Your Data is Everywhere. Make it work from where it lives.


Zetaris is the Open Agentic Lakehouse that federates,  governs and activates your enterprise data across every source. 

Your AI and analytics teams get what they need with Query-In-Place technology. Your sensitive data stays exactly where it should.

  Query at the Source
 
  Enterprise-grade governance
 
  Private AI ready
 

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See Zetaris in 30 Minutes - Query data, anywhere. No migration. No egress.
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Trusted by enterprise data teams globally

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Zetaris is the Open Agentic Lakehouse - The Control Plane for enterprise AI.

Traditional platforms were built for BI, not AI . They move and duplicate data, slowing performance and inflating cost. Zetaris flips the script with fully goverened, query-in-place federation 

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The problem every enterprise data team knows

You have more data than ever. And less ability to use it than you should.

The bigger the organisation, the messier the data landscape — and the more time your best people spend wrangling infrastructure instead of answering the questions that actually matter to the business.
Data Silos"Our data lives in 40 different places and nobody can see all of it."S3, Snowflake, Oracle, Databricks, on-prem Hadoop, three different SaaS tools — each one an island. A unified view means a 6-month pipeline project.
Migration Cost & Risk"Every centralisation project costs a fortune and takes as long as planned."Moving petabytes of sensitive data is expensive, risky, and by the time it's done, something has changed.
Governance Gaps"We can't enforce data policy consistently across every source."GDPR, HIPAA, SOC 2 — you have policies, but applying them uniformly is a manual, error-prone process.
AI Readiness Blocked"We can't give the model access to our most sensitive data."Your most valuable data — patient records, financial transactions, IP — can't be sent to a third-party API. So your AI runs on a shadow of the real picture.
Egress Costs"We didn't realise how much we'd pay just to move our own data around."Every query that crosses a cloud boundary racks up egress charges. Multi-cloud and hybrid workloads compound this fast.
Slow Time to Insight"By the time the report is ready, the decision has already been made."Batch pipelines, stale caches, and manual wrangling mean your teams are always working with yesterday's data.
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Tell us your current stack in the demo form — we'll tailor the session to your environment.
Platform capabilities

One platform. Every data source.

Zetaris connects, queries, and governs all your data in place — so your AI and analytics teams can move fast without the risk of centralising sensitive data.

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Federated by Design.

Query data where it lives. No duplication. No egress fees.

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Multi-Engine Smart Routing.

Spark, Trino, Presto, DuckDB – the right engine for every job, automatically.

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AI-Native Architecture.

Built for AI-speed from the ground up, not retrofitted BI.

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Open & Sovereign.

Deploy anywhere. Stay compliant. Keep control.

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Real-Time, Every Time.

Batch and streaming combined for sub-second AI responses.

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Secure by Default.

Enterprise-grade governance, encryption, and role-based controls ensure your data stays safe, compliant, and audit-ready at every step
The Zetaris difference

The legacy model moves data to where the analytics are. Zetaris moves the analytics to where the data lives.

No data duplication means no synchronisation debt, no egress cost, no governance lag, and no waiting. Your teams query what they need — in real time. Your sensitive data never leaves the environment it was born in.
Old way With Zetaris
Move data to a central lake before you can query it ✓Query data in place — no movement, no waiting
6-month pipeline projects before AI can run ✓Connect sources in days, run AI immediately
One governance policy per system, applied manually ✓Unified governance layer across every source
Egress fees every time data crosses cloud boundaries ✓Zero egress — data stays where it lives
Sensitive data out of bounds for AI ✓Private AI runs on your data, inside your environment
Engineers building pipelines instead of solving problems ✓Self-serve data discovery for engineers and analysts
Book My 30 Minute Demo →
Tell us your current stack in the demo form — we'll tailor the session to your environment.

Data everywhere.
Risk nowhere.

Legacy platforms force you to copy, centralise, and move sensitive data — creating risk, cost, and latency. Zetaris flips the model. Your data stays where it lives. Your AI and analytics come to it

  • Replace end-of-life legacy platforms without a rip-and-replace migration
  • Meet compliance, privacy, and sovereignty requirements — data never leaves its origin
  • Reduce data infrastructure costs by eliminating unnecessary copies and egress
  • Accelerate time-to-AI with data that's clean, governed, and ready for your models
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AI that actually knows your data

Most enterprise AI stalls because the model can't see the data. Zetaris fixes that — without the risk.

The problem isn't the model. It's the data behind it. Your most valuable data is the stuff you can't send to a third-party API. Zetaris runs your AI inside your environment, against your live data, with full governance.
Natural Language to SQLAnalysts ask in plain English; Zetaris queries the data across every connected source and returns results in seconds.
Agentic Data WorkflowsAgentFlow builds autonomous workflows that query, reason, and act inside your governance boundary — without moving a byte.
Book My 30 Minute Demo →
Tell us your current stack in the demo form — we'll tailor the session to your environment.
One MCP. Every source.

