Use cases · what teams ship on MarcoPolo today

Eight workflows. One workspace.

Each of the eight below is a workflow a customer or active prospect named themselves on a sales call. We anonymized the company names, kept the workflow shape, and detailed how each one runs on the MarcoPolo workspace.

01Account briefs & QBR prep02Equipment troubleshooting & RCA03Embedded sales-rep assistant04Compliance-as-AI05Self-serve Salesforce admin06Investment research synthesis07Governed data catalog for AI08Cost & agent-sprawl control
Use case · 01CS Ops · B2B SaaS

Account briefs & QBR prep

Ten queries, one brief, before the call starts.

CSMs walk into customer meetings without a complete picture. Pipeline lives in the CRM. Product usage lives in the warehouse. Support history lives in tickets. Conversation context lives in Slack channels. Asking the analyst means waiting two days for a stitched answer.

The workspace fans out across all four systems in a single session · joins on account ID, ranks the signals that matter, and synthesizes a one-page brief per account. The first skill our flagship customer's CS Ops team shipped, and the one that runs the most.

How it works

  1. 01
    Connect the four systems

    CRM · warehouse · ticketing · Slack · through Connections, one CLI surface.

  2. 02
    Build the skill

    10 queries joined on account ID. Skill versioned in the workspace.

  3. 03
    Trigger from any AI surface

    Slack DM, custom internal app, or directly from Claude.

  4. 04
    Brief lands in seconds

    The CSM walks in informed. Four people stop asking the same question.

"The MCP layer is what's making the data actionable."

CRMWarehouseTicketsSlackFor RevOps →
Use case · 02Data Engineering · Semiconductor

Equipment troubleshooting & RCA

Pattern-match against 100 documented incidents in seconds.

Manufacturing teams sit on terabytes of telemetry, 100+ documented root-cause analyses, and an on-call rotation that diagnoses by hand. Senior engineers carry the institutional memory of which signature means which fix. The system can't · until you give the workspace access to the telemetry, the incident database, and the procedure library together.

The workspace cross-references real-time sensor data against prior incident signatures, ranks likely causes by historical match strength, and pulls the documented procedure with the parts already checked against inventory. What a senior engineer would do in twenty minutes, the junior on-call gets in twenty seconds.

How it works

  1. 01
    Connect every signal source

    Time-series databases, log stores, incident ticketing, procedure libraries.

  2. 02
    Workspace seeds the context

    Schema and semantics initialized per source. No cold starts.

  3. 03
    Engineer describes the symptom

    From any AI surface, in plain English.

  4. 04
    Ranked candidates + procedure

    Top causes by historical match, with the documented fix and inventory check.

"100 to 200 root cause analysis processes. Right now it's taking a lot of human effort."

Time-seriesLogsIncident DBProceduresFor Data Eng →
Use case · 03Product · SaaS vendor

Embedded sales-rep assistant

Natural-language CRM actions, inside the vendor's product.

SaaS vendors increasingly want AI inside their own product · the sales rep types "update this lead, log a note, query similar opportunities" and the workflow happens in flow, not in three browser tabs. Without a workspace layer, the vendor builds and maintains the CRM integration for every customer, every release, every quarter.

With MarcoPolo embedded, the vendor ships the AI capability once. Each customer brings their own CRM credentials through the vendor's UI. The workspace routes the natural-language action through the right adapter, executes against the customer's system, and returns the result. The vendor's product gets smarter without their engineering team building integration plumbing per customer.

How it works

  1. 01
    Vendor embeds the workspace

    MarcoPolo as the AI infrastructure layer inside the vendor's product.

  2. 02
    Customer connects their CRM

    OAuth from inside the vendor's UI. Credentials never leave the customer's environment.

  3. 03
    End user types an action

    "Update lead X, create a note, find similar opps." In plain English.

  4. 04
    Workspace routes, executes, returns

    Auditable round-trip to the customer's CRM. Reversible. Logged.

"Update a lead, create a note, delete an opportunity · natural language."

CRMVendor productEmbedded MCPFor Product →
Use case · 04Compliance · Retail · Finance

Compliance-as-AI

AI as the enforcement layer for policy and regulation.

Compliance teams in retail, banking, and regulated finance spend their week screening uploaded artifacts against shifting policies, internal standards, and regulatory requirements. Most of the work is pattern matching that's exactly what AI is good at · but only if the policies are queryable and the audit trail is rigorous.

The workspace turns the policy library itself into a structured corpus. Every upload is screened automatically. Every flag is auditable. Every decision is reversible. The human reviewer stops doing the first pass and focuses on the edge cases. For PII-sensitive deployments, the workspace integrates directly with Skyflow's vault · sensitive data is redacted before it ever reaches the model context.

How it works

  1. 01
    Encode the policy library

    Internal standards, regulations, contract obligations · as a queryable corpus.

  2. 02
    Connect the upload pipeline

    S3, GDrive, internal review systems · through Connections.

  3. 03
    AI screens every upload

    Against the policy. Skyflow vault redacts PII before model contact.

  4. 04
    Flagged items surface

    To a human reviewer with the matched rule, audit trail attached.

"Make sure whatever gets uploaded is compliant with policies, standards, laws."

