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Pi Predict: Causal AI transforming embedded analytics for SaaS

Pi Predict enables SaaS organizations to quickly and securely build Machine Learning (ML) use cases to generate rich insights for end users that lie beneath your application's data.

Delivering the power of foresight into your SaaS application

In evolving sectors that include high levels of uncertainty, having an in-depth and accurate view of the past is integral to building a forward-looking perspective. Ignite your roadmap with predictive analytics built for the future.

What is Causal AI?

Causal AI is an emergent form of AI that delivers richer insight than previous versions of Machine Learning. Traditional ML models approach analysis through correlating datasets, whilst Causal AI mirrors the reasoning approach taken by humans, identifying the root cause of events. Humans use reasoning to understand the relationships between events to draw a conclusion, and Causal AI uses algorithms and models to identify causal relationships in data. Causal AI complements human involvement and allows domain expertise to be incorporated into the models.
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The cutting-edge of explainable AI

Our Causal AI module, Pi Predict takes effect in a structural causal model depicted as a decision tree to visually represent the relationship between variables. This visual approach to representing data is far more transparent by clearly surfacing the context behind the data, demonstrating how the conclusion was reached. This user-friendly approach empowers all stakeholders to make highly informed decisions with confidence and promotes buy-in from C-suite execs.

Using Causal AI in your SaaS platform gives your users the answers they have been seeking, in the most accessible way.
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  • 1.
    Stay ahead of the curve
    Causal AI is the inevitable next step in predictive analytics, and SaaS vendors late to adoption could see themselves being left behind. Businesses are seeking more informed insight from their data; intending to not just understand why outcomes occurred but also forecast future events.
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  • 2.
    Investigate ‘what if scenarios’
    As Causal AI has the potential to uncover the root cause of events and the ripple effect caused by certain variables, it means it can be applied to future scenarios. For example, users could undertake scenario models such as “What would happen to X if we made this change to Y?” to best prepare for the future. Essentially, users can use hindsight to apply foresight.
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  • 3.
    Withstand unpredictable events
    Correlation-based machine learning performs really well when operations run in a similar format over time as it makes predictions based on the patterns it has seen before, but this format struggles when there is a major disruption. Causal AI helps to navigate these changing environments by applying historical data and outcomes to possible scenarios; helping users to make effective decisions for current or future circumstances.
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  • 4.
    Inclusive decision making
    Utilizing Causal AI in your analytics solution opens valuable insight up to all users. Traditionally, the level of depth provided by Causal AI would have only been accessible to trained data scientists, but incorporating a user-friendly interface into your SaaS application lowers the barrier to entry and enables more inclusive decision-making.
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Make predictive analytics, proactive

As businesses become more dependent on data-driven decisions, the need for robust insight is amplified. Pi Predict can embed the future of predictive analytics into your SaaS application.
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Pi Predict delivers foresight

Through the utilization of Causal AI, Pi Predict delivers the optimal combination of data-driven analysis and domain expertise. It uncovers the context behind the data, for users to action change.
Seriously secure
The analysis takes place in your application to fit in line with your data security model.
White-labelled
Seamlessly integrate the next generation of predictive analytics into your application.
Rapidly deployed
As a priority in the product roadmap, you can embed predictive analytics in days, not months.
User-friendly navigation
Pi Predict cuts through the complexity of data to deliver valuable insight to any user.

A solution for all SaaS sectors

Fintech
Healthtech
Retailtech
Edtech
Fintech
Pi Predict can be applied across all sub-sectors of the Fintech market. It could be used to analyze historical data on customer spending or insurance claims to understand key variables impacting outcomes. This unique insight could then be applied to current or possible circumstances to assist in future lending.
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Healthtech
By embedding our predictive analytics solution, your users can utilize the unique insight over historical hospital operations, remote care, or patient outcomes to build a comprehensive understanding of past events. This hindsight, supported by the knowledge of the key variable impacting outcomes, can develop proactive care models.
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Retailtech
With Pi Predict, your SaaS application can uncover the causality of spikes in consumer spending. By understanding the key factor/s impacting peak spending, such as top-selling products, a promotional offer, or a loyalty scheme, retailers can look to replicate their success.
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Edtech
Applying Causal AI models enables Edtech users to understand which characteristics have the greatest impact on student performance, and, on a more granular level, discover which may have a positive impact on one group of students but have a negative impact on another. The causality approach over correlation allows educators to understand the impact of change and take effective action to improve educational outcomes.
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Fintech

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In the ever-evolving landscape of Artificial Intelligence, staying ahead of the curve is crucial. This blog delves into the 4 AI trends set to redefine how organizations approach and utilize AI. Below, we navigate through AI Simulation, Federated Machine Learning, Neuro-Symbolic AI, and Causal AI.
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Pi Predict FAQ

Questions about Pi Predict? Find the answers here.
Pi Predict delivers the next generation of predictive analytics. Utilizing Causal AI rather than traditional ML models that are correlation-focused, Pi Predict uncovers the causal relationship between variables to detect those that have the greatest impact on the outcome. By discovering the ‘why’ behind an outcome, users have much deeper insight into past events, and can also apply this knowledge to the future.

This unique distinction from other forms of analytics can elevate your SaaS platform to act as a differentiator to competitors.
After the pilot, you can implement our solution in a matter of days. Our low-code solution eliminates the need for a long development process so that you can offer predictive analytics to your users in no time.
Causal AI is an emerging form of machine learning that strives to go beyond traditional ML models. Traditional models can identify the extent to which multiple events are related using the data inputted, whereas Causal AI goes beyond this to identify the root cause of events, providing a much deeper understanding of the data. This gives Causal AI a unique edge over traditional ML, as it’s integral for users to understand why a particular event has occurred, in order to make effective change. Beyond this, it has improved transparency and observability over previous models due to it taking a user-friendly decision-tree model.
Panintelligence originated from the highly regulated finance sector and with that follow stringent data security practices. Unlike other platforms, we embed into your application and query data at the source, so no data leaves your infrastructure. Therefore, Pi Predict can align with your data security model.
Causal AI can be the step forward to de-biasing algorithms. Causal AI can envision futures that are decoupled from historical data, enabling users to eliminate biases in input data.
Traditional ML induces a limited view, narrow explainability, and high susceptibility to bias. The links and intermediaries between variables in previous ML models are often ‘black boxed’ meaning users and even the programmers cannot easily see how the variables relate to the outcome. However, Causal AI and the intrinsic nature of cause and effect means it can address many of these concerns.

Make predictive analytics, proactive.

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