How We Built Our Own Custom Data Privacy Chatbot for Privacy Policy Compliance and Security

In an era where data is currency, understanding how personal information is collected, stored, and shared is more important than ever.

Yet, for both organizations and individuals, navigating the complex web of privacy policies remains a daunting task. This growing demand for transparency and compliance, driven by global regulations like GDPR and CCPA, has sparked the need for smarter, more accessible privacy tools.

That’s exactly what led us to build our own data privacy chatbot: a scalable, AI-powered assistant designed to simplify privacy insights for everyone.

All you have to do is:

1. Select an AI provider – Choose whose legal documents you want to explore.

2. Ask your question – The chatbot searches stored policies. For ex: “Where’s my data stored?”

3. Browse results – Understand your rights and data usage. No more endless scrolling: just clear, concise legal insights!

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The Challenges of Understanding Privacy Policies

Understanding privacy policies is increasingly vital in today’s data-driven world. However, several key challenges stand in the way:

• Information Overload: Users often interact with dozens of digital platforms, each with its own dense, legalistic privacy policy, many stretching beyond 10,000 words. This makes it nearly impossible to digest and understand the implications of their data practices.
• Constantly Changing Regulations: Privacy laws like the GDPR and CCPA are continually evolving. Staying up to date with compliance obligations across multiple providers is a complex and time-consuming task.
• Lack of Comparison Tools: Users frequently need to compare how different providers handle data, but few tools offer an efficient way to do this. Without side-by-side comparisons, making informed decisions becomes challenging.

When you're dealing with more than fifty providers, these issues are amplified, making it clear that traditional methods of managing privacy information are no longer sustainable.

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RAG to the rescue

Retrieval Augmented Generation (RAG) technology can address these challenges by:

• Contextual Understanding: Unlike simple keyword searches, RAG understands the semantic meaning behind questions, delivering relevant information even when the user's terminology differs from the legal documents.
• Cross-Document Analysis: The ability to query across multiple provider policies simultaneously enables direct comparisons that would be practically impossible manually.
• Transparency and Trust: By providing direct references to source materials, our privacy policy chatbot builds trust with users by enabling them to verify information independently

Moreover, from a customer's security perspective, PrivacyBot offers:

• Due Diligence Support: Organizations can more thoroughly evaluate third-party services before integrating them into their tech stack.
• Risk Assessment: Security teams can quickly identify potential data handling concerns across multiple providers without specialized legal expertise.
• Compliance Documentation: The referenced responses can serve as documentation for compliance audits, showing that privacy considerations were actively researched.
• Informed Consent: End users can make truly informed decisions about which services to use based on how their personal data will be handled. The PrivacyBot represents more than just a convenient tool, it's a bridge between complex legal documentation and practical decision-making for both organizations and individuals, ultimately promoting greater transparency in the digital ecosystem.

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How Was the PrivacyBot Built?

PrivacyBot functions as an intelligent agent powered by a Retrieval-Augmented Generation (RAG) system that stores and retrieves privacy policies from various service providers.

Users can ask questions like “Where is my data stored?” or “Do any of these providers store personal data?” based on a list of supported providers.

The bot then searches through the stored documents and generates a clear, contextual response using the retrieved information.

This significantly reduces the chances of hallucination. If the bot can't find relevant data on a specific topic, it won't guess, instead, it will respond with something like: “Sorry, I couldn’t find any information in the provided documents.”

For a deeper dive into how RAG works, check out our full 2025 Guide to Retrieval-Augmented Generation (RAG) on the Eden AI blog.

In the following sections, we’ll walk through the development process of our PrivacyBot in more detail.

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2. Development process

This project can be seen as a Data project. These are the overall steps:

1. Understand the business questions
2. Identify data sources
3. Process data sources
4. Index data
5. Create and configure the bot

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2.1. Understand the business questions

The first and most important step of the project is asking the right questions.

These are the questions that aim to solve a real business problem. In this case, the main objective of the project is to have a system that can index privacy policies information from different providers and that can be queried using semantic queries.

The system has to be easy to use, and users should be able to select the providers they want the bot to question. The image below shows a basic mock-up of the interface. The idea is just to have a simple interface with the list of providers and at the right a chat-like interface to ask the bot.

One important aspect of this project is that the bot’s answers must include references to the source documents from which the information was retrieved.