Policy libraryUpload pipelineSkyflow vaultSIEMFor Security →
04 → 05 · halfway through

The same workspace. Eight very different teams.

Use case · 05RevOps · B2B SaaS

Self-serve Salesforce admin

Flow debugging, dedupe, metadata changes · without the admin.

RevOps teams are perpetually back-logged on Salesforce admin requests. A broken flow, fields out of sync, test data needed for a new campaign, dedupe across a freshly-imported list. Every request becomes a Jira ticket, and every Jira ticket waits behind ten others. The result: sales operations runs at human speed when the rest of the business runs at machine speed.

The workspace connects to both the Salesforce sandbox and production with scoped credentials. Permissioned skills · flow debugging, metadata changes, test data generation, dedupe · let any team member run admin-grade operations from natural language. The change is applied, the audit log is written, and the reversibility window is documented.

How it works

  1. 01
    Connect Salesforce

    Sandbox first, then production. Scoped credentials per skill.

  2. 02
    Permissioned skills

    Flow debug, metadata change, dedupe, test data. Each has its own scope.

  3. 03
    Request through any AI surface

    Claude, Slack, custom internal app. Audit logged.

  4. 04
    Change applied + reversible

    Full lineage written to SIEM. Rollback window configured.

"Build Salesforce automations without having to wait for me to do it."

SalesforceWorkspaceSIEM auditFor RevOps →
Use case · 06Investment Ops · Asset Management

Investment research synthesis

Raw data to refined brief, before the IC meeting.

Investment teams brief on portfolio companies, market segments, and deal flow · pulling from PitchBook, Carta, email archives, internal IC notes, and a warehouse mirror. The data is there. The synthesis is the bottleneck. Analysts spend half a day stitching sources together before the brief can even start being written.

The workspace joins every source on the company identifier, refreshes on freshness triggers, and produces a brief with source-by-source provenance. The analyst reviews and adds judgment. The IC reviews insight, not raw data. Briefs ship the same day they're requested.

How it works

  1. 01
    Connect every source

    PitchBook, Carta, email archive, IC notes, Snowflake mirror.

  2. 02
    Workspace seeds semantic context

    Per source. Each system's "what does this field mean" is encoded once.

  3. 03
    Analyst asks in plain English

    "What's new on company X this week? Compare to peers." From any AI surface.

  4. 04
    Brief with provenance

    Source-by-source. Freshness triggers fire when underlying data updates.

"Expedite the time from raw data to refined information."

PitchBookCartaEmailIC notesWarehouseFor AI Leader →
Use case · 07Data Engineering · Life Sciences

Governed data catalog for AI

Hundreds of scientists. One BigQuery. The right context, scoped.

Large science orgs have hundreds of practitioners hitting a centralized warehouse · but the schema is opaque, the semantic meaning of fields is tribal, and the RBAC is patchwork. Every new analyst becomes a six-month onboarding project. Every quarter, the data team builds the same five queries for five different teams who don't know the others already wrote them.

The workspace becomes the catalog layer: every scientist sees the data they're scoped for, with the context they need, governed by their identity from the IdP. Successful queries get logged and curated into the context layer · the catalog grows with every question instead of bit-rotting between Confluence pages.

How it works

  1. 01
    Connect the warehouse

    BigQuery, Snowflake, Databricks · all three if you straddle them.

  2. 02
    Seed semantic context

    From the catalog. Per-team definitions of what fields mean.

  3. 03
    RBAC inherits from IdP

    Scientist's scope cascades. Workspace returns only what they're cleared for.

  4. 04
    Catalog grows with use

    Successful queries logged. Reused. The institutional memory accumulates.

"Our science org is about 160 people. They use BigQuery a lot. They need the context."

BigQuerySemantic catalogIdPFor Data Eng →
Use case · 08Security · Enterprise IT

Cost & agent-sprawl control

One layer of governance for every AI surface.

Every team is plugging AI into something. Claude here. Copilot there. A custom agent in engineering. Three RevOps GPTs. Security can't see the credentials, Finance can't see the spend, IT can't see the data access, and the AI Council is asking for the dashboard.

The workspace becomes the single layer where every AI surface inherits the same policy plane: the same scopes, the same audit pipeline, the same cost attribution. Per-tool-call SIEM events stream to your security stack. Per-user cost attribution rolls into Cost Plane. The AI Council gets the report. The CFO gets the attribution. The CISO signs off.

How it works

  1. 01
    Deploy the workspace

    In your VPC. SSO + SCIM cascade from your IdP.

  2. 02
    Every AI surface routes through

    Claude, ChatGPT, Cursor, Copilot, custom agents · all inherit the policy.

  3. 03
    Per-tool-call SIEM events

    Stream to Splunk, Datadog, or your-own-SIEM.

  4. 04
    Cost attribution + budgets

    By user, team, workflow. Anomalies flagged. Budgets enforced.

"Everybody is building agents left and right. It's going out of control."

All AI surfacesSIEMCost PlaneIdPFor Security →
The common thread

Eight workflows. One workspace.

Every one of these starts the same way · connect the source systems, seed the context, run the query through your preferred AI. The workspace is the layer underneath, doing the joining, the governance, the audit, and the cost attribution. The use cases multiply; the architecture stays the same.

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