SaaS ideas your next customers are already looking for
100 SaaS startup ideas for the people who said they'd pay, from agency owners to dentists. Micro SaaS ideas, AI SaaS ideas and B2B SaaS ideas: find yours, build it, sign them up.
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Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
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Data Quality Breaks Automation
Messy, inconsistent, and unmonitored data silently breaks AI, automation, and reporting pipelines, forcing teams into manual cleanup and firefighting.
In their words
A lot of what's sold as "AI automation" is really an unpaid data-cleanup project with a chatbot on top.
They hardcoded dates. They hardcoded accounts. They used different inputs for the same business concepts.
I can't believe how screwy these financials are.
file shares nobody's touched in years, no consistent access model, duplicate or conflicting versions of the same document across SharePoint, email, and an old file server
I was told that all analysis work is done within Excel. I don't know exactly what the date will look like but I do know it will be messy.
I am honestly so overwhelmed in trying to figure out how to whack-a-mole garbage numbers that are extremely not my fault.
I have a daily report that I need to complete before noon, and a large part of the process is currently manual.
My data has so much undocumented institutional knowledge required to get any accurate insights
The median data error rate across our engagements is 14%. Teams always guess 5-10%.
most supply chain data is messy as hell, you can't just plug in some fancy AI and expect magic when your inventory records are wrong half the time
it feels like 70-80% goes to fixing inconsistent data sources, reconciling numbers that don't match between systems
it turns out the analyst used the column clearly named "status B" to fill "status A" in the report.
they didn't realize one of their key data hasn't been updated for 2 years, until I join them
we had regular 10 second responses on our main flows (bad data model) and still couldn't get buy-in to fix it
Data and automation teams
Where they talk
- r/dataengineering
- r/databricks
- r/analytics
- r/AI_Agents
- r/Accounting
- r/salesforce
- r/FPandA
- r/SaaS
Tools they use today
- Excel
- Claude
- Python
- Databricks
- dbt
- Power Query
- Salesforce
- Slack
Why those fall short
- hardcoded logic, spaghetti code, no standards, vendor leaves after MVP
- no proper pipelines; legacy systems; no data validation on input forms
- existing tools aren't great; documents untagged; no consistent access model
- Excel is not designed for large messy datasets
- large files, repetitive manual matching and classification
Map of ideas
Drawing the map…