Searchmaxxed is my business, and I built its AI workflows for creating blog posts, doing SEO and preparing custom proposals. I am not describing a client project or a demonstration. These are the workflows I use on my own work, and they are the reason I can teach this rather than repeat what a vendor told me. Below: what each workflow takes as input, what it produces, and where I still do the work myself.
Key takeaways
- Three workflows carry most of the load: content production, search research and proposals.
- Every one of them starts with source material I already hold. None of them start with a blank prompt.
- Research and judgment stay with me. Drafting, structuring and formatting go to the workflow.
- The proposal workflow saves the most time because a proposal is mostly assembly.
- Reusable instructions are the asset. The second use costs nothing to set up.
The three workflows
| Workflow | Input | Output | What stays human |
|---|---|---|---|
| Blog and article production | Brief, research, real data | A structured draft | The angle, the evidence, the final edit |
| Search and AI visibility work | Search data, competitor pages, live results | Analysis and a plan | Which queries matter and what to do about them |
| Custom proposals | Discovery notes, scope, prices | A scoped proposal draft | Pricing, promises and what I will not commit to |
Workflow one: blog and article production
The failure mode in AI content is obvious to anyone who reads a lot of it: no source material, so the model fills the space with generalities. The fix is unglamorous. Before anything gets drafted, there is a brief holding the query, who the reader is, what the piece has to settle, and the actual evidence.
Context. The business, the audience and where the piece sits. Task. Draft the section, not the whole internet's view on a topic. Source. Real numbers, real quotes, real pages. If I want a figure in the draft, I supply it. Rules. No invented statistics. No unnamed authorities. Flag anything unsupported instead of asserting it. Output. The structure the page needs, with the evidence attached to the claim.
Then I edit. The edit is where voice arrives and where anything hollow comes out. A draft I have not edited is not publishable, and pretending otherwise is how the internet filled up with content nobody finishes reading.
Workflow two: search and AI visibility
Search work is research, judgment and execution, and only one of those three suits a workflow.
The research layer collects: what people search, who currently ranks, what the ranking pages contain, and what AI answers say when asked the same question. That collection is mechanical and it benefits enormously from automation.
The judgment layer is mine. Which queries are worth attacking, which are noise, whether a page should exist at all, and what the business actually gets if it wins. No workflow decides that, because it needs commercial context the data does not carry.
The execution layer is drafting, structuring and checking, which goes back to workflow one.
I use this on my own site and on client work, and the same method sits behind the comparison guides on this site. Each one started with real search data and real competitor pages rather than an opinion. See how the local market compares.
Workflow three: custom proposals
A proposal is mostly assembly. The thinking happens on the call. The document then restates the client's situation, names what will be done, prices it and sets the boundaries.
Source. Discovery notes, the agreed scope, the price structure and my standard inclusions and exclusions. Rules. Use only agreed prices. Never invent a timeline, a guarantee or a deliverable. List anything unclear as a question instead of assuming it. Output. Their situation in their words, the scope, the price, the exclusions, and the next step.
The rules are the whole safety system. A proposal that promises something you did not agree to is worse than a late proposal, so the instruction to flag rather than fill is not a nicety.
What I will not hand over
Pricing. Promises. Anything a client would hold me to. The judgment about whether a piece of work is worth doing at all. Client confidences, which never go into a general-purpose tool as raw material.
The pattern across all three workflows is the same one I teach: the machine drafts from your material, and you approve. Reverse that order and you eventually ship something you did not write and cannot defend.
Build your own version
A full day at Avani Mooloolaba Beach Hotel, A$495 excluding GST, capped at 15 people. Bring the task that costs you the most time and a safe example of it. You leave with the written workflow and the tested output.
For marketing agencies and consultancies the proposal workflow is usually the fastest win, because the inputs already exist and the document is repetitive. See the agency proposal guide, or bring the session to your team.
Frequently asked questions
Which workflow should a small business build first?
The one that repeats weekly, already has written inputs, and gets checked before it goes out. For most service businesses that is quoting or proposals. Use the task selection guide.
Does AI write your articles for you?
It drafts from a brief and real source material. I set the angle, supply the evidence and edit the result. An unedited draft does not ship, and the edit is where the piece stops sounding like everybody else's.
How do you keep AI content from sounding generated?
Give it real material and cut everything that survives deletion. Most generated writing fails because it has nothing specific to say, so it reaches for rhythm and abstraction instead of facts.
Can AI do SEO on its own?
It can collect and structure the research faster than a person. It cannot decide which queries are worth your money, because that needs the commercial context. See the AI SEO workflow guide.
What about client confidentiality in proposals?
Client material stays out of general tools as raw input. Use de-identified examples while building a workflow. How to prepare safe examples.
How long did these workflows take to build?
Each one took an afternoon to get working and several rounds of use to get reliable. The rules are what improve over time, because every bad output points at a missing rule.
Do you need paid AI tools for this?
A paid assistant plan and your own documents is enough for the drafting layer. Automation platforms matter when systems have to talk to each other unattended. Compare assistants, automations and agents.
Is this different from using ChatGPT normally?
Yes, in one way that changes everything: the instructions are written down and reused. Typing a fresh request each time is not a workflow, it is a conversation. Read the five-input method.
Would this work for a marketing agency?
It is the clearest fit on this list, because agencies produce proposals, content and reports from material they already hold. See the marketing agency page.
How do you measure the time saved?
Record how long the task took before, then time it with the workflow including the review. Count the number of runs. Here is the measurement method.