How to Build an MVP for Ticket Analytics with AI and n8n

A lot of automation ideas get stuck because a big process grows around them too quickly — discussions, a development backlog, and multiple teams getting pulled in.

This is a case of how a domain specialist can build a working MVP on modern tools on their own: quickly test a hypothesis, get a result, and only then decide whether it’s worth turning into a full product.

This is not a production-ready recipe. It’s a fast way to test an idea without a long approval cycle or involving half the company from day one.

With that said, this approach quickly raises serious questions: security, access controls, data-handling rules, reliability, model output quality, and edge-case handling.

Starting Point: Limited Time and a Strong Urge to Make It Happen

The task was clear, but there was no desire to turn it into a full-scale development project. The goal was to do as much as possible independently, ideally in a no-code or low-code setup.

The task looked like this: once a support ticket is closed, the system should pull the conversation data, send it to AI for analysis, and return a summary of communication quality.

The Basic Flow

The logic was simple:

• When a ticket is closed, the CRM sends an event to an external workflow.
• The workflow receives a JSON payload with ticket data and the conversation.
• From that payload, only the data needed for analysis is kept.
• The cleaned text is sent to an LLM.
• The model returns a structured review.
• The result is either sent to notifications or stored for further analytics.

Step 1: Realize That You Need an Intermediate Layer

Because the task depended on an event, a JSON payload from the CRM, data processing, and a model call, n8n was chosen as the central layer.

Step 2: Set Up a Self-Hosted n8n

Why a self-hosted n8n? If the idea worked, the next step would almost certainly involve storing results, connecting a database, adding analytics tools, and expanding the scenario.

For this, an n8n VPS was used — a server running Alpine Linux 3 with n8n preinstalled.

Step 3: Get the CRM to Send Data on Ticket Closure

A Webhook Trigger was created in n8n. The CRM started sending a POST request to it with a JSON payload containing the ticket data and the conversation.

On the backend side, there were only minimal changes: when the “ticket closed” event fires, send the data to the required URL. In some places, no-code was enough; in others, a small code change was needed.

Step 4: Extract Only Valuable Data from the JSON

The raw JSON obviously contained a lot of extra data: technical fields, service metadata, and secondary context. For AI, it was much more useful to receive normalized conversation text in a simple format such as “name: message.”

To avoid manually parsing the structure, the webhook JSON was sent to Claude with a request to extract only the required parts. It suggested adding a Code node in n8n using JavaScript and provided a ready-to-use code snippet for the transformation.

After that, instead of a raw CRM object, the workflow had a clean input for analysis.

Step 5: Connect ChatGPT and Make It Act as Head of Support

ChatGPT was added to the workflow through the API. That required an API key and a prompt with several tasks. 

For the MVP, basic criteria for evaluating communication were enough: tone of voice, clarity of the response, estimated customer satisfaction, quality of ticket resolution, and improvement comments.

At that point, n8n was sending the conversation text to the model, which returned a clean, structured review.

Step 6: Realize That Sending Everything to Slack Is Not Okay

At first, the idea was just to dump the results into Slack. But it quickly became clear that this was not enough if the goal was not only to read individual cases, but also to analyze the overall flow.

The solution was to store the results in a database and use Metabase on top for visualization and analytics.

Step 7: Launch a Database and Start Saving Results

A database was deployed next to n8n. For this MVP, MongoDB was chosen as a convenient document store for analysis results.

The next step was to connect MongoDB to n8n and decide what exactly should be written to the database.

Claude helped here again: based on screenshots and a description of the current flow, it suggested adding another transformation step before saving. The model response should not just be stored as plain text — it should be split into fields so it can be used later.

Step 8: Connect Metabase and Figure Out What to Do with the Data

Once the database was saving results, a tool was needed to show them not as raw documents, but as analytics.

That’s why Metabase was added alongside it.

Its role in this setup was to connect to the database, read the data, allow queries and dashboards, and turn scattered analysis results into something useful for review.

This is another important part of the MVP approach: you don’t have to know in advance what the perfect analytics setup should look like. First, you can build a rough scenario, test it on a few cases, and only then refine the metrics, logic, and reporting format.

Step 9: Run the Full Scenario End-To-End

In the final test, the whole chain worked end to end:

1. A ticket is closed in the CRM.
2. The CRM sends a webhook.
3. n8n receives the JSON.
4. The messages are extracted and cleaned.
5. ChatGPT performs the analysis.
6. The response is transformed into a structured result.
7. The data is saved to MongoDB.
8. Metabase gets material for further analytics.

What Turned Out to Matter Most in Practice

The main practical takeaway was this: don’t try to build the perfect system right away. It’s much more useful to assemble a minimally working scenario quickly, test it on real data, and then make it more complex.

The second takeaway: don’t try to cover everything at once. An MVP works better when solving one clear problem.

The third point: modern AI tools in this kind of process do more than analyze data — they also speed up the development of the solution itself. That means that today, a domain specialist can often go from idea to first result on their own, without a long chain of participants.

Final Result

The result was a working MVP. After a ticket is closed, the conversation is sent for analysis, the model returns a structured review, and the results can be used as a foundation for analytics.

More importantly, the entire chain was built quickly, without a heavy process and without involving multiple departments from the start. First comes the idea, a fast launch, and hypothesis testing. Only after that come refinement, scaling, and involving other teams — if that is actually necessary.