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Getting Started

After connecting the sandbox to your AI tool — Claude, ChatGPT, Cursor, Copilot, or any MCP-compatible agent — start a new conversation and try any prompt below. Your AI will automatically call the right ontology tools. You don’t need to know the API.
The sandbox contains a realistic B2B pipeline for Meridian Technologies — 99 accounts, 244 deals, and 10,000+ connected entities. Everything you see is what Doris builds from your real CRM data, meetings, and conversations.

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Quick Start Prompts

Try these first to see what the ontology can do:

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Workflow 1: Deal Review

Walk through a deal the way a sales manager would before a forecast call. Step 1 — Find the deal:
Step 2 — Understand the stakeholders:
Step 3 — Check commitments:
Step 4 — Review the strategy:
Step 5 — Look at recent meetings:
What you’ll see: A complete deal picture — stakeholders with roles (champion, blocker, economic buyer), overdue commitments creating risk signals, competitive positioning against Gong and Clari, and meeting summaries with specific action items.

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Workflow 2: Meeting Prep

Prepare for an upcoming customer meeting in 60 seconds.
Your AI will pull together:
  • Active deals and their stages
  • Key stakeholders and their concerns
  • Recent meeting history and what was discussed
  • Open commitments (especially overdue ones you need to address)
  • Competitive landscape (CrowdStrike is in the mix)
  • Deal strategy with specific risks and next steps
Follow up with:

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Workflow 3: Pipeline Analysis

Analyze pipeline health across the entire org. Overall health:
Stuck deals:
At-risk deals:
Closed-lost analysis:
Win analysis:

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Workflow 4: Competitive Intelligence

Understand your competitive landscape.

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Workflow 5: Stakeholder Mapping

Map the buying committee across an account.

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Workflow 6: Commitment Tracking

Track follow-ups across the entire pipeline.

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Workflow 7: Cross-Entity Exploration

The real power of the ontology — connecting entities across types.

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Tips for Better Results

Instead of “show me deals,” try “show me Enterprise deals in Negotiation stage with amount over $200K.” The ontology supports filtering by any field.
The best insights come from drilling down. Start broad (“which deals are at risk?”), then go deep (“what specifically is blocking the Ironforge deal?”). Your AI maintains context across the conversation.
When you ask about a deal, your AI can expand related data: stakeholders, commitments, objections, competitors, meetings, strategy, and more. Ask for “full context” to get everything.
“Compare our closed-won deals to our closed-lost deals” or “How does the Apex deal compare to the Cobalt deal in terms of stakeholder coverage?” Comparative analysis reveals patterns.
“What objections keep appearing?” or “Which tactics are most effective?” The ontology tracks patterns across all deals and meetings.

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Entity Types You Can Query

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Ready for Your Own Data?

The sandbox shows what Doris does with synthetic data. Imagine this running on your real CRM, your actual meetings, and your team’s commitments.

Book a Demo

See it live on your pipeline — 30 minutes, no prep needed.