Easy AI for automatic machine learning predictions
Catalytic can consolidate customer records and eliminate duplicates by applying automated machine learning and fuzzy matching. It can decide what information to move, keep or merge, generating accurate data.
Catalytic Predict can make data-driven decisions when it comes to approvals, categorization, forecasting or risk detection. It can also automatically ask a person to handle exceptions or accept the suggestions.
Through data calculation and intelligent prediction, our machine learning algorithm will improve over time—just like your processes—introducing new levels of innovation and speed so you can gain time back in your day.
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With Catalytic Predict, anyone can embed machine learning into automated business processes in less than 10 minutes, giving you the ability to customize everything from data systems to prediction inputs. See how it works in the simple steps below.
Business processes automation with Catalytic can synchronize and validate data from multiple systems. Use data collected through Catalytic’s automated processes, upload spreadsheets or integrate data from your existing systems using pre-built connectors.
Example: Invoice data
Catalytic can receive multiple invoices through email, automatically gather the relevant information from each document and organize it into a data table.
Train your machine learning model to make predictions for target fields of your choice. Catalytic Predict automatically selects the appropriate classification or model to train based on your selected data sets, each rooted in a logistic regression machine learning algorithm.
Example: Invoice approval
You tell Catalytic to predict whether it should approve or deny an invoice based on the set of rules you choose. Then, you tell it what actions you want it to take after the decision is made, whether it’s processing an approved payment, or automatically reaching out to a vendor for a new invoice after a denial.
Catalytic generates predictions based on relevant factors, including both structured data and unstructured text using Natural Language Processing (NLP). The algorithm automatically determines and applies weight to the terms that are most predictive of a given category.
Example: Invoice criteria
To make a decision, you choose the common criteria on an invoice you want Catalytic to consider. Using language processing and calculations, it identifies patterns indicating what should typically be approved or denied.
Embed predictive models into your automated business processes, ensuring predictions are delivered on-demand. Confidence ratings can be incorporated to determine when a decision can be made automatically, or if it should be routed to a person with a recommendation.
Example: Invoice decision confidence rating
You set a threshold for the confidence rating, like 80%. If Catalytic’s confidence is over that percentage, it will automatically carry out an invoice approval or denial. If it’s below that threshold, the exception will be routed to a person to make the final call.
Catalytic’s predictive model trains itself as it collects data, increasing prediction accuracy and responding to changing business conditions in real time.
Example: An increasingly automated invoice process
Over time, Catalytic will compile more data to base its decisions on, resulting in more consistent high-confidence ratings. This enables it to increasingly handle more of the invoice process on its own and automatically take the next steps with less employee input, but the same amount of accuracy.
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