/ Business system
RAG for Enterprise
Q&A over your internal documents — built to handle 1 TB of content.
← All work- Pricing model
- Custom
- From
- Custom
- subscription or on-prem
- Status
- Live
- Released
- Q3 2026
- Sainskerta's role
- Architecture and build of a large-scale RAG pipeline, retrieval, guardrails and UI/UX.
/ About the product
StackNext.jsPythonVector DBPostgreSQLLLM/RAG
An enterprise-grade Retrieval-Augmented Generation platform: indexes and answers from very large document corpora (up to 1 TB+) with sourced answers (citations), access control and guardrails — for knowledge search and internal copilots.
RAG for Enterprise turns piles of documents (policies, contracts, manuals, research) into a "brain" you can question in everyday language, with answers cited back to the original source. Built for scale: ingestion & chunking pipelines, vector storage, hybrid search, re-ranking and LLM orchestration — with per-document access control, auditing and hallucination mitigation.
/ Features already shipped
What's in the box, not promises.
- Ingest & index large corpora (1 TB+ of documents)
- Answers cited to their sources (anti-hallucination)
- Hybrid search (vector + keyword) + re-ranking
- Per-document access control & auditing
- Internal knowledge copilot & API
- Guardrails & answer-quality evaluation
- Model-portable (multi-LLM)
/ Ways to use it
Enterprise / LicenseCustomsubscription or on-prem
- On-prem/cloud deployment
- Data-source connectors
- Access control & auditing
- Implementation support
/ Similar work
Try RAG for Enterprise on your data.
We'll set up a sandbox with your branding within one business day, then discuss what needs adapting.