Turn manual workflows into productized features [Extra Prompt Gift]
A simple playbook to turn a manual task into paid SaaS
Before we dive into today’s post, we have something big to share.
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Hey there,
Most “custom work” you do more than twice is a hidden SaaS feature waiting to be shipped. Even if it sounds weird, that’s exactly what it is.
If you’re a solo dev or PM, you’ve probably repeated the same deliverable for different clients, for example:
audit -> report
dataset -> cleanup + summary
transcript -> insights deck.
The work compounds zero. The reason isn’t skill, it’s packaging.
Your steps live in your head or in messy docs, so they never become software anyone can buy twice. AI changes this. Let LLMs make the judgment calls; let deterministic code do the guardrails. Wrap it cleanly, meter it, charge for it. That’s how you move from “busy weeks” to “compounding revenue.”
Today, we’ll turn one repetitive consulting workflow into a single, shippable feature.
We’ll cover:
How to extract the repeatable core from past gigs (and ignore the snowflake edge cases)
Where to insert AI for classification/extraction/summarization—safely
How to package, meter, and price it so you can launch fast
Here’s the plan.
4 Steps to Ship a Productized Feature with AI, even if you “don’t have time”
You don’t need a platform. You need one narrow feature customers pay for repeatedly.
Quick transition to the main points: Start from an existing deliverable you already sell. Encode the 80% that repeats. Automate the guts, gate the weird 20% as “manual review” or “pro plan.”
Step 1: Mine your past work for the 80% pattern
Pull 5–10 past engagements. For each, write three bullets:
Input you received
Decisions you made
Output you delivered.
Circle what’s identical across projects (file types, columns, checks, report sections). That intersection is your Feature v1. Give it a name and a promise: “Upload CSV -> Clean, standardize, and get an insights brief in 10 minutes.” If it requires edge-case wizardry, it’s out (for now).
That’s how you ship in days, not quarters.
Step 2: Draw the pipeline before writing code
Sketch the happy path:
eg. Ingest -> Validate -> Transform -> AI judgment -> Assemble outputs -> Deliver.
Under each step, list the deterministic rules (regex, JSON schema validation, column checks) and the AI calls (classify rows, extract entities, summarize). Deterministic pieces give you reliability.
AI adds the judgment that makes your consulting valuable.
Step 3: Add guardrails where AI decides
Anywhere the LLM classifies or summarizes, wrap it: schema validation, allowed-enum checks, profanity/PII filters, confidence thresholds -> fallback to “needs review.”
Place guardrails at input, retrieval, and output so your app is predictable and safe (think: prompt injection, type safety, and output schemas). Monitor these checks so you know when/why the AI needed a redo.
Step 4: Productize: contract, metering, price
Expose one clean contract: API (POST /jobs) or a super-simple UI (Upload -> Processing -> Result).
Log every step; store artifacts (input, normalized data, generated brief). Meter usage as events (“files_processed”, “rows_cleaned”, “tokens_used”) and start with a “Builder” plan + overage. OSS options like Lago or OpenMeter make usage-based billing feasible from day one, no lock-in.
Shipping checklist (keep this handy):
Contract: one route or one page; no settings explosion.
Observability: structured logs + job timeline.
Idempotency: safe retries on uploads and webhooks.
Data retention: auto-delete sensitive inputs after N days.
“Manual review” escape hatch: catches the weird 20% without blocking revenue.
🥪 The P.A.N.I.N.O. Framework
Because every great workflow has layers.
After you’ve mapped your inputs, decisions, and outputs, use P.A.N.I.N.O. as your quick checklist to turn them into a repeatable system.
P — Pattern
Find the repetitive spine hiding across your client work. That’s your base.
A — Automate
Replace manual logic with rules and AI where judgment is needed.
N — Normalize
Standardize inputs and outputs. Consistency makes automation possible.
I — Integrate
Connect steps into one continuous flow — APIs, webhooks, or n8n.
N — Nurture
Add guardrails and monitoring. Keep human review for edge cases.
O — Output
Deliver the result as a clear, sellable feature — ready to meter, price, and ship.
Build your automation like a P.A.N.I.N.O. — same core ingredients, different flavors every time.
That’s it.
8 Hours → 20 Minutes With AI
Every software engineer has that one task they’ve done so many times they could almost do it blindfolded: cleaning CSVs, summarizing reports, analyzing logs, you name it.