Give your AI coding tools a single connection to every data source in your stack — one query engine, one governance layer, zero data movement.

Claude, Cursor, GitHub Copilot, and every MCP-compatible agent can now query across your entire data estate in a single call. Zetaris acts as the super MCP — federating across Oracle, Snowflake, S3, Salesforce, Databricks and more, with your governance rules enforced, without moving a byte.
Works with every MCP-compatible AI tool
ClaudeCursorChatGPTGitHub CopilotVS CodeReplitSourcegraphWindsurf+ more
Single-platform MCP (Databricks, Snowflake, Salesforce…)
✕Agent needs a separate connection per platform
✕No joins across platforms — results merged manually
✕Governance limited to that vendor's environment
✕Data must move to answer a cross-platform question
✕Each new AI tool needs a new custom integration
✕No audit trail for agent queries
✦ Zetaris — The Super MCP  
✓One connection. Every source. Cross-source joins included.
✓Federated SQL across Oracle, Snowflake, S3, Salesforce in one query.
✓Unified policy layer — RLS, column masking, audit — across all sources.
✓Zero data movement. Computation pushed to the source by design.
✓Write once. Every MCP-compatible agent uses it immediately.
✓Every tool call logged: user, query, sources touched, rows returned.
Book a Demo →
We'll connect to a data source you own and run a live cross-source federated query in the session.
Industry Solutions

Built for where enterprise data gets complex.

From financial crime compliance to media analytics,  Zetaris delivers unified data across any environment, structured, unstructured and streaming. Query in place, orchestrate engines and deliver faster results for Private A.I. without duplication or migration.

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Financial Crime & Compliance.

Unify transaction data, customer records, and risk signals across disparate systems without moving sensitive financial data. Real-time AML and fraud detection powered by federated AI.

circle  Media & Entertainment

 

Audience Intelligence at Scale.

Query subscriber, advertising, and content performance data across broadcast, streaming, and digital — in real time, from a single semantic layer. No data warehouse required.

circle  Gaming & Hospitality

 

Responsible Gaming Analytics.

Monitor patron behaviour, loyalty data, and operational metrics across venues and digital platforms. Meet regulatory requirements without duplicating sensitive personal data.

circle  Health care & Life Sciences

 

Quality Patient Care.

Optimize patient outcomes with precision medicine, improved patient care and efficient admin processes

circle  Retail

 

Lifetime Customer Value.

Elevate your retail business with personalized customer experiences, dynamic pricing, and optimized inventory management

circle  Telco

 

Service Quality Improvements

Transform your telecommunications business with advanced network optimization, personalized offerings, and proactive customer service
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Modern
Lakehouse for AI

One single converged platform for all your AI and Data

Learn the Modern Lakehouse for AI
How it works

A data management platform built on your existing data infrastructure

Zetaris enables organisations to access, analyse, and activate their data in real time by querying it in place, without data duplication, complex ETL processes, or costly migration. Powered by a metadata-driven architecture, Zetaris gives enterprises a governed, AI-ready view of their data across every system they own, without moving it or compromising on security, cost, or sovereignty

See what our customers say

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The platform provided more consistent data in a more timely and trusted manner, enabling more informed decision-making.

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Data Governance Director
Major Australian Bank
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Zetaris allows us to connect to our disparate and unstructured and structured data platforms across hospitals, to expedite triage and discovery processes in patient care

Major Hospital Group
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We launched Zetaris, which is a hybrid platform, designed to reduce the friction and cost of securely sharing data across the organization, only a year later, we are already helping customers like The Queensland Government, Victor Chang Cardiac Research Institute and Charles Sturt University realize the value of their data to optimize their operations CEO

National Telecommunications Company
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We were able to simply connect and join the data. For financial reporting and operational dashboards, this meant speed and accuracy

Finance Company
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Zetaris allows us to integrate, manage and access all our data from a single platform

National Water Utility
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Qantas needed to understand its non-loyalty customers as well as it does its frequent flyer customers.  Its ability to do this was hampered by the information being scattered across the organization. Zetaris tapped structured information (such as customer booking records and demographic data) and unstructured information (such as email and social media) to develop an integrated view of the non-loyalty customer. The initial analysis was completed within 6 weeks and has supported a range of initiatives across the full customer life cycle

Qantas
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Westpac chose Zetaris to develop a customer insights and data analytics offering that helps its merchants make more informed business decisions about their product, marketing, customer relationships and operations. Westpac believes in working collaboratively with its merchants and wanted to offer them sophisticated, curated analytics rather than just the broad brush-stroke data reporting offered by many of its competitors. Zetaris was considered theperfect fit for Westpac because of our highly scalable and lean approach coupled with our advanced analytics and data virtualisation capability

Westpac
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A Certified and Award Winning Company

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Making life easier for Customers and Partners like

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Time to make a switch

Already on Databricks or Snowflake?
Here's what changes.

