I started my marketing career at the end of the 2000s. At that time, the digital marketing landscape was scarce and full of uncharted waters. A lot of decisions were based on gut feeling, and the cost of human error was high. There were no solutions in place for many marketing needs due to the lack of technology, so we had to swelter a lot to create kludges and workarounds (while this sparked a lot of creativity, it also significantly slowed down our execution).
At that time, we, marketers, could only dream of having someone build a one-stop-shop solution that would automatically analyze all of our campaign data and provide actionable recommendations based on that data.
A new AI era we entered in the 2010s made a lot of impossible things possible.
Yet, even today, with a plethora of AI-based tools and systems, marketers still have to do the lion's share of the work, focusing on the manual part of their operations rather than strategic decision-making. To get the most out of our campaign data, we still have to rely heavily on data scientists to help translate data into actionable insights.
No-code or codeless AI seems to be the solution we've been waiting for for a while.
As I was evaluating the current market for no-code AI platforms, I came across Pixis. This California-based tech startup provides codeless AI infrastructure to enable customers to scale accurate data-driven marketing.
To better understand how no-code AI works and benefits marketing teams and what marketers need to know before tapping into the codeless AI infrastructure, Ive talked to Vrushali Prasade, CTO and Co-Founder of Pixis.
Vrushali is a young entrepreneur with an extensive background in tech. After graduating from university and prior to starting Pixis, she founded Absentia Virtual Reality, which used AI to build a disruptive tech stack for game developers, enabling a faster go-to-market.
In 2018, Vrushali co-founded Pixis with Shubham A Mishra and Hari Valiyath, both of whom she'd worked with on numerous AI projects before.
In January 2022, Pixis announced it had raised $100 million from Softbank and General Atlantic, along with existing investors, just four months after raising $17 million in Series B. SoftBank and General Atlantic were the two new investors, with SoftBank leading the round. The total funding attracted to date is $124 million.
Enjoy the conversation!
Vrushali, what's codeless AI and how can it drive AI commoditization and mass adoption?
In very general terms, no-code AI is a code-free technology that enables non-tech folks to get control of AI solutions through a user-friendly, intuitive interface and build their own models against different parameters. So, basically, it allows anybody to harness the power of AI without being an AI engineer or a data scientist.
Marketing automation is where AI has recently become a fundamental game changer.
Innovative solutions typically go mainstream and become commodities as they become more available to mass users. As such, no-code AI infrastructure has a huge potential to drive AI mass adoption across industries and verticals.
What's the current size of the codeless AI market?
I've come across some articles in Think Tank publications saying that as of 2021, codeless AI has generated $2 billion in revenues and is expected to reach a staggering $38.5 million at a CAGR of 28% between 2022 and 2032.
What're the most critical challenges facing modern marketers, and how can no-code platforms like Pixis help solve them?
The critical challenge that many marketers face today is the scaling at which their ideal campaign setup will work perfectly well, allowing them to explore all possible audiences, creative communications, and strategies. They need to make a lot of decisions for their campaign optimization. Human intelligence is still required to fuel the right data into the automation systems, which is laborious and time-consuming.
This is where Pixis can be a "rescue ranger": we build a robust no-code AI infrastructure that enables profound depth of insights, allowing marketers to select the exact AI systems or combination of models they need to deploy to improve their marketing outcomes. And recommendations will be unique for their brand only.
Once the appropriate systems and models have been selected, all that needs to be done is to activate the AI and watch it begin to learn and act.
From a technology perspective, what does it take to build a codeless AI ecosystem for marketers?
There're three core elements at the foundation of codeless AI development.
First, it's the proper data sync. Our AI infrastructure enables a host of integrations with 3rd party platforms. It fetches data from these platforms at the required scale for our AI systems to analyze.
Second, it's a combination of AI and engineering services to build meaningful connections between users and AI dashboards.
The third would be the execution of whatever decision has been made based on the AI recommendations and ensuring it happens at the right time, with the right events, etc.
From the languages and frameworks point of view, we mainly use Python and MERN stack for the backend (data and AI models), and React and Nodejs for the application's front end. Most of our infrastructure is hosted on AWS.
For deployment standardization, we use EKS, Kubernetes, and other tools. We've recently started exploring QFlow for our deployment pipeline automation.
Pixes Founders: Shubham A Mishra (CEO), Vrushali Prasade (CTO), and Hari Valiyath (CBO)
From a user perspective, how do you make a difference compared to similar codeless AI solution providers?
Let's look at other no-code AI solutions available in the market today. The present landscape mainly consists of two types of players.
The first have built their own robust AI tech that is able to provide a set of recommendations to marketers, who then make decisions based on those recommendations and apply them to their campaigns. However, these solutions aren't actionable in nature.
The latter provide rule-based solutions, not really powered by AI, which restricts insights generation for marketers.
What Pixis does differently is as follows. Our AI infrastructure has a self-evolving or contextual nature with a robust feedback mechanism. So, basically, what happens is that our AI systems constantly communicate with each other. This leads to the infrastructure growing and evolving along with a brand's audience.
In essence, the infrastructure generates insights and takes actions unique to each brand.