It’s valuable work, but it doesn’t scale. You trade hours for outcomes, and once the project is over, the process goes back into your brain (and maybe a few notes). That’s the problem this week’s prompt solves.
It acts like a workflow engineer that listens to your process and maps it into something structured, automatable, and sellable. Instead of asking you to write specs or diagrams, it reverse-engineers your steps and exposes the hidden pattern that’s already a product idea.
Here’s how to use it effectively:
Describe your task: just a few sentences on what you do manually for clients.
Run the prompt, let AI break it into steps, mark AI vs. code tasks, and identify inputs/outputs.
Review and adjust, check if the flow makes sense, and add real examples or data types.
Refine into a feature, use the AI’s summary to define one “Upload -> get …” feature you could package or automate.
This prompt will help you turn what you already know into a blueprint for your first productized feature, ready to automate, brand, and ship.
You are a Workflow Automation & Productization Expert.
## GOAL
Turn a manual client service into a repeatable, monetizable SaaS workflow.
I will describe the current process in 2–3 sentences (in plain language).
You will translate it into a **clear, monetizable automation concept**.
---
## INSTRUCTIONS
1. **Decompose the process**
- Rewrite it as a 3–7 step workflow.
- Each step = {Action} → {Goal}.
- Label each as 🧠 “AI Needed” or ⚙️ “Rule-Based”.
2. **Define system blueprint**
- Inputs: what the user provides (files, text, etc.)
- Outputs: what they receive.
- Value Summary: plain-English statement of the transformation (“Messy input → clear output”).
3. **Define monetization lever**
- Suggest 1–2 metrics that naturally scale with usage (e.g., per file, per dataset, per report, per workflow run).
- Briefly explain *why* that metric aligns with perceived value.
4. **Craft a micro pitch**
- Format: `Upload → get [Result]`
- Must be ≤ 15 words, tangible, and outcome-oriented.
5. **Positioning insight**
- Suggest one simple way to communicate the value fast (landing page hook, tagline, or demo idea).
- Keep it non-generic; avoid “save time” or “automate your workflow” clichés.
6. **Market opportunity**
- Identify one reason this workflow is especially relevant *today* (trend, pain, or shift that makes it timely).
7. **Future leverage**
- Suggest 1 enhancement that increases stickiness, recurring revenue, or user lock-in (e.g., dashboard, API, historical insights, templates, etc.)
---
## FORMAT RULES
- Output sections with clear headers:
1. **Workflow**
2. **Automation Map**
3. **Product Blueprint**
4. **Monetization**
5. **Product Pitch**
6. **Positioning**
7. **Market Opportunity**
8. **Future Leverage**
- Use concise bullets, not long paragraphs.
- Use plain, concrete English.
- No filler phrases (“Of course”, “Sure!”, etc.)
---
## SELF-CHECK
At the end, include a short line:
> “Quality Check: This workflow is most automatable because [1-sentence reason].”
---
Here’s my process:
[Describe your task here in 2–3 sentences, like:
“Clients send me messy Excel files with store sales. I clean the data, fix column names, find outliers, and send them a summary of trends.”]🎁 Bonus for Builders
Now it’s time for a gift for you! We’re adding a little gift for the readers who go the extra mile this week.
We want to gift you an exclusive bonus prompt that takes everything the main prompt generates and turns it into a ready-to-import n8n workflow, complete with triggers, nodes, and error handling structure.
It’s the missing link between your idea → automation plan → real running workflow.
Wanna have it?
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Here’s what you learned today:
Productize a slice, not a platform: pick the 80% repeatable spine and ignore edge cases early.
Blend deterministic rules with AI judgment—then guardrail the AI so it’s predictable.
Package with a clear contract, metered usage, and simple pricing so you can sell it tomorrow.
Apply it this week.
Choose one deliverable you already do for clients, write the input -> steps -> output on a page, and draft your pipeline. If you’re orchestration-curious, browse n8n’s templates, then slot your own rules and prompts into a test flow.
Action step: Ship a private beta to one real user. Limit scope to one file type and one success metric (e.g., “<2% validation errors”). Charge a starter price. Iterate only after you see a completed job and a paid invoice.
Hit reply and let us know why.
Now go build!
See ya next week,
— Ale & Manuel
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