ᕙ(⇀‸↼‶)ᕗ I would not build “an AI app.” I’d build boring software for one expensive, repetitive workflow.
Your subscriptions are excellent for research, prototyping, code generation, testing, documentation, and marketing assets. They are a poor foundation for a business if the product needs unlimited access to expensive models forever.
The strongest pattern is:
One niche customer → one recurring pain → one measurable result → narrow automation → subscription or usage fee
Focused B2B products are usually easier to monetize than consumer tools because saving a business several hours or one missed lead can justify a monthly fee. Current micro-SaaS research also keeps pointing toward narrow vertical workflows, developer tools, integrations, and back-office automation rather than another general-purpose chatbot.
The five I would test
| Priority |
Product |
Buyer |
Pricing starting point |
Why it survives without your subscriptions |
 1 |
Quote-follow-up system for one trade |
Roofers, installers, agencies |
$29–99/month |
Rules, email, CRM data; AI optional |
 2 |
Niche document/intake workflow |
Accountants, recruiters, property managers |
$49–199/month |
OCR + templates + human review |
 3 |
Website/change monitoring for agencies |
Agencies, e-commerce operators |
$15–79/month |
Scraping, diffs, alerts, scheduled jobs |
 4 |
GitHub maintenance assistant |
Small software teams |
$19–99/month |
GitHub API, static analysis, cheap model fallback |
 5 |
Industry-specific content pipeline |
Realtors, podcasters, local businesses |
$19–79/month |
Templates and batch processing; AI is replaceable |
01 · Quote-follow-up system for one trade
Do not build a generic CRM. Pick one group—for example:
- Solar installers
- Commercial cleaning companies
- Small web agencies
- Equipment repair businesses
- Independent landscapers
- Wedding photographers
The workflow:
text
New inquiry→ qualify lead→ generate quote from template→ send follow-up reminders→ detect reply→ mark won/lost→ request review after completion
The valuable feature is not “AI-generated messages.” It is:
“You stopped losing quotes because the system follows up automatically.”
MVP:
- Lead form
- Customer/project record
- Quote template
- Email follow-up sequence
- Calendar reminder
- Won/lost tracking
- Basic revenue dashboard
Use ChatGPT, Claude, or Gemini to create the first templates and edge cases. Later, most of the system can run with ordinary database rules, scheduled jobs, email APIs, and customer-provided templates.
Why I like it: clear ROI, recurring use, low model dependency, and you can sell it directly to businesses instead of waiting for search traffic.
02 · Document intake and missing-information tracker
Pick a niche that constantly requests documents:
- Accountants collecting tax files
- Mortgage brokers collecting applications
- Recruiters collecting candidate documents
- Property managers collecting tenant paperwork
- Small import/export businesses collecting invoices and certificates
The product does this:
Create client request→ send secure checklist→ receive files→ classify documents→ identify missing items→ send reminders→ export organized folder
AI can classify documents and extract fields, but the core product should still work with:
- File upload
- Checklists
- Due dates
- Reminder rules
- PDF/text extraction
- Manual correction
- Export to Drive or email
Do not start with highly regulated medical or legal decisions. Start with administrative organization, where the software is assisting rather than making a professional judgment.
Pricing: charge per business, not per individual document. A $79/month customer who saves several hours every week is more attractive than hundreds of low-paying consumers.
03 · Website and competitor-change monitor
This is a surprisingly good “small system” because the value is simple:
Watch selected pages→ take scheduled snapshots→ compare text, price, layout, or availability→ send an alert→ keep a history
Possible customers:
- Marketing agencies monitoring client websites
- E-commerce brands tracking competitor prices
- Recruiters monitoring job pages
- Procurement teams tracking supplier pages
- Local businesses tracking government or tender pages
The first version does not need AI. Use browser automation, HTML snapshots, image/text diffs, cron jobs, and email alerts. Add AI only to summarize:
“The competitor changed its annual plan from $99 to $129 and removed the free trial.”
This is a good example of a product that remains functional when your current subscriptions expire.
04 · GitHub maintenance assistant
Avoid building “AI that writes code.” That market is crowded and model-dependent.
Build something operational for small teams:
- Weekly dependency-risk report
- Stale issue reminders
- Pull-request review checklist
- Release-note generator
- Changelog from merged PRs
- License inventory
- Failed CI explanation
- “What changed this week?” digest
- New contributor onboarding report
The basic version can rely on:
- GitHub webhooks
- GitHub Actions
- Repository metadata
- Static analysis
- Dependency scanners
- Deterministic templates
Use an LLM only for summaries and explanations. Customers should still receive a useful report if the AI call fails.
