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Unbounce snags Snazzy.ai to add automated copywriting to platform
Unbounce, a Vancouver startup best known for helping marketers create automated landing pages, added a new wrinkle this morning when it announced it has acquired Snazzy.ai, an early-stage automated copywriting startup. The two companies did not share the terms.
Unbounce Chief Strategy Officer Tamara Grominsky says that her company focuses on helping customers convert their customers into sales, and with Snazzy, it gets some pretty nifty technology based on GPT-3 artificial intelligence technology.
“We’re focused right now on building conversion intelligence software that will allow marketers to work with machines to really unlock their true conversion potential […] and we saw a huge opportunity with Snazzy to focus particularly on the content creation and copy creation space to help us accelerate that strategy,” Grominsky explained.
She points out that the product is really aimed at the marketing generalist charged with overseeing landing pages, and who is responsible for a range of tasks including writing copy. “The average Unbounce customer isn’t a specialized copywriter, so they don’t spend [their work] day writing copy. They’re what we would consider a marketing generalist or really someone who’s responsible for a wide range of marketing responsibilities,” she said.
Snazzy co-founder Chris Frantz says the tech is really about getting people started, and then they can tweak the results as needed. “The hardest part has always been to get that first line, that first page, the first couple of words in — and we eliminate that entirely. That might not always result in amazing copy, but on the plus side you can always click the button again and give it another try,” he said.
Frantz says that with so much competition in the space, he and his co-founder felt they could build a market much faster as part of a larger and broader marketing platform solution like Unbounce.
“I love Tamara’s vision for the future of Unbounce. I think she has a very ambitious vision. She sold me on that very early on in the process. At the same time, there was a lot of competition in the space, and to have a key differentiator with a company like Unbounce, which has a decade of marketing experience and a lot of trust within this community, I think it’s a very powerful wedge that we can use to further grow our audience,” Frantz said.
The tool lets you write a range of copy, from landing pages to Google ad copy. The company launched in alpha last October and already had 30,000 customers, which Grominsky says Unbounce hopes to convert into customers. The good news for those customers is that the company plans to leave Snazzy as a standalone product, while incorporating the tech into the platform in ways that make sense in the coming year.
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Okay, the GPT-3 hype seems pretty reasonable
This morning TechCrunch covered an interesting round for Copy.ai, a startup that employs GPT-3 to help other companies with their writing projects. GPT-3, or Generative Pre-trained Transformer 3, is a piece of AI from the OpenAI group that takes text from the user, and writes a lot more for them.
As part of the process of covering the Copy.ai round, I got caught up in the idea of AI-powered writing. I’ve long been more curious than afraid of automated writing. So when the Copy team described their very positive impressions of the GPT-3 AI writing tool to TechCrunch during an interview, I was intrigued.
To scratch this newly-formed itch, I doodled around this morning with a competitor of sorts to the Copy team , Headlime. And, freaking heck am I am impressed at what folks have managed to build around the GPT-3 technology.
Sure, GPT-3 can add words to a prompt. But the technology can do a lot more than that. The GPT-3-powered Headlime managed to not only write some medium-good stuff for me, but also managed bring in concepts concerning my reporting beat that were in my head but not in the prompts I provided.
I can’t do better than just show you what I mean. So, here’s what happened when I used Headlime for the first time, sans help.
Here’s the first thing that Headlime showed me, a language selector and a request for a description of the post that I wanted to write. I decided to push the system a bit by just telling it about a piece I need to write in light of today’s market action:
Ha ha, I thought, that will kick it in the teeth and I, a biped of intelligent meat wrapped around some calcium sticks, will feel grossly superior to the computer player. I hit go and then realized that I actually had to provide 500 characters of stuff, so I rambled for a bit to fill in required length:
Time for the next step! Hitting the button brought up a list of possible headlines for the post I was helping create, which were honestly not terrible:
Fair enough, yeah? At this point I was starting to become impressed.
I selected the first headline as it was my favorite and moved along. Next came the work to get an intro put together for the post, a process that involved the strenuous work of clicking a button:
Here are the options proffered:
Again, not bad.
What struck me about these are not merely minor variations on each other. They are structured differently, taking various angles on what I was halfway talking about in the 500 characters of bilge I had fed into the system. I was starting to wish that I had given GPT-3 a bit more to work with up top, as it was trying its best after I had clearly not.
