AIT Contacts Extractor – An AI-Powered Add-On for Gmail

Attention all Gmail™ users! Say goodbye to tedious manual contact extraction and hello to a more efficient way of growing your contact list. Introducing AIT Contacts Extractor, the AI-powered add-on that streamlines the process of adding new contacts to your list.

With AIT Contacts Extractor, you can quickly and easily extract important information such as name, job title, organization, mailing address, phone number, and email address from any text within the message body. This information is then stored in separate columns in a Google Sheets™ spreadsheet, making it easy to keep track of your growing contact list.

Not only does AIT Contacts Extractor save you time and effort, it's also safe and secure. Rest assured that your email messages and contact information will not be collected, read, used, or transferred by the add-on.

So what are you waiting for? Take your contact list to the next level with AIT Contacts Extractor. Get it now and start building and growing your contact list without ever leaving your inbox.

After using the 5 free requests, you can continue using AIT Contacts Extractor by purchasing a subscription. To sweeten the deal, I have a special promo code for you that you can use to get a discount on your subscription. Simply send me a message to request the promo code, and I'll be happy to provide it to you. Keep streamlining your contact extraction process and take your contact list to the next level with AIT Contacts Extractor. Get your promo code now!

USPS address parsing is the process of breaking down a postal address into separate components such as recipient name, street address, city, state, and zip code. It is performed to standardize and validate the address information and improve the efficiency and accuracy of mail delivery. The process can be done manually or using automated software tools and algorithms and reference databases are used to identify and separate address components. USPS address parsing is important for organizations that need to manage and analyze postal address data.

Address parsing is difficult due to complex address formats, abbreviations and acronyms, non-standard addresses, variations in address components, inconsistent data quality, and international addresses. It requires sophisticated algorithms and reference databases to accurately identify and separate address components. Despite advancements in technology, address parsing remains a challenging task that requires high accuracy.

I selected 20,000 addresses from a database of 180 million records using algorithms such as Stratified Sampling, Cluster Sampling, Systematic Sampling, and Simple Random Sampling. I then fine-tuned the model of OpenAI for USPS address parsing. The fine-tuning process involved pre-processing the task-specific dataset, training the model on the data, evaluating its performance, and adjusting its hyper-parameters to achieve improved accuracy in parsing USPS addresses.