Turn your enterprise into
secure intelligence.
A private RAG layer for your enterprise — deployed in your VPC, grounded in your data, every answer cited.
GG RAG is an enterprise Retrieval Augmented Generation platform that deploys inside your VPC, grounds LLM answers in your private documents, and attaches a citation to every response — with built-in hybrid search, row-level RBAC, and ISO/IEC 42001-aligned AI governance.
Stop Hallucinations.
Start Reasoning.
Generic LLMs don't know your business. GG RAG bridges the gap by ingesting your proprietary documents into a semantic knowledge graph. This ensures every answer is grounded in fact, citeable, and secure.
99.9% Context Accuracy
Advanced retrieval reranking algorithms.
Auto-citation
Every response links back to source PDFs.
Zero Data Leakage
Data never leaves your VPC during inference.
Enterprise-Grade Infrastructure
Vector Database Built-in
High-performance vector storage optimized for billion-scale embeddings. No external dependencies required.
Hybrid Semantic Search
Combines dense vector retrieval with sparse keyword search (BM25) for unparalleled retrieval quality.
Granular RBAC
Row-level security ensures users only retrieve context from documents they are authorized to view.
Real-time Indexing
Changes in your data source (SharePoint, Google Drive, S3) are reflected in the knowledge base in seconds.
See GG RAG in Action
A unified workspace for indexing, querying, and governing your enterprise knowledge.
// Ask any document — answers with citations, scoped by identity.
One platform. Two audiences.
Deploy GG RAG for your employees, your customers — or both. Identity-aware retrieval keeps the right answers in front of the right people.
Inside your organization
An employee copilot grounded in your private knowledge. Departments scope their own corpus and policies.
- HR
- Payroll & compensation
- Leave & time-off policy
- Onboarding playbook
- IT
- Internal knowledge base
- Procurement workflows
- Security policies
- Finance
- Expense & approval policy
- Quarterly reports
- Operations
- Standard operating procedures
- Incident playbooks
Customer-facing assistant
A grounded, citeable agent for your users — public docs, support content, and product knowledge only.
- Customer support
- FAQ & help center
- Product manuals
- Troubleshooting guides
- Pre-sales
- Feature explainers
- Pricing & plans
- Public Q&A
- Compliance disclosures
- Whitepapers & research
How GG RAG Orchestrates Intelligence
A complete end-to-end pipeline for unstructured data.
Your data stays in your perimeter.
GG RAG runs inside your environment. Identity controls who sees what, every query is auditable, and your data never leaves to train anyone's model.
In-VPC deployment
Runs in your cloud or on-prem — no data egress.
Identity-aware RBAC
Row-level access mirrors your IdP, per query.
No-train guarantee
Your data is never used to train models — ours or anyone else's.
Audit-ready logging
Every retrieval and answer is traceable and exportable.
Certifications & Compliance
We align our engineering and data handling with internationally recognized security, quality, process, and AI governance standards, and hold Turkish public sector software authorizations.
Meet the Minds Behind GG RAG
Built by the GGTech team — engineers, designers and AI specialists shipping production-grade software from Istanbul.
Deployed in production by Beyond Creator
External — customer-facing
We deployed GG RAG as the customer-facing assistant inside BeyondReview. It grounds every answer in our own product and review content, cites the exact source so a user can verify it, and gives our team a clear audit trail — exactly what an enterprise AI surface needs.
Frequently asked about GG RAG
Direct answers to the questions enterprise architects, security leads, and AI teams ask before adopting a private RAG layer.
GG RAG is an enterprise Retrieval Augmented Generation platform that deploys inside your VPC, grounds LLM output in your private documents, and attaches a citation to every answer. It ships with built-in vector storage, hybrid semantic search (BM25 + dense), row-level RBAC, real-time ingestion connectors, and audit logging.
Ready to transform your knowledge base?
Join forward-thinking enterprises deploying secure RAG today.