For example, if a user asks about OpenAI’s privacy policy, the bot should not only provide a relevant answer but also cite the specific document sections (or chunks) and include the source URL (OpenAI’s, in this case).

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2.2. Identify data sources

Once the business question and overall goal are clear, the next step is to gather the relevant data sources. In this case, that means listing the URLs of the privacy policies for each of our providers. Our own privacy policy is also included in the dataset.

2.3. Process data sources

To process the data, we'll use our RAG system. The first step is to create a RAG project:

Next, you can configure the RAG project. We're using a custom settings approach so we can fine-tune some of the parameters:

Among the configurable parameters, you can choose the vector database to use, the embedding provider for your project, the default LLM model for the bot, chunk size, chunk separators, as well as OCR and TTS providers.

For our project, we use a chunk size of 1,200 tokens. This helps preserve the contextual integrity of each document section, which is crucial for generating accurate and relevant responses. Selecting the appropriate chunk size is essential to ensure the quality of the answers, especially in relation to the original business question.

Once the project is set up, we can begin uploading and indexing the data.

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2.4. Index data

Now, we can start adding documents to our RAG system. To do this, we use the API endpoint. Below is an example in python.

import json
import requests

headers = {"Authorization": "Bearer 🔑 Your_API_Key"}
url = "<https://api.edenai.run/v2/aiproducts/askyoda/v2/{project_id}/add_url>"

payload = {
    "urls": [

        "<https://www.affinda.com/privacy-and-data-protection-policy>",

        "<https://www.ai21.com/privacy-policy>",

        "<https://aleph-alpha.com/data-privacy>",

    ],
    # Optional
    "metadata": [{
        "provider": "affinda",
        "subfeatures": [
            "invoice_parser",
            "resume_parser",
            "receipt_parser",
            "identity_parser",
            "financial_parser"
        ],
        "privacy_url": "<https://www.affinda.com/privacy-and-data-protection-policy>"
    },
    {
        "provider": "ai21labs",
        "subfeatures": [
            "generation",
            "summarize",
            "embeddings",
            "spell_check"
        ],
        "privacy_url": "<https://www.ai21.com/privacy-policy>"
    },
    {
        "provider": "alephalpha",
        "subfeatures": [
            "summarize",
            "embeddings",
            "question_answer"
        ],
        "privacy_url": "<https://aleph-alpha.com/data-privacy>"
    }]
}
response = requests.post(url, json=payload, headers=headers)
print(response.status_code)

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Under the hood, the RAG system uses a scraper to visit each website, retrieve the HTML content, clean it, extract text chunks, generate embeddings from those chunks, and finally store them in the vector database.

The data cleaning process removes unnecessary elements such as styles and scripts from the HTML. In our case, we only need the actual page content, so stripping out extra elements streamlines the data and reduces the cost of the embedding process.

Once the embeddings are created, they are stored in the vector database. Along with each embedding, we attach metadata, additional information that enriches the embeddings and helps the bot deliver more accurate and context-aware responses during later stages.

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2.5. Create and configure the bot

Now that our documents are indexed in the database, we can create a bot capable of answering questions based on that content.

The description defined in the bot’s profile serves as the system prompt during conversations, guiding the bot’s tone, behavior, and scope of responses.

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Once the bot profile has been created, everything is set up and ready for asking questions. To test the bot, we can make a request to its endpoint. For example, using cURL:

curl --location '<https://api.edenai.run/v2/aiproducts/askyoda/v2/d20417f4-526c-45c3-b08d-19645d6f529c/ask_llm_project>' \\
--header 'Content-Type: application/json' \\
--header 'Authorization: Bearer 🔑 Your_API_Key' \\
--data '{
    "query":"Do these providers store my personal data during training? Explain for each selected provider.",
    "llm_model":"gpt-4o",
    "k": 10,
    "max_tokens": 1000,
    "filter_documents": {
        "provider": ["openai", "xai"]
    }
}'

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Now, your project is ready to be used, either by calling the endpoint directly, integrating it into an Eden AI workflow, or embedding it in a separate web application.

In our case, we built a new component within our application that connects to our RAG project, just as we envisioned during the initial planning phase:

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3. Technical and non-technical challenges

Using Eden AI’s RAG framework significantly simplifies the development of these types of projects. It handles complex and time-consuming tasks like web scraping and data cleaning, which are often among the most challenging parts of the pipeline.

