SerpApi VS SheetRender

SerpApi's answer

We provide more search engines under one subscription.

SheetRender's answer:

Two reasons, and they are narrow ones. If you already have a document you like, you can skip template building entirely, which is the step where most people give up on tools like Autocrat or Form Publisher. And if what you need is one file per person rather than one merged document, that is the default here instead of a workaround.

If you are comfortable building templates with merge tags, or you want a PDF generation API to call from your own code, something like PDFMonkey or CraftMyPDF is a better fit. SheetRender is aimed at the person who has a spreadsheet and a deadline, not at a developer.

SerpApi's answer

Developers/Companies who need data from search engines.

SheetRender's answer:

Non-technical people who own a spreadsheet and need one document per row. In practice that is HR and operations staff sending offer letters, trainers and course administrators issuing certificates to a whole cohort, and small business owners producing invoices or quotes from a billing sheet.

The common thread is that they are not developers, they already have a document that looks right, and the job is repetitive enough to hurt but not big enough to justify building something custom.

SerpApi's answer

Ruby on Rails and MongoDB

SerpApi's answer

We're the first web scraping company that focus on scraping search engines.

SheetRender's answer:

Every other mail merge tool starts by asking you to rebuild your document as a template full of merge tags. SheetRender starts from the finished document instead. You show it one example, a certificate or invoice or offer letter you already have, and it works out the layout and builds the template for you.

The other half is the output. Every row in your spreadsheet comes out as its own separate PDF file, correctly named, rather than one long document you then have to split by hand.

SerpApi's answer

Back in 2017, Julien Khaleghy, the founder of SerpApi, built an iOS app that can analyze data from a picture. iOS didn't have a proper machine learning framework back then. It was challenging: iPhones' RAM were limited, no GPU or no dedicated chip acceleration were available, using only CPU was painfully slow, and compiling/porting C code from machine learning framework like Tensorflow or Caffe to iOS wasn't straightforward. Oddly, all of this wasn't the most difficult part of this project. Collecting images from Google Images was.

In these projects, 80% of his time ended up being spent on scraping and parsing Google Images. And maybe only 20% on actual machine learning model training, UI design of the actual apps, and iOS programming. This is how SerpApi was born.

SheetRender's answer:

It came from watching people hit the same wall. Someone in HR or training would have a perfect certificate or offer letter in front of them, and a spreadsheet of names, and the tool they were told to use would ask them to rebuild that document from scratch as a template with merge fields in it. Plenty of them stopped there and did it by hand, one at a time.

The question was whether the template step could be removed rather than made easier. If the finished example already contains the layout, software should be able to read it. That turned out to be the interesting problem, and the product grew around it.

SerpApi's answer

• Airbnb
• Nvidia
• Meta
• Shopify
• Grubhub
• and more!