | AUD $3,500-$8,000 |
| Agentic Coding / Repo Debug Sprint | When the repo, billing flow, model workflow, route, or runtime behavior needs focused rescue. | AUD $2,500-$6,000 |
| Monthly Retainer | When an active product needs ongoing AI ops, automation, prompts, billing, and workflow maintenance. | AUD $2,000-$5,000/mo |
No. I am Brisbane-based and work Australia-wide. Remote global work is possible for focused scopes, and English or Chinese is fine.
Use an audit when the workflow, data, repo state, or commercial path is unclear. Use a sprint when there is already a clear owner, user, workflow, budget, and definition of done.
The strongest projects have a repeated action, current materials, a human review point, and a first useful output that can be tested against real inputs.
No. Chat With Me helps shape the brief and can copy a draft into the form. The project brief form is the formal submission path.