Traditional RAG creates risk. In enterprise AI, accuracy and security aren’t optional.
Most vector-only databases are built for speed, but they ignore enterprise realities like security and compliance. Without context, access controls, or accurate recall, they create compliance gaps that make AI unsafe for regulated industries.
At Protecto, we built GPTGuard to change that — making enterprise AI safe by preventing data leaks, enforcing privacy, and keeping compliance intact. Every design choice mattered, especially the foundation of Retrieval-Augmented Generation (RAG).
When it came to the vector database layer, we didn’t just need speed. We needed accuracy, speed, and security – all at once. That meant carefully evaluating our options and selecting the right technology to support our approach. One of those choices was SingleStore.
Where Traditional Vector-Only RAG Falls Short
Most pure-play vector databases are great for fast semantic search, but in enterprise environments, they create roadblocks:
- Context is ignored. Metadata like access rights, legal classifications, or departments gets lost.
- Enforcing access control is tough. Role-based restrictions don’t fit naturally into vector-only systems.
- Hybrid queries are clunky. Questions like “Find contracts from 2024 mentioning VendorX” require complex, brittle middleware.
- Accuracy suffers. ANN indexing prioritizes speed, not recall – a dealbreaker for compliance-heavy industries.
These shortcomings make traditional RAG risky for enterprises where accuracy and security aren’t optional.
Why SingleStore Made Sense for GPTGuard
In building GPTGuard, we evaluated different options and found SingleStore’s hybrid capabilities aligned with our needs. Unlike pure vector databases, SingleStore integrates vector search directly within a robust SQL engine, enabling:
- Native hybrid queries. Vector similarity joins seamlessly with metadata, text search, and access controls.
- Unified data. Vectors and metadata live together, eliminating sync issues and external joins.
- Flexibility. From brute-force recall for sensitive searches to fine-tuned indexing for performance, SingleStore gave us control.
This combination allowed us to deliver guardrails without compromising accuracy – a critical requirement for our customers.
How GPTGuard Builds on This Choice
In GPTGuard, security starts at ingestion:
- Sensitive data (PII/PHI) is automatically masked.
- Entities like contract clauses, account numbers, or patient IDs are extracted and tagged.
- Metadata is generated to pair structured filters with semantic embeddings.
With this approach, GPTGuard can support complex enterprise queries like:
“Fetch all contracts created after Jan 2024 mentioning VendorX, semantically similar to ‘termination clauses’ – but only if the user belongs to Legal.”
That level of control and precision is only possible with the right architectural choices – and SingleStore was one of them.
Raising the Bar for Enterprise RAG
By choosing SingleStore as part of our stack, GPTGuard redefines what enterprise-grade RAG can deliver:
- Accurate retrieval across huge document sets
- Built-in security that prevents leaks and violations
- Hybrid search that reflects real-world enterprise needs
- Scalability without fragile middleware
As our CTO, Baskaran Alagarsamy, explained:
“We selected SingleStore for its hybrid query capabilities. It was one of the technologies that allowed us to secure sensitive data without compromising accuracy.”
👉 Learn more about GPTGuard and how we’re helping enterprises deploy AI that’s both safe and scalable.
Learn more about SingleStore Hybrid Search
