Ever wonder which of your marketing efforts actually drive revenue? Most companies can’t answer this because their customer data lives in silos—marketing contacts in HubSpot, product usage in one system, payments in Stripe. You know the revenue numbers, but you can’t see the complete customer journey. In the next 10 minutes, you’ll experience something remarkable. You’ll ask business questions in plain English and AstroBee won’t just give you numbers. It’ll provide business insights, context, and actionable recommendations you never thought to ask for. You’ll watch AstroBee automatically connect fragmented data sources, then see how you can refine that understanding to unlock insights that span your entire customer journey. By the end, you’ll have transformed disconnected data into a unified business intelligence system using nothing but natural language.
Prerequisites: Before you begin, create an account and sign in.

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Get started

We’ve set up a realistic demo environment that mirrors the data challenges every business faces. You’ll work with three separate datasets that represent a common scenario: marketing contacts living in HubSpot, product usage data in your application, and billing records in Stripe. Right now, these systems can’t talk to each other. This is the problem you’re about to solve. The data is messy and realistic, just like yours: different email formats, inconsistent IDs, and no clear way to connect a marketing lead to their eventual revenue contribution.

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Sign in to AstroBee

Sign in to your AstroBee account at app.astrobee.ai.
AstroBee Sign In Screen

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Select the demo dataset

After signing in, you’ll see the onboarding screen. Click Use Demo Data to load the pre-configured demo dataset. Once we finish the quickstart, you can connect your own data sources. Learn more in Data sources.
Selecting the demo model
Once you select the demo data, AstroBee will show you the raw data sources. The demo represents a SaaS company with the same challenge you face:
  • Marketing contacts in HubSpot CRM (leads, prospects, customers)
  • Product usage data (signups, logins, feature engagement)
  • Billing records in Stripe (subscriptions, revenue, payment status)
Click Continue to let AstroBee automatically suggest an ontology based on the dataset’s structure, a business layer that organizes and connects data into one clear source of truth.

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Preview your ontology

In the ontology preview, you’ll AstroBee has defined 6 entities based on the raw data tables. This is just a starting point. You’re about to refine and enhance this model to unlock powerful cross-system insights.
Ontology preview screen
What’s really powerful is that AstroBee has already analyzed the siloed raw sources, automatically cleaned, mapped, and unified it into a semantic layer you can explore.

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Generate the ontology

Click Generate Ontology to create the initial ontology. The ontology building process takes a few minutes and requires you to keep the browser tab open while it runs. You’ll see real-time progress as AstroBee builds the ontology. Click Refine Ontology to start editing the ontology.
Refine Ontology button

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Explore the interface

You’ll see two main sections in AstroBee: Chat, and Sources. Here’s what each section does:
  • Chat - Where you’ll have conversations with your data using plain English, and toggle to Ontology editing mode to edit your ontology and see how AstroBee understands your business model and data relationships
  • Sources - Your raw data connections and schema explorer
Once you’ve completed onboarding, you’re on the Chat page in Ontology editing mode by default.

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Refine the ontology

Here’s where the magic happens. Right now, your data exists in silos. Your marketing team sees HubSpot contacts, your product team sees usage data, and your finance team sees Stripe payments. But what if you could ask “Which marketing channels drive the highest-value customers?” and get an answer that spans all three systems? That’s exactly what you’re about to enable. Recall the demo represents a SaaS company with the same challenge you face:
  • Marketing contacts in HubSpot CRM (leads, prospects, customers)
  • Product usage data (signups, logins, feature engagement)
  • Billing records in Stripe (subscriptions, revenue, payment status)
The problem: These datasets can’t connect. Email formats don’t match, IDs are inconsistent, and there’s no reliable way to track a customer’s journey from first contact to final payment. The solution: AstroBee’s AI-powered ontology editing. You’ll describe what you want in natural language, and AstroBee will automatically clean, standardize, and connect your data so you can finally see the complete picture. When you bring together data from disparate systems, you need to reconcile the different representations of real world objects across each system if you want to do reliable cross-system analysis and automation. Right now, AstroBee has created a basic ontology. You’re about to transform it into something much more powerful.

