All of your most important questions answered!
How long can I use the free trial and what features can I access during this time?
The free trial lasts for 2 weeks after sign up. You will have access to all “business” features and usage limits during these 2 weeks, to understand what these are, please view the pricing page.
Can I get a demo of the AI & Analytics Engine?
Yes, you can. Make a booking here, or watch online here.
What happens when I hit my usage limits?
You will be notified and unable to go beyond that particular usage limit. You will be prompted to advance to the next plan tier to continue being able to use that function. If you choose to continue with your current plan, some usage limits reset at the commencement of the next billing period. Refer to our pricing page for usage details.
What is the payment process?
No credit card is required to get started with your free trial.
When upgrading to a paid plan, you will be prompted to enter in your credit card details. Upon confirmation, your first payment will occur, and your 1-month billing cycle will commence. Payment takes place before the start of the month, and the price will be automatically charged to your saved payment method.
AI sounds complex - are there any tips to get me started with the AI & Analytics Engine?
We have created a repository of useful tutorials and documentation called the Knowledge Hub. If you have a specific question you can send through a request form and our helpful team will get back to you with some guidance.
How do I cancel my plan?
As an organization owner, you can terminate your plan by going to Organization > Billing > Request Termination. Our support team will get in touch with you over email to assist you with the process & confirm the account deletion and or/ plan termination.
Do note that once your plan is terminated, your organization will cease to exist and any organizational data will be deleted from our servers. However, your AI & Analytics account will still exist, and you will still be able to login to the Engine and participate in any other Organization that you are a member of.
How do I know what plan is right for me?
One of our Team Members will be happy to help. Get in touch via the contact us form and one of our friendly Team Members will reach out to assist you.
I have a use case in mind - but I am not sure if the AI & Analytics Engine is the right solution?
We can assist in validating the solution given your needs and objectives. Get in touch via the contact us form and detail out your use case and objectives and we will be in touch to provide guidance.
I don’t have a use case in mind - but I am interested in understanding if the AI & Analytics Engine could create a competitive advantage?
There are many use cases that the Engine can be applied to, take a look at our repository of use cases to understand some of the applications of AI across industry.
PI.EXCHANGE also offers Advanced AI Services in addition to the Engine. Here we can assist with the discovery, design, implementation, and optimization of AI projects. Our approach is to provide specialized guidance and resourcing, get in touch via the contact us form if you would like to hear more about our Advanced AI Services offer.
What are templates?
Templates are the Engine’s business-user-friendly approach to build a ML solution with no code. Instead of the user figuring out how to prepare and transform their data (manually building a data-wrangling recipe), and picking the ML problem type (supervised vs. unsupervised, binary vs. multi-class etc.), the template automatically builds a ready-to-use end-to-end ML solution, taking care of technical data science and ML considerations like recipe and problem type under the hood. The inputs required by a template involve only the business context, and other information based on the user’s domain knowledge and business understanding.
What is “build from scratch”?
The “build from scratch” option is available to users in case none of the templates meet their requirement/problem type. It allows users to manually decide the steps in their ML pipeline, such as:
Preparing datasets as per their requirement and re-use the recipe
Choosing among standard ML problem types such as classification, regression, and clustering
Obtaining insights and predictions from their model in a flexible manner