Governed AI infrastructure, deployed in your cloud.

Unblock teams today. Prove your ROI tomorrow.
Own your context forever.

MarcoPolo scales enterprise AI durably, lowering token costs, grounding answers in your actual data, and keeping credentials inside your perimeter.

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.

Customer voice

Built with teams already scaling AI 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.

Jessica Mercedes

Director of CS Ops

DuploCloud · live in production

Trusted byDuploCloudSybillFresh KDSFrore SystemsSkyflowTheom+ growing
Connections

Connect to everything you already use.

One CLI, 50+ source systems. The agent loads one tool. The workspace fans out.

AI Surfaces
Claude
ChatGPT
Cursor
Copilot
CRM & Sales
Salesforce
HubSpot
Pipedrive
Outreach
Warehouses
Snowflake
Redshift
BigQuery
Databricks
Databases
PostgreSQL
InfluxDB
Prometheus
MongoDB
Operations
Gong
Chorus
Marketo
Mailchimp
The problem

Enterprise AI promises compounding ROI.
Most deliver a compounding bill.

Agentic AI rollouts today share three leaks: token waste, wrong answers, and data exposure.

Naive MCP burns tokens

Tool definitions fill the context window

  • 134K tokens consumed before a single question is asked;

  • Schema discovery repeats on every query;

  • Up to 98.7% of tokens go to overhead, not the answer;

  • Cost scales before the first business result;

Wrong answers prompt retries

Hallucination increases cost

  • Users tolerate errors by reprompting;

  • Each retry is a new token charge;

  • Accuracy problems hide inside cost overruns;

  • The CFO only sees a growing bill;

Data exposure scales with reach

Credentials leak into context

  • Every per-user MCP adds one more credential boundary;

  • Each connection is a new egress path to audit;

  • Blast radius grows faster than seats added;

  • Security scanning can’t keep pace with MCP sprawl;

The solution

Own the context. Rent the model.

Frontier models will keep getting cheaper, faster, more capable. The model you bet on today is not the model you’ll use in 2027. Build your enterprise context as a portable layer, and every model swap becomes a configuration change.

Your context

The workspace is yours.

Repo-shaped, deployed in your cloud, readable and exportable. It accumulates value with use, sits on your books, and leaves with you intact.
The commodity

The model is swappable.

Claude today. ChatGPT tomorrow. What ships next quarter, after that. The workspace works the same way the next day, with no re-grounding.
Your IP

Your context is the IP.

Encoded how your business actually works · your fields, your processes, your semantics. Unique to you. Impossible for a model provider to commoditize.

No vendor lock-in · git-like portable workspace · egress you control

How it works

MCP is the connection. MarcoPolo is the workspace.

A place where AI and humans do the work together. One sandbox per user or agent, hosted in your cloud, usable from any AI surface, with credentials that never leave your environment.

Secure K8s container · Scoped credentials · SIEM audit
How we do it

A governed workspace that connects
AI to enterprise systems and data.

Three products. One platform. Each solves a specific operational pain, in a specific order · today, tomorrow, ultimately. All three run on the same governed workspace.

Foundation · Integrations
Connections
One CLI verb across 50+ enterprise systems. The agent loads one tool, not fifty. Less surface to audit, less data in context, lower token spend. Build once · every AI surface inherits it.
Learn more about Connections →
Tomorrow · Optimize
Cost Plane
See what AI costs. And what it returns. Spend attributed across retries, context, and routing · so you see which workflows pay off and which fail. The knobs to drive cost down, and a defensible path to ROI.
Learn more about Cost Plane →
Underlying platformContext layer · your IPContext · the assetSecurity · 5 control pointsObservability · audit + SIEM
In action

Two outcomes for the same question.

“Which deals slipped last quarter and what engineering issues were blocking them?”

Native MCP hands the agent a connector.

AI needs a place to work.

Before any work Tool definitions fill the context window.
Prompt“Which deals slipped last quarter and what engineering issues were blocking them?”
Step
01
Salesforce MCP returns 18 slipped deals into the context window.+25K tokensschema + records
Step
02
Jira MCP returns 64 engineering tickets into the context window.+30K tokensschema + tickets
Step
03
Now join them. Where, exactly? The model string-matches account names against customer fields.+10K tokensmodel reasoning

No insights · just a partial list, and a retry. Every record now sits in the model’s context.

MarcoPolo gives the agent a workspace.

Schema and semantics already loaded.

On connect Schema and semantics already loaded.
Prompt“Which deals slipped last quarter and what engineering issues were blocking them?”
Step
01
connection query salesforce · 18 slipped deals.+0.9K tokensquery result
Step
02
connection query jira · 64 P1/P2 tickets, 14 deals.+1.1K tokensquery result
Step
03
Join in DuckDB · materialized on account.+1.0K tokensjoin output
Answer73% of slipped deals had open engineering blockers older than 30 days · concentrated in API integration, SSO, and performance at scale.

Raw data stays in the workspace. Credentials never in context. Every call logged to your SIEM. The next user inherits the join.

The workspace where the leaks stop.

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.