AI Hustle: News on Open AI, ChatGPT, Midjourney, NVIDIA, Anthropic, Open Source LLMs: Google Boosts Context AI's LLM Analytics with $3.5M Backing

Welcome to the OpenAI podcast, the podcast that opens up the world of AI in a quick and

concise manner.

Tune in daily to hear the latest news and breakthroughs in the rapidly evolving world

of artificial intelligence.

If you've been following the podcast for a while, you'll know that over the last six

months I've been working on a stealth AI startup.

Of the hundreds of projects I've covered, this is the one that I believe has the greatest

potential.

So today I'm excited to announce AIBOX.

AIBOX is a no-code AI app building platform paired with the App Store for AI that lets

you monetize your AI tools.

The platform lets you build apps by linking together AI models like chatGPT, mid-journey

and 11 labs, eventually will integrate with software like Gmail, Trello and Salesforce

so you can use AI to automate every function in your organization.

To get notified when we launch and be one of the first to build on the platform, you

can join the wait list at AIBOX.AI, the link is in the show notes.

We are currently raising a seed round of funding.

If you're an investor that is focused on disruptive tech, I'd love to tell you more

about the platform.

You can reach out to me at jaden at AIBOX.AI, I'll leave that email in the show notes.

The first thing I want to talk about is that this move is that context is making right

now is really kind of reflecting the rapid growth and interest in AI.

This is a London based startup and context AI has successfully secured like I mentioned

$3.5 million in funding and what I think is interesting here is their backers.

Their backers include Google Ventures, Tomasus, Tongues from Theory Ventures and a bunch

of other undisclosed ventures, but I think Google is kind of the big headline backer

of this project.

The company specializes in providing analytics for enterprises to optimize applications

powered by LLMs.

This is kind of interesting.

I think you may have started to notice a trend here and that is that there are a ton of analytics

companies that are getting funded right now that essentially companies that are working

with data companies enterprise to help them use their data and get analytics, get insights

out of that data.

It is really just like the big hot topic.

I swear like 2023 is the year of data analytics companies.

When I recently went to the AI for conference, I noticed a lot of the vendors at the conference

aka companies that had some solid amount of money to spend on becoming a vendor were data

analytics companies.

It is interesting.

I think it is a really big space that we are seeing right now and a place where a lot

of capital is currently getting deployed.

This fresh capital injection aims to really scale context AI's engineering team and bolster

their analytics platform.

The strategic growth occurs at I think what is a critical kind of crucial time when organizations

worldwide are aggressively adopting AI specifically LLMs to streamline their operations and kind

of innovate customer experiences.

What is interesting is McKinsey actually estimates that if the current trends hold, generative

AI technologies could contribute to up to 4.4 trillion dollars annually to the global economy.

This is really impressive and honestly they are saying if the current trend holds, I am

assuming this trend accelerates, that is my opinion, but I am sure everyone has got their

own opinions on this.

I see this as really something that is going to continue to grow and scale quite large.

Let's talk about some of the challenges in developing LLM powered apps.

Although LLMs are capturing significant attention, developing applications based on these models

isn't without its challenges.

Ensuring a model's performance, tracking its usage and kind of guaranteeing accurate

unbiased responses definitely necessitates a lot of really kind of a very keen oversight

you could say.

So I think without that insights, product development risks become essentially directionless.

So Henry Scott Green, who is formerly a product manager at Google, kind of captured this sentiment

in a statement.

He said, quote, we talked to many product founders in the AI space and discovered that

this lack of user understanding was a shared critical challenge facing the community.

That was when we decided to build context.

So context AI addresses these challenges by offering a robust analytics platform geared

towards LLM powered applications.

And I think that the big thing here is that the platform kind of dives deep into user

engagement and also product performance.

So beyond just, you know, the standard metrics like conversion volume, subject matter and

user ratings and satisfaction, that kind of thing, context AI analytics specifically

kind of is set up to tackle some of the more nuanced elements.

And I think these include topics like, you know, tracking, like really specific tracking,

even potentially hazardous topics and kind of the full conversation transcriptions that's

going on.

And the platform's inner workings are seamless.

So they said, quote, we ingest message transcripts from our customers via API.

And we have SDKs and a LangQing plugin that makes this process take less than 30 minutes

of work.

This was Scott Green talking about it.

And he kind of elaborated on this and said that these ingested transcripts are then run

through machine learning workflows, helping teams better understand user needs and fine

tune their products.

So since its inception, which was around four months ago, context AI has won over several

paying clients, such as Cognizies, Juicebox and ChartGPT.

In addition to multiple large enterprises, well, inbound by not, well, you know, like

pretty much they're bound by a bunch of nondisclosure agreements, but, and so they couldn't divulge

too many specifics about those deals.

However, with the new funds, context plans to scale up by hiring a technical founding

team, thereby speeding up product development, they said, quote, our goal is to continue

growing our customer base while delivering value to the businesses using our products

and we're seeing success.

So talking a little bit about the competitive landscape, who else is in this space?

Who else is doing this, right?

I think the ever increasing demand for LLM powered applications is also giving rise to

a lot of competition.

And that's in the performance tracking solutions kind of arena.

So as a, so when we're looking at it, there's firms like Arise that have already rolled

out products like Phoenix, which visualize complex LLM decision making.

There's DataDog, which is another kind of contender offering model monitoring capabilities.

Despite this, right, despite other companies already doing it, Scott Green believes that

context AI offers a more in-depth analysis than competitors, aligning more closely with

web product analytics firms like Amplitude and Mixpanel.

So the funding round also included participation from a bunch of different tech luminaries and

venture capital firms like 20 VCs, Harry, Stebbings, Snake founder Guy Pujari and Google

DeepMind, Mehdi Gissari.

So there's a couple, you know, big players that are currently investing in this.

And I think is AI kind of continues to, you know, redefine the boundaries of technology

startups like Context AI are becoming increasingly vital in helping organizations navigate the

complex landscapes of large language models.

I think with this latest funding round, Context AI is kind of poised to play a bit of a significant

role in this rapidly evolving ecosystem.

Really interesting startup.

So it's going to be interesting to see how that capital is deployed and how they plan

on scaling this into the future.

If you are looking for an innovative and creative community of people using chat GPT, you need

to join our chat GPT creators community.

I'll drop a link in the description to this podcast.

We'd love to see you there where we share tips and tricks of what is working in chat

GPT.

It's a lot easier than a podcast as you can see screenshots, you can share and comment

on things that are currently working.

So if this sounds interesting to you, check out the link in the comment.

We'd love to have you in the community.

Thanks for joining me on the open AI podcast.

It would mean the world to me if you would rate this podcast or wherever you listen to

your podcasts and I'll see you tomorrow.

Machine-generated transcript that may contain inaccuracies.