Intro selected, I was brought into a CMS of sorts, where our selected bits were included, and your humble servant was asked to do a bit more writing.
I was happy to oblige, only for the system to stop me and offer to take over:
Having precisely no idea what a credit is, or what two of them cost as I was on a free trial of sorts, I hit the “Write for me” button. This is what came out:
Look at how it finished that sentence I started, even after I used em-dashes! The software gets the next sentence backwards, but is right back on the horse afterwards talking about how higher interest rates make exotic investment classes like venture capital less attractive! I was gobsmacked.
I will keep playing with the tech and the various software wrappers that are being built to productize GPT-3. More notes to come. But I wanted to pause and share my initial delight. This is cool. I can’t recall the last time that technology actually shocked me. But, well played GPT-3, you’re amazing.
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Writing helper Copy.ai raises $2.9M in a round led by Craft Ventures
Copy.ai, a startup building AI-powered copywriting tools for business customers, announced a $2.9 million round this morning. The investment was led by Craft Ventures. Other investors took part in the deal, including smaller checks from Li Jin’s newly formed Atelier Ventures and Sequoia.
The startup is notable for a few reasons. First for its model of building in public. I initially heard of the company through its monthly updates that it posts on Twitter. Thanks to that, I can tell you that Copy.ai generated monthly recurring revenue (MRR) of $53,600. That figure, up 46% from January, works out to annual recurring revenue (ARR) of $643,200.
Copy.ai also shares usage numbers, and, humorously, the number of Twitter followers that its founder Paul Yacoubian picked up in the last month.
The startup is also worth watching because it is part of a growing cohort of companies building atop GPT-3, what its progenitor the OpenAI project describes as an “autoregressive language model with 175 billion parameters.” More generally, it’s a piece of AI that can generate words.
Some investors are rather bullish on startups using the technology. Recently on TechCrunch, for example, Madrona’s Matt McIlwain wrote that “the introduction of GPT-3 in 2020 was a tipping point for artificial intelligence” that will lead to “the launch of a thousand new startups and applications.”
So far that’s holding up. Not only has Copy.ai managed to find early in-market traction, TechCrunch has covered a number of other startups busy leveraging GPT-3, including OthersideAi, which raised $2.6 million back in November of 2020, and an “AI Dungeon-maker” called Latitude that also employs GPT-3 and raised $3.3 million this February.
But enough about its cohort. Let’s get into how Copy.ai got built.
Origins
Before founding Copy.ai, Yacoubian was an investor and, it seems, a tinkerer. He played with GPT-3 predecessor GPT-2 when it came out, telling TechCrunch in an interview that he discovered that the tool generated lots of “nonsense,” with the occasional “flash of brilliance.” GPT-3 proved even better in his view, providing something akin to a “50x” improvement on the generation that came before it.
Leaning on Twitter as a distribution method — Copy.ai uses Twitter as a distribution channel, hence its reporting on social media metrics — Yacoubian and his co-founder Chris Lu launched a few different draft-projects using GPT-3. Simplify.so did text condensing, a slackbot was built but never made it to the outside world and taglines.ai was put together to help companies come up with slogans.
That last one found early traction, generating around 700 sign-ups in two days. That was enough of a user base, the co-founders decided, to begin monetizing their tool. Then they decided that the initial concept could be extended to other writing use cases, helping people with myriad distinct writing projects. Copy.ai was formed out of that concept.
The product can now generate text for blogs and products and headlines and the like, based on user-provided word inputs.
What’s odd and nearly antithetical to your humble servant as a writer is that Copy.ai doesn’t want to save you word count, per se. Instead, it generates a number of possible text results from which the customer then chooses. Recall the flashes of brilliance that Yacoubian said GPT-2 could generate? GPT-3 is even better, giving users of Copy.ai even better possible text formulations for their needs. And then the human-in-the-loop plays the editor role, choosing which they want the most and, I presume, tweaking from there.
When it was released back in October of 2020, Copy.ai snagged 2,000 sign-ups in its first two days. Then investors started reaching out.
Quitting their day jobs, Copy.ai became a full-time affair. The unorthodox startup also put together an unorthodox round, raising from what Yacoubian described as “as many people as [they] could.” That wound up being 80 people, give or take.
The round was raised as a capped SAFE, the Y Combinator-favored investing instrument that allows startups to accrete capital from external sources without a formal pricing; instead, SAFEs are often “capped” at a maximum valuation. Copy.ai raised its cap as its fundraising process trundled along.
