The data gateway for AI agents.
Build once, connect your agents to any data or system. Governed access and shared context, across any model.
Integration · Governance · Context
Trusted for production agents at
Integration. Governance. Context.
Agents demo well, then break in production. Because your data stack was built for humans, not agents.
Every integration is yet another project
MCP standardized the protocol, not the work. Each one is still a connection to maintain, a security review to clear, and access to grant and revoke. Forever.
Solved by Secure connections →Agents break the access model
RBAC assumes a person in a fixed role. Agents work flexibly across systems, so teams over-permission and lose the audit trail.
Solved by Governed workspace →Every agent relearns your business
How your business works and where your data lives gets fed over with every prompt, by every team. Nothing compounds, and answers drift.
Solved by Own your context →Any AI, every system.
One MCP connection · Every path trusted · Every action audited
Govern
Ground
Secure connections
50+ systems behind one MCP server. Scoped credentials that never touch model context, set up once and inherited by every AI surface.
Explore Secure connections →Governed workspace
AI works on data in code, so the model reasons over results, not raw records. Sensitive values tokenized, every action logged.
Explore Governed workspace →Own your context
LLM-maintained knowledge of your data that compounds with every task, so two people asking about the same customer get answers that agree.
Explore Own your context →Your rules. Real outcomes.
Agents trigger queries across your systems as they work. Set the rules once at the foundation, and every step is scoped, run in code, and logged.
Claudegoverned by MarcoPoloWhich renewals are at risk this quarter, and why?
governed · completeScoped access granted · credentials never enter model context
Renewal rules, churn signals & working joins loaded
Joined 3 systems · sensitive values tokenized
11 tool calls · 0 raw records · fully logged
3 renewals at risk: declining usage + unresolved P1s. Full brief with per-account evidence, ready for review.
And what teams run through it every day
For every team taking AI into production.
One server carries
every system.
Every system behind one MCP server, inherited through your app.
For developers →AI leadersOne integration,
one review.
Clear the integration backlog from one place, access managed centrally.
For ai leaders →Security & CISOProduction without
the risk.
Tokenized values, scoped credentials, every action logged to your SIEM.
For security & ciso →OperationsAI that knows
your stack.
A living map of how your systems work · clean up, then automate.
For operations →Data teamsThe context layer,
past the warehouse.
Catalogs and semantics kept current by an agent, not a backlog.
For data teams →Built with teams scaling AI. From first agent to enterprise scale.
New CSMs don't have to ask anyone. The workspace already knows.
Director of CS Ops · DuploCloud · Live in production
“The MarcoPolo MCP layer is what's making the data actionable. CSMs with a new account don't have to ask anyone · the workspace already knows.”
The controls your CISO will ask for.
Everything your security team needs to sign off, documented and ready for review.
SOC 2
Type I complete · Type II in progress (Q3 2026). ISO 27001 underway. HIPAA BAA on request.
Your cloud, your data plane
VPC, dedicated peer, on-premise, or air-gap. We operate the software; the data never leaves.
Audit your SOC can read
Every tool call as a structured SIEM event · user, tool, system, tokens, credential scope.
No-train, by contract
Default and contractual. Scoped credentials never enter model context. GDPR & CCPA by architecture.
CISO one-pager · controls matrix · sample SIEM events · Trust & security →
Don't just take our word for it. Ask your favorite AI.
Let ChatGPT, Claude, Gemini or Perplexity help. Click a button and see what your favorite AI says about MarcoPolo.
Questions about the data gateway.
What is a data gateway?
A data gateway is one secure connection point between AI agents and enterprise systems. It does for agents what the warehouse did for BI · access, governance, and context · but federated across the live systems that own the truth, instead of one central copy. Agents connect once; the gateway enforces who can see what, works on data before it enters model context, and maintains the knowledge agents need to answer correctly.
How is this different from an MCP gateway?
An MCP gateway proxies and governs tool servers · access control for tools. A data gateway governs the data itself: a governed workspace where agents work on data in code before anything reaches the model, and Own your context that captures schema, semantics, and rules so answers agree. Access is the start; correct, governed answers are the product.
Do I have to move or copy my data?
No. Access is federated: the gateway reaches into the systems where authoritative data already lives · CRM, ERP, databases, warehouses, SaaS · with scoped credentials. The warehouse stays an important source; it becomes one connection among many.
Does the model ever see raw records or credentials?
No. Credentials are scoped per identity and never enter model context. Data work happens in the governed workspace · DuckDB, Python, shell · and the model reasons over results. Sensitive values are tokenized with meaning intact, and every action streams to your SIEM.
Which AI surfaces does it work with?
Any MCP-compatible surface: Claude, ChatGPT, Cursor, Copilot, and the agents your teams build with frameworks like LangGraph or CrewAI. One gateway backs them all; switching models doesn't lose your context.
Enable agents to work with your enterprise data.
Start with a discovery call this week. From there, your security team reviews the architecture, and you're running a production pilot in your cloud within six weeks.