Egress fees, vendor lock-in, and centralisation costs are built into the model you're on. Zetaris isn't a replacement — it's the federation layer that sits across your existing stack and eliminates what's draining you.

Old way Databricks
Egress costs
Every query that touches data outside Databricks racks up egress charges. Multi-cloud or hybrid workloads compound this fast.
Vendor lock-in
Delta Lake, Unity Catalog, and MLflow create deep dependency. Migrating away means rewriting pipelines, retraining teams, and renegotiating contracts.
Data centralisation
Everything must land in the Lakehouse first. Sensitive data from regulated sources gets copied, increasing your compliance surface area.
AI readiness
AI workloads require data to be ingested and transformed before models can access it — adding latency and pipeline complexity.
Zetaris way Zetaris + Databricks
Egress costs
Zetaris federates queries in place. Data stays at source — no cross-cloud movement, no egress bill. Keep Databricks for what it does best.
Vendor lock-in
Zetaris sits across your stack as an open semantic layer. Your Databricks investment is protected — and you're free to add or swap sources at any time.
Data centralisation
Sensitive data stays at origin. Zetaris queries it in place under your existing governance rules — no copies, no expanded compliance risk.
AI readiness
AI agents and models query federated data directly via Zetaris. No pre-ingestion required — your models get live, governed data instantly.
Book My 30 Minute Demo →
Tell us your current stack in the demo form — we'll tailor the session to your environment.

Ready to Get Started?

Take the first steps toward AI efficiency. See the Metadata Lakehouse live with your own data challenges on the table. 

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FAQ's: Everything you've probably already thought to ask.

Does Zetaris actually not move any data?
Correct — zero data movement. Zetaris pushes query computation to the source via its native query API. Only the result set travels. Query federation, not replication.
Which data sources does Zetaris connect to?
Zetaris connects to any source with a query API — including Snowflake, Databricks, BigQuery, Redshift, S3, Oracle, SQL Server, PostgreSQL, MySQL, SAP, Salesforce, REST APIs, and more. New connectors are added continuously.
How does Zetaris handle data sovereignty and residency?
Because data never moves, it never crosses a boundary it shouldn't. Zetaris queries data in place, so your Australian data stays in Australia, your EU data stays in the EU — automatically, without configuration.
What does implementation look like?
Most organisations connect their first data source within a day. A full production deployment — with governance policies and AI activated — typically takes 2–6 weeks, not months. Zetaris provides a dedicated implementation team.
How does Zetaris fit with Databricks, Snowflake, dbt?
Zetaris sits as a federation and governance layer across your existing stack — it doesn't replace Databricks or Snowflake, it federates across them. Your existing pipelines and investments are preserved; Zetaris adds the cross-source query layer, unified governance, and AI activation on top.
We already use Databricks and Snowflake. They both have AI integrations now — why do we need Zetaris?

Zetaris is the only layer that federates across your entire data estate in a single call, with one consistent governance policy applied across all of it. Databricks and Snowflake each have MCP connectors — but they only connect an AI agent to that one platform. If a business question spans Salesforce, an on-prem Oracle warehouse, and S3, the agent needs three separate connections and still can't join across them. 

Can AI agents actually query our data without it leaving our environment?

Yes — and this is the core of how Zetaris works. When an AI agent sends a query through Zetaris, the computation is pushed to your data source. Only the result travels back. Your data never moves to a third-party server, never crosses a boundary it shouldn't, and never touches the AI model's infrastructure directly.

How does governance work when an AI agent is making queries?

The same way it works for a human. Every agent query runs under the authenticated user's identity and permissions. If a user can't see a table, neither can their agent. Row-level and column-level security policies you've defined in Zetaris apply automatically — there's no bypass path through the AI layer. Every tool call is also written to a structured audit log: who queried, what they asked, which sources were touched, how many rows came back.

What's MCP and do we need to understand it to use this?

MCP — the Model Context Protocol — is the standard that lets AI tools like Claude, GitHub Copilot, and Cursor talk to external data systems. Anthropic open-sourced it in 2024 and it's now supported by every major AI coding platform. For your team, it means AI assistants your developers already use can query your data directly — without custom integration work. You don't need to understand MCP to benefit from it; your developers install one package and it works.

Is the MCP read-only, or can agents make changes to our data?

The Zetaris MCP server is read-only. Agents can query, explore, and reason about your data — they cannot insert, update, or delete anything. This is an explicit design decision for enterprise environments where audit and control are non-negotiable.

What does the audit trail look like?

Every agent query is logged in structured JSON: timestamp, user identity, the exact query or natural language request, which sources were touched, row count returned, latency, and any errors. Log destination is configurable — file, stdout, or an external sink you already use. This is available from day one, not a later add-on.