A developer tool is especially compatible with your GitHub Education background, but distribution matters more than implementation. You need a specific audience—such as small agencies maintaining 10–50 client repositories—not “all developers.”
05 · Industry-specific content pipeline
Do not build another general AI writer. Choose one repeated content package:
- Real-estate listing → social posts → email announcement
- Podcast episode → show notes → timestamps → newsletter
- Restaurant menu update → website copy → local listing update
- E-commerce product data → descriptions → marketplace fields
- Local service job → before/after post → review request
The product should be a workflow, not a text box:
Upload source material→ extract facts→ fill niche-specific fields→ generate draft package→ approve/edit→ export or publish
The defensible part is the saved templates, integrations, customer history, brand rules, and niche-specific formatting—not the prompt.
The “subscription expiry” test
Before building, remove the AI calls from your architecture and ask whether the product still has value.
| Product component |
Should depend on an LLM? |
| Authentication and billing |
No |
| Database and customer history |
No |
| Scheduling and reminders |
No |
| File storage and exports |
No |
| Rules and calculations |
No |
| Search, filters, dashboards |
No |
| Classification of messy text |
Sometimes |
| Draft generation |
Optional |
| Summaries and recommendations |
Optional |
| Core customer outcome |
Never |
Use your current subscriptions to build faster, but design the paid product around replaceable interfaces:
generate_summary(input, provider)classify_document(input, provider)extract_fields(input, provider)
Then you can switch between a paid API, a cheaper provider, a local model, or a deterministic fallback without rewriting the application.
How I would use your current benefits
: Build phase
- ChatGPT: product specs, edge cases, onboarding copy, test cases
- Claude: large codebase refactoring, architecture review, documentation
- Gemini: alternative implementations, research synthesis, multimodal testing
- GitHub Education: repository hosting, CI, issue tracking, domains/credits where available
- Free cloud credits: staging, demos, background jobs, and initial testing—not permanent economics
: Revenue phase
Your paid product should eventually pay for:
- Hosting
- Email/SMS
- Storage
- Payment processing
- AI usage
- Monitoring
- Your maintenance time
Set an AI-cost ceiling per customer. For example, if a customer pays $49/month, do not let unlimited generation cost $40/month. Put generation behind usage limits, batching, caching, or a higher-priced tier.
The validation loop I would use
Do this before building a full SaaS:
- Pick one customer type.
- Interview 10–15 people who currently perform the workflow.
- Ask for screenshots, spreadsheets, emails, and current workarounds—not hypothetical feature requests.
- Build a landing page describing one outcome.
- Offer a manual or semi-manual version to the first three users.
- Charge something immediately, even if it is only $20–50.
- Automate the most repetitive part after users repeat the workflow.
The key question is not:
“Would you use this?”
It is:
“What do you currently use, how often does the problem happen, and what did it cost you last month?”
Research on current micro-SaaS patterns consistently recommends validating a narrow, measurable workflow before expanding the product. The main advantage of a niche is not that the technology is difficult; it is that the customer, pain, and distribution channel are easier to identify.
My suggested portfolio
I would not launch five SaaS products simultaneously. I’d build a small portfolio in this order:
| Stage |
System |
Purpose |
| 1 |
One B2B workflow SaaS |
Main recurring revenue |
| 2 |
One small developer tool or monitor |
Low-support secondary income |
| 3 |
Template pack or automation kit |
Cheap product and lead generator |
| 4 |
Niche content site/newsletter |
Distribution for the first products |
| 5 |
API or integration |
Only after repeated customer demand |
A realistic target is not “passive income.” It is:
3 customers → prove the workflow10 customers → cover infrastructure25 customers → meaningful side income50 customers → decide whether to expand or keep it small
If I had your exact resources today
I would start with a quote follow-up and document collection tool for one specific trade or agency niche.
It has:
- A clear buyer
- An obvious business outcome
- Recurring usage
- Low AI dependence
- Simple MVP scope
- Direct sales potential
- A path from manual service → automation → SaaS
Use the AI subscriptions to create the first version in weeks, but make the actual product valuable because it stores workflow data, sends reliable reminders, integrates with existing tools, and prevents money from being lost. That is much more sustainable than selling access to another chatbot.
. I’m willing to build, code, test ideas, automate things and put in the initial work.
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