Now in GA — Federated Multi-RAGs Engine v2.6

Go Beyond Vector Search.
Connect the Deepest Context.

The next-generation enterprise platform for federated retrieval and multi-agent reasoning across hundreds of heterogeneous data sources — powered by dynamic knowledge graphs, not just embeddings.

Connected:PostgreSQLConfluenceS3 / PDFsNotionGitHubSnowflakeSharePoint

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Invite-only early access · SOC 2 Type II · Self-hosted Docker images

Trusted by data-intensive teams

NorthStar BankAtlas HealthVector LabsQuantaMeridianHelix AI

The retrieval gap

Ordinary RAG stops at the silo. DeepRAGs connects them all.

When the answer lives across hundreds of databases, drives and document clusters, point retrieval falls apart. Federated routing makes the whole estate addressable.

Single-point RAG

Retrieval breaks across departments, formats and silos
Single-store vector search misses cross-document links
Hallucinations on complex tables, financials and charts
Bloated context windows inflate token bills

DeepRAGs Federated Engine

One query federates every store with smart re-ranking
Graph-RAG reasons across documents like a human analyst
Deep-Parser reads multi-modal layouts with high fidelity
Token-Trimmer cuts API spend by 35%+ on every call

Core engine

Four pillars that turn raw data estates into deep, reasoned context.

Multi-modal ingestion

Deep-Parser

Adaptive parsing for complex tables, financial reports, scanned PDFs and charts — preserving layout and semantics that flat text extraction destroys.

Cross-document reasoning

Graph-RAG

Dynamically builds a knowledge graph over your corpus so the model can associate entities and reason across documents, not just match nearest vectors.

Context compression

Token-Trimmer

Aggressively prunes and compresses context before it hits the model, cutting API token spend by 35%+ while preserving the signal that matters.

Multi-RAGs orchestration

Federated Router

Schedules, queries and re-ranks results from dozens of vector stores in parallel, then fuses them into a single coherent, cited answer.

Interactive stress test

Feel Multi-RAGs scale under pressure.

Add heterogeneous data sources, flip the DeepRAGs optimization engine, and watch system performance respond in real time.

DeepRAGs Engine

Federated routing on

Document libraries6

Code repositories3

Databases & warehouses4

Total connected sources13

System performance

Optimized

P95 Latency

466 ms

Retrieval Recall

94.5%

Token Cost / Query

$0.101

Hallucination Rate

2.2%

Illustrative model. With the DeepRAGs engine, federated routing and Token-Trimmer keep latency, recall and cost stable as your data estate grows — while naive fan-out degrades sharply past a handful of stores.

Engines & architecture

Built for architects who refuse to compromise.

DeepRAGs peels back the mystery of complex retrieval. Every layer — from ingestion to the final cited token — is inspectable, configurable and deployable inside your own cloud.

10+

Vector stores per query

35%

Lower token spend

99.9%

Engine uptime SLA

Federated RAGs Router

Dispatch a single query to ten different vector stores at once, then re-rank and fuse the candidates into one ranked, deduplicated result set.

Hybrid Graph Indexing

Unify dense embeddings, sparse keyword signals and an entity knowledge graph into a single tri-modal index for precision and recall.

Multi-Agent Orchestration

Planner, retriever and verifier agents collaborate over the graph to decompose complex questions and self-check every cited claim.

Private by Design

Air-gapped Docker images, physical isolation tiers, WAF and field-level data masking keep regulated workloads inside your perimeter.

Dev hub

Industrial-grade integration in a few lines.

Ship from prototype to production without rewrites. Connect a store, point at your sources and let the Federated Router handle the orchestration.

Python & .NET / C# SDKs
Docker & private on-prem images
Streaming gRPC + REST APIs
Versioned reference docs

quickstart.py

from deeprags import DeepClient

client = DeepClient(api_key="dr_live_...")

# federate across every connected store
answer = client.query(
  prompt="What drove Q4 churn in EMEA?",
  engine="graph-rag",
  sources=["*"],
  trim_tokens=True,
)

print(answer.text, answer.citations)

Pricing

Elastic compute, provisioned on demand.

Start free, scale to multi-store federation, and graduate to fully isolated enterprise clusters when you need them.

Developer

Explore deep retrieval on a single store.

$0/ forever

Request access

Local single-store RAG testing
Graph-RAG basic reasoning
Community Python SDK
10K queries / month

Scale

Production Multi-RAGs for growing teams.

$1,200/ month