Building AI applications while keeping your data secure

tl;dr

Leveraging AI like ChatGPT can supercharge business workflows. However, most users don’t understand the issues around data privacy, especially submitting first-party data to these systems.

We provide a technical solution to this problem, allowing AI to be trained on your private data while ensuring the data is secure and private to your teams.

AI's is a transformative power, but at what cost?

Finding someone who’s not used ChatGPT to write emails or documents would be hard... While this tech is fairly safe if you generate a public piece of content like a tweet, it’s a different story if you summarize a private sales call or pitch.

When you submit a query to an AI platform, your data can be used to train it. The data can be surfaced to other users and potentially other AI platforms.

It only takes 2000 lines of code to run a GPT instance, but billions of data points - it’s the data that powers the technology and if not treated properly submitted data will be used to train the AI and can be surfaced up to the billions of users.

Taking a responsible approach:

The balance between innovation and security is tough; at rehab, our core AI ethics are clear,

To this end, we have been deploying applications using a range of technology solutions, including the recent launch of Google’s Vertex AI solution. The solution runs on Google Cloud’s infrastructure and is designed to keep private company data secure while enabling us to develop bespoke AI applications and tools for our clients.

How it works:

Google’s powerful PaLM2 LLM (Which Powers the chatbot Bard) is on par and in some cases more cognitive than Open AI’s GPT4. We use this technology stack to build a lot of the creative applications that power our client’s internal workflows.

There are other private LLM solutions available, they are cost-prohibitive and difficult to manage, the Vertex AI platform gets around that by allowing us to leverage the billions of data points in the LLM while allowing a secure sandbox of private data to sit on top of it, allowing the data to be queried in new ways.

Use Cases

AI unlocks the power of your data, from pattern analysis to prediction and generative AI. Some of the most powerful outputs are created using data from your organization. Never before have you been able to leverage it in these ways.

First Party Data

1. Personalized Marketing: Companies can leverage first-party data such as user preferences, purchase history, and browsing patterns to train AI models to generate personalized marketing messages, improving customer engagement and conversion rates.

2. Product Recommendations: Retailers can use AI to analyze customer data and generate personalized product recommendations, enhancing the shopping experience and boosting sales.

User Interview or Panel Data

1. Product Development: Companies can analyze user interview or panel data to uncover user needs and preferences, using these insights to guide product development and improve existing offerings.

2. Customer Sentiment Analysis: Using AI, companies can analyze qualitative interview data to gauge customer sentiment about products, services, or brand image.

Consumer Research Data

1. Market Trends: By training AI models on consumer research data, companies can uncover emerging market trends and patterns, informing strategic decision-making.

2. Competitive Analysis: AI can assist in processing extensive consumer research data to provide insights into competitors’ strategies, strengths, and weaknesses.

Sales Data

1. Sales Forecasting: AI can use sales data to generate accurate sales forecasts, helping companies to plan for the future and optimize inventory management.

2. Performance Analysis: Companies can train AI models on sales data to identify high-performing products, regions, or salespersons, helping to inform business strategy and incentivize performance.

A more technical deep-dive:

To ensure the safe use of the PaLM2 model, the Virtual Private Cloud controls access using Google Cloud IAM user management and IP address restrictions.

Vertex allows us to run a private instance of the PaLM2 model, meaning the data used to train this model is not accessible by any other users. This technical architecture ensures a secure and private environment for data handling and training AI models, answering our clients' valid concerns about data privacy.

Here is a breakdown of this secure infrastructure:

For clients unable to leverage Google Cloud, there are similar solutions for Microsoft and Amazon; reach out to our team to find out more:

How might you use it?

Our team is available to walk you through some of the most powerful private LLM use cases that we’ve created using customer data.

Find out more at Rehab Agency and subscribe for more insights from our team as we discover them.