Turn any data into a living knowledge graph

Ingest any data source. Get a knowledge graph that auto-enriches itself. Ask questions that SQL and RAG simply can't answer.

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The Flywheel

Knowledge that grows itself

Your data is scattered across silos

EHR
Claims DB
FDA API
Trials
Patient A
Dr. Chen
Metformin
Lisinopril
Diabetes T2
Renal Unit
+Adverse event
+Off-label use
+Drug interaction
+Comorbidity
Entities0
Omnix Intelligence

Which patients on Metformin also developed renal complications and were treated by doctors who reported adverse events with Lisinopril?

3 patients found

Crossed 4 data sources: EHR records → prescription history → adverse event reports → physician case logs. SQL would require 6+ joins across siloed databases.

4 hops traversed
Ask your knowledge graph...
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4+

Data hops per query

Zero

Schema design needed

91%

Avg. accuracy (10 domains)

Built different

The architecture decisions that make Omnix possible.

Living Knowledge, Not Snapshots

Attach functions to entity types that auto-enrich and refresh your data on schedule. Commodity prices update daily. Company financials refresh quarterly. Your knowledge graph stays current without manual intervention.

Join Your Data With the World

Your private procurement data automatically connects with public market trends, industry benchmarks, and supplier intelligence through a shared ontology. Every new dataset makes every existing dataset more valuable.

Drop a CSV. Ask Questions. That's It.

No schema design. No data modeling. No configuration files. Omnix auto-infers entity types, attributes, and relationships from your raw data. Production-ready knowledge graph in seconds.

Exact Answers, Not Approximations

SPARQL computes over the complete dataset. COUNT returns the real count. AVG computes from every row. Multi-hop joins traverse actual entity relationships. No retrieval sampling, no hallucination.

Agent Ready

Easily connect with your AI agents

Python SDK, REST API, or MCP — pick your path. Integrate in minutes.

Get started in 3 lines

from omnix import Client

client = Client(api_key="your-key")
client.ingest("sales_data.csv", kg="my-data")
answer = client.ask("What's the average deal size by region?")
print(answer)  # "$47,500 across 12 regions"

One-line agent integration

Quick setup

CLI.

Ingest any CSV and start querying in seconds.

npx omnix ingest data.csv --kg my-data
Agent integration

MCP.

Connect any MCP-compatible agent to your knowledge graph.

{ "mcpServers": { "omnix": {
    "command": "npx", "args": ["-y", "omnix-mcp"],
    "env": { "OMNIX_API_KEY": "your-key" }
} } }

Benchmarked against the best

Accuracy across 4 real-world datasets — from multi-hop entity graphs to flat regulatory tables. Compared against DAIL-SQL, LangChain Pandas Agent, and production RAG + Reranking.

OmnixPandas AgentText-to-SQLRAG + Rerank
Accuracy0%0%0%0%
Latency0ms0ms0ms0ms
LLM calls / query0000
Omnix
Pandas Agent
Text-to-SQL
RAG + Rerank
100%67%80%60%EntertainmentIMDB Movies100%80%87%27%ScienceNASA Exoplanets93%53%87%47%FinanceSEC Filings87%60%87%53%HealthcareFDA Regulations

Same LLM (Gemini 2.5 Flash), best of 5 runs. Ground truth verified by SPARQL execution against Neptune.

Start building today

The core engine is Apache 2.0 — deploy it yourself or let us run it for you. Enterprise features like auto-enrichment, managed hosting, and SLAs available on request.

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