Hybrid Retrieval Engine
Combine semantic search, BM25, and RRF reranking for more accurate and context-aware AI retrieval.
Build AI retrieval
Fast & reliable RAG.
Deploy production-ready retrieval pipelines, semantic search, embeddings, and contextual memory through one unified platform.
Everything your AI stack needs to
retrieve better context.
From ingestion to retrieval, manage the complete RAG workflow through a single API platform designed for modern AI applications.
Graph Memory Layer
Build persistent AI memory using connected knowledge graphs and contextual relationships.
Memory Hierarchy
Structure AI memory across buckets, threads, sessions, and long-term context layers.
Agentic RAG
Enable autonomous agents to retrieve, reason, rerank, and respond using multi-step workflows.
99.99% Uptime SLA
Built for mission-critical AI workloads with reliable infrastructure and uninterrupted performance.
Works with the AI tools you
already use.
Plug directly into your existing AI workflow and infrastructure stack.
Built for high-performance
AI retrieval.
Production-ready infrastructure designed to scale retrieval pipelines, vector systems, and AI workloads without operational complexity.
2048D Vector Support
Store and process high-dimensional embeddings optimized for advanced semantic retrieval systems.
Fully Serverless
Deploy retrieval infrastructure without managing servers, scaling, or operational overhead.
256-Bit Encryption
Enterprise-grade encryption for secure vector storage, retrieval pipelines, and data transfer.
MCP Integrations
Connect seamlessly with your favorite AI tools, workflows, and external applications.
Millisecond Retrieval
Deliver fast semantic search and context retrieval with ultra-low latency infrastructure.
Auto-Scaling Infrastructure
Scale storage and retrieval automatically with infrastructure built for growing AI workloads.
DEVELOPER EXPERIENCEIntegrate retrieval pipelines
Integrate retrieval pipelines
with your existing stack
Use your preferred language, framework, or AI workflow to deploy and query production-ready RAG infrastructure through simple APIs and SDKs.
import { GigaRAG } from '@gigarag/sdk';
const client = new GigaRAG({ apiKey: process.env.GIGARAG_API_KEY });
const { matches } = await client.retrieve({
index: 'docs',
query: 'How does hybrid search work?',
topK: 5,
rerank: 'rrf',
});
console.log(matches[0].text);All your questions.
Answered
Everything you need to know about deploying scalable AI retrieval infrastructure.
Retrieval-augmented generation infrastructure is the layer that stores your content, embeds it, and fetches the most relevant passages at query time so a model answers from your data instead of guessing.
Bring your own from OpenAI, Gemini, Claude, or any open-source model, or use ours. Vectors up to 2048 dimensions are supported, and you can mix models across indexes.
No. Indexes are provisioned, sharded, and scaled for you. There are no clusters to size and no nodes to patch.
Files, object storage, databases, and SaaS apps via our connectors, plus anything you push through the ingestion API or an MCP server.
Typical hybrid queries return in well under 100ms at p95, including reranking, from the region closest to your workload.
Yes. Semantic search and BM25 run together and are fused with reciprocal rank fusion, so you get both exact keyword matches and conceptual recall.
Yes. Use the REST API, an official SDK, or the MCP integration to drop retrieval into the agent framework and tooling you already run.
Storage and retrieval scale automatically with your workload. You pay for what you use rather than for provisioned capacity.
Data is encrypted with AES-256 at rest and TLS in transit, isolated per tenant, and never used to train models.
Yes — a documented REST API plus first-party SDKs for Node.js, Python, Java, Kotlin, and Swift.