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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Messy Data Integration for AI
Organizations struggle to connect, clean, and govern fragmented data across systems so AI tools and agents can reliably use it.
In their words
Lige nu går der meget tid med manuelt at indsamle den data (som er offentligt tilgængelig), selvom min egentlige opgave er at lave modellerne.
Forms only create new records. But sometimes I just need a client to update their own row — address, status, a few fields.
We pay a terrible vendor to do this right now and it's 60k a year.
Lige nu går der meget tid med manuelt at indsamle den data (som er offentligt tilgængelig), selvom min egentlige opgave er at lave modellerne.
I usually do a manual export every other day during a post project, sometimes a few more days go by than I like for comfort.
This bites us maybe once every couple weeks in production, but when it does the user sees wrong numbers and trust goes down fast.
We have people querying our data warehouse A THOUSAND TIMES A DAY.
our only data analyst had left the company and they did not want to hire a new one, so they asked me to build it. They still havent needed to hire one.
We use it for project tracking, light CRM-ish stuff, and a bunch of automations glued to Slack and Google Drive.
Turns out it will do a full refresh of the cached data every time, there is no concept of a timestamp and incremental update.
You have to write/rewrite the data (or the app "settings") through a spreadsheet which, in regular basis, gets staged into Snowflake.
I also haven’t been able to use it to build data flows, or SQLs tables, or build a power bi dashboard from scratch.
I need X, but X involves some data from legacy system Y and legacy system Z, and that's going to take me an hour to glue together
There are a lot of operational needs that exist downstream from dbt models that combine data from the various source systems.
Data and AI teams
Where they talk
- r/MicrosoftFabric
- r/databricks
- r/dataengineering
- r/Supabase
- r/Airtable
- r/PowerBI
- r/salesforce
- r/snowflake
Tools they use today
- Claude
- Databricks
- Excel
- Supabase
- MCP
- Airtable
- Salesforce
- Snowflake
Why those fall short
- no query plan or cost check before running expensive scans
- predecessor left no documentation, models break on column changes
- Basic data quality and legacy issues remain unresolved
- exports drift from pipelines in weeks and nothing signals the change
- not airtight, hard to maintain, breaks integrations, old records uneditable
Map of ideas
Drawing the map…