Writer | 🏆 October’s Retail Tech Startup of the Month

Foreign let's give a big Omni Talk welcome back to AWS's Danele Stropa because Danielle is here once again to hand out this month's Retail Tech Startup of the Month award.

Danielle, for those who may not remember or, or who are new to our show, is the worldwide technical lead for AWS Partners in Retail at Amazon Web Services where he drives the technical strategy for AWS Partners in Digital Commerce, Customer engagement and Generative AI.

Daniela, it's great to have you back.

I think Ed, we need a drum roll here.

I know I don't have a drum roll like little cue for the.

I don't have a sound effect yet.

But I know we need to get this sound effect.

Drum roll, drum roll, drum roll is what we'll have to say.

That's good.

Drum roll, drum roll, drum roll.

Yes.

I love that ad.

All right, so Dan, who is October's Retail Tech Startup of the month?

So this month Retail Tech Startup of the month is Rytr and that's W R I T E R and they are a full stack generative AI platform that's really transforming the way that retailers and brands are automating process and generating content across their whole organization.

Okay, so Danelli, tell us a little bit more like what's differentiating Rytr compared to some other players in the space and why is it so important for retailers and brands listening to kind of know about what they do?

Yeah, I think what's what makes them particularly interesting is their end to end approach and solution.

Right.

They've built their own large language model which are some of the top scoring large language model out there.

They have their own knowledge graph based solution.

They have built in guardrails to ensure consistency and compliance with the content that is being generated.

They really provide a full package, a full solution.

On top of that they also have pre built, pre vetted agent and workflows that consumers, customers can take.

They can test them out, plug them into their system, test them out and then move on to production with these ready workflows.

So what that really means is that they are really accelerating the way that brands, retailers can go to production with these predefined pre tested solutions in a much faster way.

So Danielle, I'm curious too and this is a question I've never thought about until you just brought that up.

So we always know the big four, right?

The Geminis of the world, the chatgpt, perplexity, anthropic, we hear about those all the time.

But now you're saying there's this other option that does something different.

So if I'm a retail executive, how put rider in that context of how it sits alongside those other options and why am I choosing rider to do my work versus one of them?

Yeah, so when we look at the large language model landscape now, or generative AI landscape, right?

There is models that are good at a bit of everything and then there's models that are fine tuned, they are tweaked, they are a bit more specific for certain tasks.

This is what Writer has done.

They have models that are more specific for certain tasks and as I said, on top of that, they have these pre built solutions, pre built templates that are specific for an industry.

Right.

So they have agents specific for retail and consumer goods.

They have other industries there as well.

But this really means that this is specialized, specific solution for this industry.

They said they are pre vetted and they are pre tested.

So it's really almost as a plug and play solution that an organization can take and utilize like that.

Got it.

So if I'm an executive, what I'm taking from that is that it should enable the.

Specialization is the key word there and it should enable me to go faster and with more certainty or accuracy in the result I'm trying to ultimately get from using their solution.

Correct?

Yeah.

Okay.

All right.

Awesome.

Well, you heard it here first folks.

The October start of of the month.

Writer.

Thanks, Danielle.

Thank you.

Thank you.