SaaS ideas your next customers are already looking for
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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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Unreliable Text-to-SQL Agents
AI agents generating SQL from natural language produce confident but wrong or incomplete results on messy, undocumented schemas lacking business context.
In their words
i burned a stupid number of edits on my app fixing database columns the ai invented that never existed
judgment work is still human evenings dressed up as GTM engineering
an ai analyst was querying a deprecated table for 6 weeks and nobody caught it becuase the answers looked completely reasonable
o agente escreve um SQL plausível, que roda limpo e devolve o número errado sem erro nenhum pra te avisar
the agent turns out to be quite slow and tend to crash the capacity
having to explain every single edge case and nuance can be so time consuming to the point where it’s just faster for me to write the SQL myself
we spent the last week dealing with a massive headache: database integration
we did a pilot about a year ago with a few different offerings including the ones above and decided they weren't quite ready, but we've gotten some renewed pressure to try to get something working.
people just use the dashboard to validate the ai, that confuses me as you had to check the dashboard anyway what’s the post of asking an ai?
The failure mode isn't an error. It's a number that's slightly wrong and totally believable.
Pure local SQL from a small 7 to 14B model is still a bit shaky on complex joins in my tests.
after adding a second fact entity connected to the same dimension entity, ontology-based queries started failing with errors
the tool couldn't handle what looked like a very simple aggregation
it either doesn’t get it, or get things 90% there and I need to jump in, decipher the query it came up with and then figure how to change it to what I need
Data and analytics teams
Where they talk
- r/AI_Agents
- r/analytics
- r/LLMDevs
- r/databricks
- r/dataengineering
- r/Rag
- r/BusinessIntelligence
- r/LangChain
Tools they use today
- Databricks
- Claude
- dbt
- MCP
- Snowflake
- Genie
- PostgreSQL
- Claude Code
Why those fall short
- requires too much context to understand goals
- model changes, inconsistent answers, no accountability, not reliable enough
- AI answers lack visual context to sanity check them
- semantic layer not maintained; guardrails don't catch wrong numbers
- context failures, not SQL failures, cause 20% wrong answers
Map of ideas
Drawing the map…