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Describe your changes

In the ontology editor, click the Try Sample Question card or type the following request in the chat input: 
Please add normalized properties to each of these datasets (without creating new ones), and link them together, so that I can join them together without losing any data.
Request to create a new entity
AstroBee analyzes your request, understands the relationships between the datasets, and adds the necessary properties to each entity.
AstroBee processing request
AstroBee processing relationships
AstroBee executes each step with real-time updates where you can see the progress of creating the new entity and its relationships.

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Review and save changes

When complete, you’ll see what AstroBee created:
  • Email matching made easy: AstroBee standardized all email formats across systems: lowercase, trimmed, and cleaned up billing suffixes so HubSpot contacts can connect to Stripe customers seamlessly.
  • ID consistency: Added proper prefixes to customer IDs so your product usage data can link directly to billing records without manual matching.
  • Reliable customer journey tracking: Each system now has clean, consistent identifiers that enable you to follow customers from first marketing touch to final payment.
Not only did AstroBee add normalized properties to the entities, but it also established relationships between them, allowing you to join the datasets without losing any data.
Notice: You asked for data connections, and as a bonus, AstroBee also standardized country formats and timezone data across all systems - things you didn’t even think to ask for. This is AI-powered data modeling that anticipates your needs and solves problems before you encounter them.
Click the edited Contacts entity to preview the changes AstroBee made, view the edited tables, and save your model. Click ” Save Model” to make your changes permanent. You’ll see notification toast telling you that the ontology was saved successfully.

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Query across systems

Time to see what was impossible before. Previously, answering “How much revenue comes from each marketing stage?” would require:
  1. Exporting data from HubSpot
  2. Matching emails manually in spreadsheets
  3. Cross-referencing with Stripe payments
  4. Hours of data wrangling and potential errors
Now? You’ll simply ask the question in plain English and get instant insights that span all your systems. This is what makes AstroBee different: you think in business terms, not technical queries.

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Your first cross-system analytics query

Under the chat input, toggle from edit to chat mode. You’re about to ask a question that was impossible before the ontology improvements: a query that spans HubSpot marketing stages and Stripe revenue data.
Ask questions normalized datasets
Select the first sample analytics prompt or type this in the chat input: 
Create a bar chart showing total MRR from stripe per deal stage in hubspot
What happens next is the magic ✨

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Chat response

AstroBee analyzes the data, understands your question, and generates a response that includes not just the answer, but also insights and recommendations based on the data patterns it finds. AstroBee responds:
I’ve created the bar chart showing total Stripe MRR by HubSpot deal stage. Here are the results (all rows returned): customer: 4,640 marketing_qualified: 4,200 lead: 3,940 sales_qualified: 3,890 The data includes active and past-due Stripe accounts, joined to HubSpot contacts by normalized email. Key insights:
  1. Customers generate the highest MRR, which aligns with expectations.
  2. Leads and marketing-qualified contacts collectively contribute nearly the same MRR as existing customers, indicating potential for growth.
  3. Sales-qualified contacts are lagging behind other stages, highlighting a potential area for conversion improvement in the sales process.
You can view the full dataset in the table section, and the bar chart is now displayed in the chart section of your report panel.
This is the “aha” moment: Notice what just happened. You asked one question, but AstroBee gave you:
  • The exact revenue numbers you requested
  • Business insights about pipeline performance
  • Strategic recommendations for improving sales conversion
  • A complete analysis that required data from two completely separate systems
Before the ontology improvements: This question would have been impossible to answer automatically because HubSpot and Stripe data couldn’t connect. After your 5-minute ontology edit: You can now analyze the complete customer journey, from marketing contact to revenue contributor, with a single natural language question. No SQL, no exports, no manual data matching required.

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View the answer report

On the right, you’ll see the answer report with the tabs: Results, Sources & Logic, SQL, and Pipeline.
The Results tab shows the answer in the form of relevant charts and tables. For this question, AstroBee generated a chart showing the number of users per company in descending order.
Results tab showing charts and business insights

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Explore more questions

Now that you’ve seen how to refine the ontology and ask questions, try these additional queries to explore the dataset further: 
Show a bar chart of engagement (total product events in the last 30 days) by customer industry

Create a chart showing total mrr for active customers whose last login is more than 30 days ago, compared to those who logged in less than 30 days ago

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Next steps

Ready to use AstroBee with your own data? Choose your path:

Data sources

Connect to BigQuery, Snowflake, or upload CSVs to query your data.

FAQs

Get answers to common questions about AstroBee features and capabilities
Need help? Check our troubleshooting guide for setup assistance.