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Quickly train a machine learning model to predict and categorize any data from a spreadsheet or database. No code required.

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Quickly train a sentiment analysis model


If you can put it in a spreadsheet, we can predict it.

Think of Quicknet as an add on to any tabular data source. If you can model it in a spreadsheet, and provide a few ground truth examples, we'll quickly train a model based just on your data and give you an easy way to predict future values.

Upload A CSV

Export from your database, google sheets, or excel and upload into Quicknet

Annotate Your Data

Fill in the truth on a few examples. This could be a simple yes/no, or a few different categories.

Train And Use Your Model

Kick of training, and have an easy to use model to predict values based 100% on your data.

AI doesn't need to be so fancy

Use Cases

Simple, practical use cases to speed up manual processes and maximize time to insights.

Lead Qualification

Which companies would be best to reach out to and most likely to sign up for your product or service?

Churn Prediction

Given a users events from your application, predict whether or not they will churn.

Content Moderation

Quickly find out if any user generated content may violate your policies.

Call Center Routing

Given the customers request, who should the call be routed to?

Sentiment Analysis

Is your Net Promoter Score really 9? Pass your Intercom messages through a sentiment analysis engine and find out at scale!

Conversion Prediction

Given your recent free signups, find out who you should contact to have the highest chance of converting them to a new customer.

Sales Forecasting

Given historical sales figures, accurately predict future sales

Fraud Detection

Send Quicknet your customer’s account history or payment details to detect if a transaction is fraudulent.

Applicant Screening

Given the contents of an applicants resume, are they a good fit for a particular role?

Product Demand

Given recent and historical orders, predict when demand will spike and stay ahead of inventory issues.

Ad Optimization

Which ad copy will perform the best? Plug in previous ads and their performance to start predicting what will work.

Anomaly Detection

Detect when something goes awry whether thats sql queries or customer usage.