Most RAG systems do not fail because of the model. They fail because of the data. Teams spend days reading documents and hand-writing question-and-answer pairs, ship without ever testing retrieval quality, and then wonder why the chatbot hallucinates. FAQai exists to fix that.
What FAQai actually is
FAQai is our RAG data infrastructure platform. You upload a document and it produces the structured datasets a retrieval-augmented generation pipeline needs, ready to load into a vector database. What normally takes days of manual preparation takes minutes.
It is built for the people shipping AI features: AI and ML engineers, data teams, product teams, agencies, and anyone wiring documents into chatbots, search, or Q&A systems.
How it works
The workflow is deliberately simple:
- Upload a PDF, DOCX, or TXT file.
- Generate five dataset types automatically.
- Export to your vector database in the format you need.
- Test retrieval quality in the built-in Playground.
- Ship with confidence.
Small documents (under 30 pages) typically process in under a minute; 100-page documents take roughly two to five minutes.
Five dataset types from one upload
This is what makes FAQai different. A single upload produces:
- RAG Chunks: smart chunking with heading and structure detection, list handling, overlap, and deduplication, not naive fixed-size splitting.
- Canonical Q&A: clean question-and-answer pairs grounded in your content.
- Query Variants: the many real-world ways users actually phrase the same question.
- Evaluation: multi-hop, cross-section, and ambiguous questions for benchmarking retrieval quality.
- Adversarial: trick questions, edge cases, and out-of-scope prompts that harden your system before users find the gaps.
You wouldn't ship code without tests. FAQai gives you the evaluation and adversarial data to stop shipping RAG systems without them.
Export anywhere
FAQai exports to 16 formats covering 9 vector databases, including Pinecone, Qdrant, pgvector, ChromaDB, Weaviate, Milvus, LanceDB, and Upstash Vector, alongside major ML framework formats. A full REST API and webhooks let you wire it into CI/CD pipelines or automation platforms like n8n and Make, so RAG data prep becomes a repeatable step rather than a manual chore.
Built for confidence and privacy
The built-in RAG Playground lets you test retrieval quality before you deploy, so you can see how your system answers real questions while it is still cheap to fix. And your documents are never used to train AI models.
Getting started
There is a free plan to try it on a real document, with paid plans (Basic, Starter, and Professional) as your volume grows. If you are building anything on top of RAG and still preparing data by hand, FAQai will save you days.
Try FAQai at faqai.app and see your first datasets in minutes.