And what should marketers know before tapping into the no-code AI infrastructure? Any pitfalls to be aware of?
I believe one of the most important things is to make sure that you shift your whole marketing approach from operations-first to a strategic one.
In the past, 40% to 50% of marketers' efforts were spent monitoring their campaign performance, making decisions like whether to increase or decrease the budget, which campaigns to turn off, etc. Even today, for most marketers, about 50% of their time is still spent on operational activities, which takes them away from their strategic focus.
Before harnessing the power of codeless AI, marketers need to start thinking broadly and realistically. At the end of the day, most manual jobs they did in the past and still do now will eventually be replaced by AI-based intelligence solutions, leaving them with a lot of time on their hands to concentrate on strategic decision-making and experimentation at scale.
Based on your experience, what's the current level of codeless AI adoption among marketers? And what prevents most marketing teams from taking this path and jumping on the codeless AI bandwagon?
Typically, multiple stakeholders are involved in running a marketing campaign. Brand managers make decisions about the communication and creative part of the campaigns. Digital marketers work day to day on campaign optimization. Others make decisions about media buying budgets and planning, etc. The more people are involved in a process, the more difficult it is to promote a new way of doing things and get their buy-in. As such,
multiple stakeholders contributing to one major decision pose a challenge to new platform adoption.
We tried to pinpoint and address this issue by creating the right product experiences that would cater to multiple marketing stakeholders, which can help facilitate a new automation-driven approach.
From a marketing manager's standpoint, they have to spend a boatload of time learning the ins and outs of each marketing platform they're going to work with. There're a lot of books and courses on Facebook Marketing or Google Marketing, so they really have to invest a lot of effort into expanding their knowledge and gaining first-hand experience with those tools and platforms.
So, whenever a new solution pops up that requires a steep learning curve from them, it's the solution provider's responsibility to make sure this new learning curve aligns with their existing user experience and work approaches.
We've seen a couple of companies with good AI offerings but a highly complex user experience, which can hold back mass adoption, too.
As such, it's both a challenge and an opportunity for us, innovative platform providers, to educate people in data-driven marketing and show them the clear benefits of shifting their marketing mindset to be more automation-focused.
What's your typical customer portrait?
At present, Pixis is targeting large, enterprise-level customers. Later, we may start building and opening our solutions for SMBs. Our user base is currently split between agencies using Pixis to support their customers, and in-house marketing teams.
And what about user data privacy? How reliable is Pixis from a security point of view?
Pixis doesn't use personal information (PI) at all. Our solutions are based on the aggregated data and provide insights at the cohort level.
There're three core solutions we offer:
- Governance AI makes decisions around the budgets and metrics, it is the AI system that is also responsible for continuously communicating and providing feedback to the rest of the AI systems. It governs the entire process allowing for real-time strategy updates with targeting and communication delivery;
- Targeting AI deals with media planning, customer behavior and engagement feedback;
- Creative AI deals with generation of contextual text and creative assets.
All the recommendations are provided at the cohort rather than individual level. All insights we generate for brands are also based on aggregated data and don't require any PI ingestion. Our AI looks into particular industry trends, analyzes the tone of voice and other indicators, and generates actionable campaign optimization and improvement recommendations.
Of course, when working with brands, we may have to ingest some of their proprietary data to the core of our AI so that it can learn from it. Whenever we do it, we set up complex API-based integrations and never actually take the customer data away from their original sources.
Pixis has raised an impressive $124 million in funding. That's a real success story. What piece of advice would you give to fellow entrepreneurs who're just at the start of their fundraising journey?
The key driver of attracting the funding was our steady growth in revenue, customer expansion, pipeline of AI products in R&D and the clarity of vision we hold for Pixis. The scale and rate at which we were growing and developing our codeless AI infrastructure seems very exciting for investors.
Our main pitch was based on the comparisons of how marketing teams operated in the past versus how they can operate now with the help of Pixis. The numbers we shared spoke for themselves. For instance, many customers who use our products have seen areduction of their customer acquisition costs by 40%-50%.
When you want to hit the market with an innovative solution, you often can't resist the temptation to help solve a dozen different problems.
My advice for fellow entrepreneurs would be to figure out one core problem plaguing their target audience and pinpoint solutions tailored specifically to solve it.
We had the same situation at Pixis – initially, we intended to help marketers solve as many problems as possible with AI. Yet, once we figured out our core problem statement, it became a game changer for us. So my advice would be,
find your key hook and let your solutions blossom out from there in a logical and directional manner.
From your perspective, do you believe AI will eventually take over human intelligence, or how will humans coexist with AI in the future?
From a human standpoint, if we look at it historically, thousands of years ago, physical strength was valued a lot, as it was essential for survival. Then, over time, engineering solutions like a wheel helped humans get away from laborious physical work to machine-based activity. That was the beginning of an IQ era.
Today, we're basically going through very similar transformations, moving from machine-based activities to intelligent automation. However, we still need human intelligence to make strategic decisions in our AI era. As such, AI is just another tool for us to improve our job in a more precise and reliable way.
So, answering your question, I don't think AI will take over humans. However, people will have to learn many new things to harness the power of AI and operate it in a risk-free environment.
An exciting path is ahead of us, isn't it?
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