Answers grounded in your data.
Rsquare builds retrieval-augmented generation pipelines that ground every AI answer in your own documents, policies, and records, with citations, for banks, government, and enterprise across the GCC.
Stop guessing. Start citing.
A generic AI answer comes from what a model memorized during training, which goes stale and can't see your internal documents. RAG changes that: every answer is retrieved live from your own knowledge base and cited, so your team can verify it in one click.
A complete RAG capability map.
From document ingestion to citation-backed answers.
Document Ingestion & Chunking
PDFs, scanned forms, spreadsheets, and internal wikis, parsed, cleaned, and split into retrievable chunks that preserve context.
Vector Search & Embeddings
Semantic search over your knowledge base, finding the right passage even when the question doesn't use the same words as the source document.
Citation-Backed Answers
Every generated answer links back to the exact source passage it came from, so your team can verify it in seconds, not guess.
Hybrid Search
Keyword and semantic search combined, so exact terms, IDs, and reference numbers are matched precisely alongside conceptual queries.
Multi-Format Document Support
Scanned PDFs, tables, and ID cards processed with OCR and vision-capable models before they ever reach the retrieval pipeline.
On-Premise Vector Stores
Your embeddings and documents indexed inside your own infrastructure, never sent to a third party, for data that can't leave your network.
Answers your auditors can trace.
Every RAG answer we ship is source-cited and reviewable, so your compliance team can trace any answer back to the document it came from. This is why the Central Bank of Bahrain named Rsquare winner of its Reg-Tech Innovation Challenge, and why our AI runs inside regulated institutions today.
- Source-cited answers
- Full retrieval audit trails
- Human review on demand
Cloud, hybrid, or fully private retrieval.
RAG pipelines architected around where your documents are allowed to live.
Cloud RAG
Frontier models for generation with no infrastructure to manage; document data masked before anything leaves your network.
Hybrid RAG
Your documents and vector store stay in-house; cloud models handle the heavy reasoning over retrieved passages.
On-Premise RAG
Open-weight models and vector store running entirely on your own hardware. Air-gapped capable. Documents never leave your network.
We build RAG in three stages.
Data Audit & Ingestion
We map your document sources and knowledge bases and design the ingestion pipeline around them.
Build & Tune
Our engineers build the retrieval pipeline and tune it against real questions from your team until accuracy holds up.
Operate & Retrain
Ongoing monitoring and re-indexing as your documents change, so answers stay current.
Part of a wider AI practice.
This is one of four specialist capabilities inside our AI practice. Explore the others, or see how they come together.
What we build with.
Where does your team go looking for answers?
That's the knowledge base worth making searchable. Book a 30-minute assessment.
Schedule a Free Consultation