How Aparavi and Langflow Created a Financial Services Chatbot Without Data Risks
Good data makes trustworthy AI. This blog shows how Aparavi turns messy data into AI-ready assets through a real case study.
The Problem: Michelle’s AI Chatbot Project
Michelle, a product manager at a financial company, needed to build an AI chatbot but faced big challenges. Her data was scattered across 12+ systems with hidden sensitive information in thousands of documents. She was spending weeks on manual sorting with tight deadlines looming and had no way to ensure safe data for AI training. And there is a list of works she needs to be done within a week.
How Aparavi Helped
Aparavi helped Michelle connect to all data sources in minutes instead of weeks. The platform automatically found and protected sensitive content, cleaned data specifically for AI use, and delivered ready-to-use, safe data that met compliance requirements.
Aparavi’s Demo Pipeline
| Node | Description |
|---|---|
| Data Source – AWS S3 | Connects to Amazon S3 buckets to retrieve stored files and documents |
| Data Source – Drag and Drop | Allows manual file uploads through a simple drag and drop interface |
| Data Source – Microsoft SharePoint | Connects to SharePoint repositories to access corporate documents and files |
| Data Source—Google Drive | Integrates with Google Drive to retrieve cloud-stored documents |
| Data Source – Microsoft OneDrive | Connects to OneDrive personal storage to access files and documents |
| Data Source – Azure Blob Storage | Integrates with Azure cloud storage for scalable file access |
| Data Parser | Extracts and separates content into different formats (text, tables, images, etc.) |
| Text Classification—US Data | Specialized classifier that identifies and tags US-specific regulatory data |
| Text Classification—Australia Data | Specialized classifier that identifies and tags Australia-specific regulatory data and policies |
| Data Classification – Compliance | Identifies documents containing compliance-related information for US regulations |
| Text Anonymizer | Identifies and masks personally identifiable information (PII) to ensure privacy compliance |
| Preprocessor – Chonkie | Advanced text chunking with multiple strategies, including neural and semantic chunking for optimal document segmentation |
| Preprocessor – LLM | Prepares text specifically for large language model consumption with appropriate formatting |
| LLM – OpenAI | Integrates OpenAI’s language models for advanced text processing and understanding |
| Http Results | Outputs processed data via HTTP endpoints for integration with other systems |
| Embedding—OpenAI | Leverages OpenAI’s embedding models to convert text into high-quality vector representations |
| Vector Store – Qdrant (2) | Dual Qdrant vector database instances for redundant storage and retrieval of both the vector embeddings |
Chatbot Creation with Langflow
After preparing the data with Aparavi, Michelle used Langflow to create her AI chatbot. Langflow’s visual interface allowed her to design conversation flows without coding. She connected the clean, anonymized data from Aparavi directly to her chatbot’s knowledge base, creating a secure AI assistant that could answer customer questions without exposing sensitive information.
No-Code Simplicity
Michelle loved how easy it was to use Aparavi, just connect blocks like Lego pieces in a simple flow: Ingest → Scan → Classify → Export. This approach required no coding skills and maintained version control that allowed for tracking changes when needed.
Key Results
- 90% reduction in data preparation time
- Zero data governance risks in the final AI application
- Complete visibility into what data is being used where
- Scalable solution that grows with expanding data needs
- Successfully classified and anonymized personal information
- Seamless integration with the Langflow platform
Why This Matters
- AI is only as good as its data—garbage in, garbage out
- Clean data prevents AI hallucinations and inaccuracies
- Proper data governance protects against compliance violations
- Time saved in data preparation accelerates AI deployment
- Using Aparavi allows teams to focus on business outcomes rather than data cleaning
If you’re struggling with messy data for AI like Michelle was, start for free with Aparavi. Clean, trusted data is the foundation of successful AI projects and can make the difference between a frustrating failure and a celebrated success.
