Hello and welcome to 'This is My Architecture'.
I'm Joe.
And I'm joined today with Bailey from Levels Beyond.
Thanks for having me.
So, Bailey, talk to me a little bit about Levels Beyond?
What do you guys do, actually?
So we help our clients monetize their media,
starting from the camera card, taking all the way to the screen.
Outstanding, outstanding.
So you have a lot of AWS services up here.
Can you walk us through,
kind of, start to finish as far as how you guys ingest that data?
Sure.
So this right here is our media to cloud workflow.
And it starts with the user uploading a file to an S3 bucket.
That upload triggers a step function.
The first thing that the Step Function does is
kick off the transcode and MediaConvert.
Okay.
What exactly are you doing with MediaConvert there?
So MediaConvert is creating us a proxy
that's gonna to be good for playing back in the browser
so that our user is gonna be able to have,
kind of, visual check to go along with all the metadata
that we're gonna be extracting.
Do you have multiple customers
leveraging different kinds of video feeds, different formats?
Yep.
We have basically every format you can imagine
is coming into Reach Engine as pathway.
Okay. Okay.
- Yeah, continue. - Sure.
So after that proxy is generated,
the Step Function is going to trigger some Lambdas.
It's going to extract checksums,
other kind of standard technical metadata.
And it's going to store that over in DynamoDB.
Okay.
Now this is just metadata about the video itself,
just high level metadata?
Correct. Yeah.
You know, checksum of the videos so that you could,
you know, validate its authenticity.
It's going to pull out, you know, frame rates, all kinds of media info.
Useful for the customer that's gonna leverage that.
- Yep. - Okay, got it.
After all, that kind of basic metadata is extracted,
we're going to trigger another Step Function,
and that's going to start off in Rekognition.
Rekognition is going to take that video,
it's going to identify any celebrities,
any violence, any nudity,
any kind of data that our clients might want
to be able to extract from that content.
Now is that just tagging that information,
how are you handling that?
So we're gonna take all that data,
it's going to be associated with time codes,
and that is going to go into DynamoDB.
Okay.
After that, we're going to trigger Transcribe,
using that same Step Function.
Transcribe is going to take the audio from that content
and translate it into text.
After it's translated to text,
that's going to get driven into Comprehend,
where we can run analysis on that text to extract entities,
you know, understand things like when profanity is happening.
And then all the data that comes out of both of these guys
is going to go into DynamoDB.
Excellent.
Now that you store DynamoDB data forever,
is that something that we're...
So DynamoDB is a temporary data store.
So after the data is there, we're going to trigger,
ingest into Reach Engine.
Talk to me about Reach Engine.
So retention is really the meat of our product.
It's going to allow the users
to play the content back that they ingested here.
It's going to allow them to view all that metadata that was extracted.
It's going to allow them to search on all that metadata.
It's going to, you know, particularly with Rekognition,
it's going to make a nice visualization
where you can see new tracks for each celebrity
and when they appear in the video.
Yep.
And then with all this metadata,
they can pump that into our orchestration engine,
which will allow them to take that content from Reach Engine
and put in places where it's going to allow them to make...
And that's outside
of your Reach Engine platform at that point.
Correct.
Possibly on a client, it's hybrid, or wherever you guys deploy that.
- Yep. - Outstanding.
I love the solution.
You know, I do appreciate coming in and talking about your solution,
seems like a very powerful solution.
And I thank you for coming in and describing what that is.
- Thanks for having me. - Sure.
Thank you for watching 'This is My Architecture'.