In this article we’ll look at how AI can augment your data pipelines and we’ll use a real world example (analyzing support ticket history to gain insights into user behaviour) to illustrate how accessible these new capabilities are and how quickly they can be adapted for entirely new use cases.
Contents
Why AI Needs the Right Data Process Behind It
Data Privacy by Design: Local Anonymization
AI That Responds Reliably — Not Just Creatively
From LLM Output to Actionable Analytics
A Template for Endless AI-Driven Use Cases
The rapid advancement of large language models (LLMs) has created exciting opportunities for organizations to automate, accelerate, and enhance data-driven processes. Yet many companies struggle to operationalize AI in a way that is reliable, repeatable, and safe for enterprise environments.
This is where CloverDX’s newly released AI-powered data transformation features (introduced in version 7.1 of CloverDX) can make a significant difference.
CloverDX has always been a platform for building robust, transparent data workflows. Now, with integrated AI capabilities, it becomes a powerful bridge between unstructured information and structured, actionable insight.
In this article we’ll look at how AI can augment your data pipelines and we’ll use a real-world example (analyzing support ticket history to gain insights into user behavior) to illustrate how accessible these new capabilities are and how quickly they can be adapted for entirely new use cases.
AI needs the right data process behind it
AI is only as good as the prompts and data you give it. When it comes to feeding data to AI, a common challenge when working with LLMs is that:
- They become confused when given too much irrelevant detail, or
- They fail when missing key context needed for a task.
CloverDX solves this by making AI a step within a larger structured pipeline rather than a standalone tool. Therefore, before any data reaches the AI, CloverDX allows you to reshape, clean, enrich, and anonymize it — ensuring that the model receives exactly what it needs and nothing more.
Follow our example
Check our real world example, were we're using CloverDX to prepare a history of chat conversations as data fed into AI (yes, including anonymization!) for gaining statistical insights. In this first part, CloverDX converts raw JSON exports from Zendesk into clean, consistent text by removing noise (timestamps, IDs and metadata) while adding meaningful context for the AI model to work with.
Data privacy by design: Local Anonymization
A standout advantage of CloverDX’s AI features is the ability to maintain full control over sensitive data. Before any content leaves your systems, you can use on-premise small language models to automatically detect and redact personal data such as names, emails, or license numbers.
Anonymize locally before sharing with LLM
In our example we're anonymizing data locally (customer names, phone numbers and email addresses are redacted) and thus we're sending only "safe" text to the external service. This hybrid approach (local anonymization + external LLM processing) offers a safe, enterprise-grade path to operational AI.
AI that responds reliably — not just creatively
In a simplistic AI workflow, you would run a prompt and accept whatever answer comes back. CloverDX’s AI Client goes much further.
In AIClient component, which acts as the interface between your data pipeline and an external LLM of your choice, you can write a simple piece of code that can analyze the model’s response, validate it against your own rules, e.g. expected data structure, and automatically retry the LLM query with corrective guidance if the output is malformed or incomplete.
This capability transforms AI into a powerful and dependable data transformation tool in your repertoire rather than an unpredictable helper.
Validating AI output and retrying prompts
In our example we’re asking AI to classify support tickets in various categories, e.g. what part of our platform the ticket is related to, or to classify whether the problem is a product issue, user error, configuration error, etc. We’re expecting a well-formed JSON structure with strictly predefined elements. Watch how we're validating the LLM's output and retrying the prompt if it's not producing the expected results.
From LLM output to actionable analytics
Once AI-generated response is generated and validated (ie. the response leaves the AIClient component), we’re back to where CloverDX helps the most – orchestrating a data flow of structured data and tasks that need to be carried out on top of it. In CloverDX you can easily convert the response from the LLM into structured rows and columns — just like any other dataset.
Turning AI responses into actionable data
In our example we’re analyzing support tickets and using AI to produce insights such as ticket summaries, identifying products, type of issues and patterns in customer problems. This data can then feed dashboards, training pipelines, quality-assurance processes, or machine-learning systems.
A template for endless AI-Driven use cases
While our example focused on a specific customer support use case, the approach is broadly applicable. Anywhere you have semi-structured or unstructured text, using an LLM to analyze or categorize it is a great time saver. CloverDX is a great orchestration layer that can combine preparation, anonymization, LLM analysis, and structured output into a single, easy to manage job.
Potential use cases include:
- Contract analysis and risk flagging
- Invoice or receipts summarization
- HR ticket categorization
- Marketing content auditing
- Incident-response reporting
- Knowledge-base extraction
- Sales call summarization
The key insight: you don’t need to redesign your workflows to use AI — you simply augment them with CloverDX’s new capabilities.
AI becomes part of the data pipeline
Not an Afterthought CloverDX can easily integrate AI tasks into a standard data-transformation pipeline. You can:
- Pre-process data to give AI exactly what it needs
- Protect privacy before data ever leaves the system
- Ensure high-quality, structured AI output
- Combine AI results with any downstream process
- Or even use your own on-premise model for full control
Explore more ways to use AI in CloverDX
See what else the CloverDX data management platform is capable of.
Conclusion: CloverDX makes enterprise AI practical
CloverDX’s new AI-powered data transformation capabilities remove the complexity and unpredictability often associated with LLMs. By handling preparation, anonymization, prompt engineering, validation, and structured output automatically, CloverDX allows teams to focus on using AI insights rather than wrestling with implementation details.
Watch our full example video:
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