u/Ogretape

I've been running unfairgaps-os for a while - MIT repo with 4 pipelines that mine court filings, regulatory fines, and enforcement data to find business pain points. B2B angle: what industry-level problem is documented in lawsuits worth solving with a SaaS.

Wanted to extend it to individual professionals. Started off thinking the same court-records approach would work - just narrow it from "construction in US" to "lawyers in US." It didn't. Lawyers don't get sued over the fact that calculating filing fees per court is tedious. Accountants don't get fined because reconciling trust accounts is annoying. The pain a working professional feels every Tuesday isn't in court records - it's in the regulation that says "you must file form X by date Y or pay penalty Z" plus the daily grind of actually doing that.

So I switched approach. Two-stage pipeline:

Stage 1 is WebSearch - 7 targeted queries pulling regulatory facts from .gov, law.cornell.edu, BLS, and professional association sites. Daily routine + documents, regulations + licensing, software they use, jargon, career levels + fears, professional communities, labor market. Output is a structured JSON profile with ~30 specific facts and source URLs per profession.

Stage 2 hands the profile to Opus 4.7 with a deductive prompt and no web access. Given the regulation and daily routine, infer 8-15 specific recurring tasks that would be painful and produce a structured spec for the AI tool that would solve each one.

Loaded 130 US profession profiles into the repo. Ran stage 2 on 25 of them to seed.

Here's the full output from one run - auto detailers in the US - so you can see what actually comes out:

1. Price a detail job profitably (cost-plus, not guess) - calculator
2. Quarterly estimated tax + self-employment tax calculation - calculator
3. EPA stormwater compliance checklist (avoid wash-water Clean Water Act fines) - checklist
4. California Car Wash and Polishing Act registration + bond compliance - checklist
5. Vehicle intake / pre-inspection form (protect against damage claims) - template
6. Ceramic coating warranty + service agreement template - template
7. Sales tax on detailing services - state-by-state lookup - reference
8. Mobile detailer route optimization + travel cost recovery - calculator
9. Chemical inventory + reorder + PFAS compliance tracker - checklist
10. Paint correction estimate from photos + paint depth gauge - advisor
11. Winter cash flow + slow-season pricing strategy - advisor
12. Damage claim response (customer alleges scratches/damage) - checklist
13. IDA Certified Detailer (CD/SV-CD) exam prep + study tracker - reference

The first one is the most obviously buildable. Most detailers eyeball pricing and undercut by 25% because they don't run a real cost-plus formula. The actual output JSON includes the formula (labor + chemicals + the 2026 IRS $0.67/mile rate + 15.3% SE tax + monthly overhead allocation), inputs (10 of them including services list and target margin), and outputs (minimum profitable price, recommended price with margin, breakdown, tax set-aside). That's a $19/mo SaaS already specced out.

Number 3 is the scariest. EPA Clean Water Act civil penalty is $64,618 per day per violation if you dump wash water in a storm drain. EPA has literally put mobile detailers out of business for this. The output is a 12-step compliance procedure with warnings (biodegradable soap is NOT a defense) and citations (33 USC 1311, 40 CFR 122.26).

Each of the 13 has a structured spec like that. Not platitudes, buildable tools.

Honest framing: this isn't a problem interview. It's a discovery funnel. The pains are inferred from regulation + daily routine, not from real users complaining. You'd use this to sift 130 professions in an afternoon, pick 5-10 candidates that sound viable, then spend a week on real customer development to validate. Beats brainstorming SaaS ideas with your roommate.

Repo: https://github.com/AyanbekDos/unfairgaps-os Direct link to the auto-detailer output: https://github.com/AyanbekDos/unfairgaps-os/blob/main/data/professions/us/pains/us-auto-detailers.json

105 profiles still need stage 2 run on them. Takes ~5 min of LLM time each.

tldr: open-source repo finds AI tool ideas per profession by reading regulations instead of guessing. 13 specific ideas with formulas + citations for auto detailers as a real example.