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.
Popular:
| # | Idea |
|---|
No idea matches this filter.
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.
Popular:
| # | Pain point |
|---|
No pain point matches this filter.
LLM Document Extraction
Developers and analysts struggle to reliably and affordably extract structured, schema-correct data from large volumes of messy documents using LLMs.
In their words
It cannot be that the only acceptable level of due diligence in verifying results is for me to re-perform the analysis.
reliably parse all the newsletters I get each day and build a obsidian doc with highlights and relevant links
our users are mostly more or less computer-illiterate... I would like to help them by either integrating LibreOffice with a local LLM
Claude Opus does it beautifully, but at ~₹2.20 (~$0.026) per job that's ~₹44K/day, roughly $16K/month.
I consume so much tokens because I do legal-related work which requires insane amount of document processing, it takes a lot of non-cached hits before it finally stabilises.
Need to be build a high inference document processing pipeline initially and then some agentic stuff later.
And yes, copilot or claude will choke on the data.
You're paying for a reasoning trace on every single document. For a task where the correct answer is fully determined by the input and the schema.
There is immense structural value, working logic, and blueprinting trapped in those chats.
per ambiti come legale dove la precisione è critica? Meglio aspettare modelli italiani specializzati
the only way i got client call pipelines reliable was sticking a pydantic schema between whisper output and the claude prompt to reject anything that lacks an explicit roas target or daily spend cap
I now have hundreds of isolated chat sessions sitting in my history tabs... manually sifting, copying, and sorting through them one by one is an absolute nightmare that would take me weeks.
We can consult Claude for edge cases, create sample dataset and train a the tiny LLM on demand so we go to Claude less.
Cleaning this up was unimaginable before as a "something we can do in a day" project, but was more of a "possible with insane costs"
LLM and RAG developers
Where they talk
- r/Rag
- r/LLMDevs
- r/LocalLLM
- r/LocalLLaMA
- r/AI_Agents
- r/ClaudeAI
- r/ClaudeCode
- r/LangChain
Tools they use today
- Claude
- Qwen
- ChatGPT
- Gemini
- Ollama
- DeepSeek
- n8n
- Llama
Why those fall short
- Claude became terrible, lobotomized, saboteur; needs strict privacy hygiene
- Cheaper models drop extraction quality on messy, unstructured job descriptions
- cache discount only applies to exactly repeated tokens; new docs shift prefix
- Python overhead per call compounds over millions of short documents
- free models limited and unreliable; Claude Max budget exhausted before finishing
Map of ideas
Drawing the map…