One important factor to consider is chunk size. The ideal chunk size can vary depending on the type of documents and the specific goals of the project.

This requires experimentation, testing different sizes and evaluating the quality of the system’s responses to find the right balance between context retention and processing efficiency.

A major technical challenge we encountered was the inconsistent structure of privacy policies across different providers. Some companies present their policies using clear headings and logical sections, while others use less conventional formatting, embed legal references, or combine multiple policies into a single document. This structural variability forced us to implement flexible parsing logic capable of adapting to different document architectures, while still preserving semantic coherence within each chunk.

In several cases, we had to manually review how documents were being processed to ensure that key context wasn’t being fragmented or lost, especially in documents with nested sections or table-based formatting.

Finally, it's also important to experiment with different bot profiles. Fortunately, the Eden AI RAG interface allows you to create multiple profiles (with only one active at a time). This enables A/B testing and the flexibility to update the active profile even after deployment.

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5. Impacts & results

Eden AI users now have access to a conversational bot that provides immediate, contextual answers about privacy policies across multiple providers. This fundamentally transforms how they interact with complex legal documents:

• Time Efficiency: What previously required hours of manual reading and cross-referencing now takes seconds. Internal testing shows users save approximately 85% of the time previously spent researching privacy questions.
• Accessibility: Technical and non-technical users alike can now access and understand privacy information without needing legal expertise, broadening the audience who can engage with this critical information.
• Decision Support: Users report making more informed decisions about which providers to integrate into their workflows based on specific privacy considerations that align with their organizational requirements.

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6. Reflection & Learning

The development and deployment of PrivacyBot provided valuable insights that extend beyond this specific project. These learnings will guide our approach to future RAG implementations and product development.

6.1 Business Alignment First, Technology Second

As noted briefly in the original section, understanding the business question proved to be the most critical foundation of the entire project. We found that:

• Problem Definition Clarity: Spending additional time upfront with stakeholders to precisely define the problem (accessing privacy information across multiple providers) prevented scope creep and maintained focus.
• User Journey Mapping: Walking through the user experience before implementation helped identify key friction points in privacy policy navigation that the RAG system needed to address.
• Success Metrics Definition: Establishing clear metrics for what constitutes a "good answer" guided our technical decisions around embedding models, chunk sizes, and retrieval strategies.The lesson here is clear: RAG is not just a technical solution but a business solution enabled by technology. When the business problem is well-defined, the technical implementation follows more naturally.

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6.2 Technical Optimization Lessons

Several technical insights emerged during implementation:

• Context Window Trade-offs: While larger context windows improved answer coherence, they also increased costs and sometimes introduced irrelevant information. We found an optimal balance through systematic testing.
• Model Selection Impact: Testing different LLM models revealed that while more advanced models produced more nuanced answers, they didn't always justify the increased cost for straightforward questions, leading us to implement a tiered approach.
• Prompt Engineering Iteration: We went through multiple iterations of system prompts for the bot, finding that explicit instructions about citation formats and comparative analysis significantly improved output quality.
• Retrieval Parameter Tuning: The optimal number of chunks to retrieve (k value) varied depending on question complexity, leading us to implement dynamic k-selection based on query characteristics.

6.3 Future Directions

Based on our learnings, several promising directions that can be explored:

• Multi-document reasoning: Enhancing the system to draw connections between related sections across different provider policies.
• Historical tracking: Implementing version control for policies to track how provider stances on privacy evolve over time.
• User-specific contextualization: Adapting responses based on a user's industry, geography, and regulatory requirements.
• Proactive alerts: Notifying users when policy changes might affect their specific use cases.The PrivacyBot project has reinforced that successful RAG implementation requires a holistic approach spanning business understanding, data processing expertise, user experience design, and technical optimization,

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Conclusion

PrivacyBot successfully simplifies the complex task of navigating privacy policies, making critical information more accessible and actionable for both organizations and individuals. By leveraging Retrieval Augmented Generation (RAG), the bot provides fast, contextual answers while fostering trust through transparent, verifiable sources.

While challenges like inconsistent document structures and parameter tuning were addressed, the project demonstrated the power of AI in enhancing time efficiency and accessibility for non-technical users.

Looking ahead, PrivacyBot paves the way for further innovations, including multi-document reasoning and proactive policy change alerts, ensuring a more transparent and informed digital landscape.