Our team has 15+ years of data science and AI experience working with companies like Meta and Larry H. Miller. We have access to nationwide market survey and demographic data, with hundreds of standardized algorithms in our library. (That's what we use to find quality insights!) We are educators in the AI space and have millions of records of metrics and performance data for lead generation and online sales.

Perhaps the biggest differentiator, however, is our proprietary AI engine which is connected to major generative platforms, like OpenAI and Google's Gemini. Having our own engine allows us to develop overlay models that extend the capabilities of other specialized AI platforms.

Large data sets have immense value because they provide sufficient points of correlation to produce trustworthy insights, which are the actual realized value of doing data science. However, most SMBs don't have sufficient data to produce them. A work around is to pool multiple data sets from different sources into a larger set from which insights can be produced. You can choose to retain your data with no additional benefit beyond the status quo or you can tap into the powerful insights that data science offers by pooling your data with Pria.ai.

We offer data science processing starting at $0 because of your data's value toward the creation of insights. Some insights are given to you for free, which is an added bonus for sharing your data, but to be fully transparent, the most powerful are offered at an additional purchase price. It's a performance-based model that gives your company the opportunity to participate in the benefits of doing data science at a much lower cost. The alternative is spending thousands of dollars to purchase all the data internally to generate the insights you're after. We think you'll agree that our model is a win for everyone, especially SMBs.

Insights are the "golden nuggets" that result from doing quality data science. Here are a few examples:

• A lead list of potential customers with a high probability of purchasing your product based on previous sales data.

• The identification of an event that causes something to happen with relative certainty, allowing you to forecast and craft business decisions around the event reoccurrence.

• A list of customers that are likely to stop using your service, so you can proactively approach them before they drop.

• Evaluating the success rates of different marketing campaigns to maximize your return on investment.

• Analyzing competitor pricing and current consumer demand to optimize pricing strategies.

• Using sensor data from machinery to predict failures before they occur to maximize efficiency.

• Identifying emerging trends in social media chats and forums that can shape product development.

• Predicting future demand for products, improving supply chain management.

And the list goes on!

Having a sizable and diverse dataset is crucial for accurate data analysis and meaningful insights. The more data we have to process, the more reliable the results. This is also why we prefer to integrate public sources with your data before processing, because it increases the data set size and gives us higher confidence in the results. Generally, we recommend a minimum of 1000 customer records for participation with Pria.ai.

Data science insights often depend on specific correlations and the goals of the organization. For example, consider a car dealership with the contact information of buyers as well as details of their trade-in. We would start by creating the profile of an ideal trade-in customer for a specific model using multiple points of correlation. Running it against a new data set of people who currently own similar trade-in vehicles is likely to return a high-quality lead list. In this scenario, your data is a good fit for the intended goal.

Now, let's take away the details of the trade-in from the data set. It is now impossible to identify potential clients with that data point, which is crucial to narrowing down the number of potential matches. The resulting lead list would be overly large and not very accurate. Even though multiple points of correlation exist, the key point for the intended goal is missing. A substantial amount of labor, time and money would be wasted pursuing that list, so the data is not a good fit in this scenario.

No. The insights we provide use "whitewashed" data, which means it has been obscured to omit identifying details.

If we find a potential collaboration between your product and another vendor we will recommend and broker a cross-promotion campaign. Our platform provides an obfuscation layer for communication so that you can connect with potential clients from other vendors without their personal information being revealed directly.

The timeline for seeing results from data science can vary depending on the complexity and scope of your goals.

Generally, you can expect to see initial insights within a few weeks of working with your data. For comprehensive projects that require extensive data engineering and model training, the timeline may extend to a couple of months, especially if we are integrating additional data from public and premium sources

Data science costs vary depending on how you choose to incorporate it into your organization. It typically ranges from $50 to $300 per hour industry wide, but we offer a wide variety of options ranging from $0 - $150 per hour, based on your goals. Our unique model brings the benefits of data science, typically reserved for big business budgets, and makes them accessible to SMBs through alternative methods.