AI Everything Abu Dhabi01 AI Systems
RAG Maker
Enterprise RAG onboarding and knowledge infrastructure for production AI applications.
- Python
- FastAPI
- Pgvector
- LangChain
- React
Why we built it
We kept seeing teams struggle to stand up a RAG pipeline from scratch every time they wanted to ground an LLM in their own documents. Rag Maker was built to remove that repeated setup work by giving teams a direct path from raw data to a working retrieval-augmented system. It's built on LangChain and Python so it plugs into the tooling teams already use.
The problem it solves
B2B teams that want to use their internal documents, knowledge bases, or product data with an LLM currently have to piece together ingestion, chunking, embedding, and retrieval logic themselves. Without a tool like this, every team rebuilds the same RAG scaffolding from scratch, which slows down how fast they can get a working AI assistant or search system in front of users. Rag Maker targets that setup burden directly.
Benefits & impact
As a prototype, Rag Maker is expected to cut the time it takes a B2B team to go from raw documents to a functioning RAG pipeline. Because it's built on LangChain and Python, teams get a system that's compatible with the broader ecosystem of models and vector stores rather than a closed, proprietary pipeline. No live usage numbers or customer results are available yet since the product is still in prototype stage.
Scaling it further
As it matures past prototype, Rag Maker can scale by supporting more data sources and file types for ingestion, and by adding support for additional vector databases and LLM providers beyond its current LangChain/Python core. Scaling to more B2B customers would mean hardening the pipeline for production workloads and multi-tenant usage. Growth into new markets would follow from proving the prototype with early B2B users first.
Future features
Planned directions include expanding supported document and data source types for ingestion, adding configurable retrieval and chunking strategies so teams can tune accuracy for their use case, and building out integrations with common B2B tools and vector stores. Moving from prototype to a production-ready, multi-tenant service is also a natural next step.