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krzcinski

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A little backstory behind Raport.

Me and my friend have been running several side projects. In order to grow, we had to stay up to date with our metrics, such as: revenue, page views, bounce rate, SEO clicks, social media engagement and many, many more.

These metrics came from different data sources such as: Stripe, AdSense, Google Analytics, Search Console or Google Ads. For example revenue was coming from Stripe, Google AdSense and 3 other publishing partners so we had to put it all in Google Sheets to see how we're performing.

We finally realized how much time and effort it all takes. The time we should spend on marketing, sales and product development. That was when we thought it would be great to have an app that would: connect metrics from all these web apps, display all our data in one dashboard, send us email reports, send alerts when an anomaly occurs, provide root-cause analysis.

So we started searching but the few products we found didn't meet our expectations. That's when we decided to build Raport.

Right now it's almost ready. We will most likely release free beta version next week.

If part of your work is analyzing data and want to see what we're building, just put your email address in the waiting list form. We’ll reach out directly once we move to beta.

Thank you, I'm glad you liked it!

It's not difficult at all as we have our own plugin. After signing-up just go to Welcome -> connect your blogging platform -> WordPress. You will find a step-by-step guide on how to connect it.

Converting audio is super-easy with this WordPress plugin.

We and cloud providers charge different things as they charge for the number of processed minutes and we charge for articles.

The thing is, with cloud providers you need to use API and we built the whole UI and automated the process so that it's just more user-friendly.

But if you know how to convert articles just using cloud providers and not BlogAudio, then most likely you'll pay less.

Thank you for a kind suggestion as I'm fairly new to this community. I edited the title as recommended.

As for your question. We utilize voice models made by cloud providers (Google, Amazon) and apply custom NLP, to achieve better speech fluency and to handle edge cases such as pauses in text, numbers etc.