Build RAG at Scale
Built for Deep Research. Ready for Machine Speed.
Generative AI is evolving from answering questions to conducting deep research. Today’s AI agents issue hundreds of retrievals per session to investigate complex topics, ground insights with evidence, and generate reliable, contextual answers. This is beyond the capabilities of conventional retrieval stacks. Deep research requires a new foundation.
Why Deep Research Breaks Traditional RAG Systems
Most RAG implementations are stitched together from vector databases, external rerankers, and brittle infrastructure. While that may work fine for simple question answering, these systems fall short when pressure-tested by agentic workflows because they:
- Can’t enforce symbolic filters or business rules for compliant, controlled retrieval
- Rely on external services for ML reranking, slowing responses, and increasing cost
- Break under real-time update requirements with no live indexing or ingestion
- Can’t join structured and unstructured data on-the-fly
- Suffer latency spikes and throughput drops under multi-hop retrieval
Deep research requires more than prompt engineering. It requires a retrieval engine purpose-built for scale, complexity, and speed.
Vespa solves these problems natively, at scale.
Why Choose Vespa for Deep Research
Machine-speed Retrieval
Deliver answers in milliseconds, even as agents chain multiple hops and issue hundreds of queries
Contextual Intelligence
Run ML models natively to rerank results using embeddings, metadata, and domain-specific logic.
Unified Data Handling
Join structured, unstructured, and embedded data in a single, expressive query.
Production Reliability
Support real-time updates, autoscaling, and granular access controls—without duct-taped integrations.
Perplexity uses Vespa.ai to power fast, accurate, and trusted answers for millions of users.
With Vespa RAG, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.
Vespa: Built for the Demands of Deep Research
Unified Retrieval Engine
- All-in-one platform for retrieval, ranking, indexing, inference, and model execution
- Eliminate glue code and fragmented architecture with native orchestration
Real-time Indexing and Inference
- Feed and update documents continuously—no batch windows
- Run ML models directly at query time for reranking, classification, or scoring
Scalable, Low-Latency Performance
- Handles billions of documents and millions of queries with sub-second latency
- Designed to maintain consistent throughput even under multi-hop agent load
Precision Through Hybrid Ranking
- Combine sparse, dense, and metadata signals in a single hybrid scoring function
- Customize ranking with domain-specific tensors and learned models
Cost-Efficient Elasticity
- Autoscaling infrastructure adjusts to data volume and query demand
- Multi-phase ranking pipelines optimize cost by limiting expensive inference to top candidates
Enterprise-Grade Security & Governance
- Built-in support for secure access control, including document-level permissions and role-based policies
- Encryption at rest and in transit, compliance-ready controls, and support for isolating workloads by tenant
Ready to go Beyond Basic RAG?
With Vespa, you don’t just retrieve documents. You power intelligent systems that reason, rank, and scale. From research copilots to market intelligence platforms, Vespa enables deep research at machine speed.
Explore More
Layered Ranking for RAG Applications
Deep research requires multiple ranking phases to balance precision, latency, and cost. This post shows how Vespa’s layered ranking lets developers combine fast approximate retrieval with deeper model-based re-ranking, enabling RAG pipelines to scale to billions of documents without losing accuracy.
The RAG Blueprint
The RAG Blueprint is a modular application template for designing, deploying, and testing production-grade RAG systems. Built on the same core architecture that powers Perplexity, it codifies best practices for building accurate and scalable retrieval pipelines using Vespa’s native support for hybrid search, phased ranking, and real-time inference.
Advancing HNSW in Vespa
For large-scale vector and hybrid search, efficiency in approximate nearest-neighbor algorithms is key. This post explains Vespa’s enhancements to Hierarchical Navigable Small World (HNSW), showing how these techniques improve recall and latency trade-offs in production environments handling high-throughput AI workloads.