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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Unreliable Enterprise RAG
Building reliable, secure, and scalable RAG systems over large, evolving enterprise document sets is hard due to retrieval, chunking, permissions, freshness, and latency issues.
In their words
the system hallucinates a lot for data points not present in the PDF. It report some value for not reported.
You might find your "LLM problem" is a retrieval problem wearing a trench coat.
Create an internal repository for all of our company PDFs and documents so they can be easily searched and queried in natural language
I do actually need AI for those because they're ridiculously complex to use. I fed them into a RAG system Hermes build and I just ask questions now.
Create an internal repository for all of our company PDFs and documents so they can be easily searched and queried in natural language
it feels like LLM isn't there yet at a level to create these high quality graphs
Standard vector databases have zero concept of time or state.
Every blog post I find demos 20 PDFs in a folder.
Too much chunk overlap meant retrieval kept pulling near copies of the same paragraph and the exception never made it into context.
we hit a massive performance ceiling with standard vector search. our RAG chatbot kept missing relationship logic across multi-system data
Every blog post I find demos 20 PDFs in a folder.
When it couldnt find a real answer, it didnt refuse, it just invented a config flag that doest exist and stated it like fact.
Vector search returning semantically similar chunks from documents that weren't actually the best source of truth.
our core problem seems to be a lack of enough contrast between chunks which makes one-shot pure embedding based RAG extremely difficult and low quality
Enterprise AI and RAG developers
Where they talk
- r/Rag
- r/LangChain
- r/LLMDevs
- r/LocalLLM
- r/AI_Agents
- r/LocalLLaMA
- r/copilotstudio
- r/n8n
Tools they use today
- Claude
- Ollama
- ChatGPT
- Qdrant
- SharePoint
- Docling
- Qwen
- RAGAS
Why those fall short
- stock models insert headers, confuse proposed and final regulations; RAG build is very time consuming
- Judge is noisy, truncates context, and cannot separate claim buckets
- chunking loses structure; vector search misses exact terms; permissions hard
- top-k always returns chunks; no none-of-the-above option; hallucinates absent values
- Daily limits, shallow citations, spreadsheet chunking noise, no real-world scale guidance
Map of ideas
Drawing the map…