If you work with data infrastructure, you’ve probably noticed that qualitative customer data — interview transcripts, open feedback, research notes — usually sits outside your domain.
It’s unstructured, inconsistent, and hard to query.
But what if that kind of data could be made process-ready and useful for business functions like marketing, product, or sales?
That’s exactly what we’ve been experimenting with:
storing structured qualitative interview data in Neo4j, then using the Neo4j AI Agent to generate recognizable frameworks automatically — such as a Value Proposition Canvas, Empathy Map, or Customer Journey Map — directly from the graph.
Activities that usually take days, weeks or sometimes months can now be done in a few seconds, with utmost precision.
The Challenge: Making Human Language Computable
Qualitative data has a simple problem: it’s rich in meaning but poor in structure.
In our setup, we used Customer Progress Design (CPD), a formalized ontology for customer research data. It defines 12 element types (like Job-to-be-done, Pain, Gain, Desired Outcome, Constraint, Habit, etc.) and a controlled set of relationships between them.
After structuring just nine in-depth interviews, we ended up with:
- 485 nodes (atomic insight elements)
- 695 relationships
- 20 defined relation types
Once this data is in Neo4j, everything becomes queryable:
we can traverse motivations, constraints, and behaviors as connected entities rather than paragraphs of text.
The Experiment: Using the Neo4j AI Agent
Neo4j’s new AI Agent makes this data directly usable.
Because the agent can “see” the graph context and use natural language, we can now prompt it to build output structures that are familiar to business users.
For example, asking:
“Create a Value Proposition Canvas from the interview data.”
The agent queries the graph for nodes labeled Customer Job, Pain, and Gain, organizes them by relationship, and renders a coherent output.
The same works for Empathy Maps, Persona Cards, or Journey Maps — frameworks that most non-technical stakeholders already understand.
Why This Matters for Neo4j developers or managers
This use case shows how graph databases can bridge qualitative and quantitative worlds.
As a developer or data manager, you can now:
- Demonstrate how unstructured insights can become structured graph data.
- Use Neo4j schema and relationships to enforce semantic consistency across interviews.
- Enable AI agents to produce interpretable outputs without manually coding template logic.
- Offer stakeholders a familiar representation (e.g., Value Map) while keeping all traceability in the database.
It’s a practical way to introduce graph-based thinking into teams that deal with human insights but lack the technical means to process them.
Next Steps
We’re extending this approach to:
- Auto-generate behavioral segments directly from the graph.
- Combine qualitative insight graphs with quantitative sources (CRM, surveys, NPS).
- Use Neo4j’s agent to power “explainable AI” reasoning over real customer evidence.
Our goal isn’t to automate empathy — it’s to make it computable and reusable.
If you’re managing a Neo4j instance in your organization, this might be a good entry point to bring qualitative data into your graph ecosystem.
It’s an untapped data source that can make your graph useful to entirely new audiences.