David Sacks, founder of Craft Ventures, told TechCrunch that he thinks that “natural language generation powered by AI is going to change the way that marketing teams write copy,” adding that amongst startups it is “rare to see such strong bottom-up adoption in so short a time.”
I am honestly a bit excited to see what Copy.ai can do, not because I will use its product — it’s not precisely in my wheelhouse — but because I am rather excited about GPT-3 as a technology. And the startup is an in-market experiment regarding AI and writing. Two things I care quite a lot about.
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OthersideAI raises $2.6M to let GPT-3 write your emails for you
When I send an email, it’s special. A crafted, beautiful thing that — who am I kidding, it’s mostly automatic. So why not automate it? OthersideAI is taking this idea (with a $2.6 million seed round) beyond the auto-responders and smart replies, using OpenAI’s GPT-3 language generation engine to turn bullet points into full, personalized messages.
GPT-3, or Generative Pre-trained Transformer 3, is of course the latest version of the AI model that writes such convincing copy that everyone under the sun has let it write their column about it, and then attempted to surprise readers by revealing the fact at the end. (There are usually a few tells, though.)
Access is carefully limited, though, and the team at OthersideAI has a cozy but uncharacterized relationship with OpenAI . It began when the team was working on their previous project, and found they had more emails than they could handle. At the time, GPT-3’s predecessor GPT-2 was in vogue.
“We built a cold email thing with it, but then we thought — that might be the business we should be pursuing,” said CEO Matt Shumer. “So we decided to go all in.”
He and his colleagues Jason Kuperberg and Miles Feldstein built a demo that got a bit of attention when they posted it to Twitter, and soon obtained access to the new version of the GPT engine.
OpenAI arguably already did the hard part by building this astonishing language engine, but it’s not as simple as letting it run wild in someone’s inbox. Unrestrained, GPT-3 will chase its own tail down a rabbit hole, producing truly strange stuff, as any player of AI Dungeon can attest.
“GPT-3 makes an amazing demo, but putting it in a product is another story,” said Shumer. “Our job is in a sense to tame its creativity.”
The resulting product turns a summary or bullet points into a complete email, and looks like this in action:
Image Credits: OthersideAI
If you don’t like the result, or there’s an error, or you just like torturing AIs, you can hit the button and it’ll generate it again, differently. Tweak it a bit first and the system will understand that in the future you’d prefer the new way.
The GPT systems are trained on millions of words and phrases, and then generate text inspired by that corpus after being given an input to work from. In this case the system takes as input not just your bullet points, but other information from the email chain and the user’s past preferences.
That way it picks up not just context: it may say “It was great to sit down for coffee with you” if coffee is referenced even if you only wrote “good to meet” in the bullet. And it also learns your style, preferring certain words or phrases or learning that you like to sign off a certain way.
It can make good guesses at technical and financial details, such as in making a job offer:
Of course, for something so important, you may wonder: why bother letting an AI do it at all?
It’s sort of like how a car can go 120 MPH, but you never drive it faster than 80 (okay… 90). You want to know the thing isn’t going to fall apart as soon as it leaves its most obvious use case. For OthersideAI’s model, this means being robust enough to handle “serious” emails even if it’s most likely to spend its time replacing rote messages.
Kuperberg said the company, which has almost 10,000 people waiting to get into its test version, has seen interest from engineers and developers as well as sales and support people. One instantly sees the application in a support or sales scenario where a handful of scripted questions or replies can be re-generated to be different every time, or slightly adjusted for the person or situation. That avoids the feeling of receiving a “form email” even though it amounts to the same thing.
I mentioned the possibility of helping people who have trouble typing — someone who must write emails letter by letter using gaze detection might find this extremely compelling. Shumer said this hadn’t been on their radars to begin with but that in the last few weeks they’ve seen interest from this direction.
Shumer was careful to assure that security comes first and this isn’t a data-sucking operation — obviously no one would want to use a tool that reads your email and uses that info for nefarious purposes, with the notable exception of Gmail.
They feel secure in their approach, noting that Google seems more interested in selecting the right reply for the context, and text generation tools aren’t robust enough to handle the inputs OthersideAI’s GPT-3-based system handles with ease.
“If you want to make an email in the tone of the user, it can’t guess about the details. It needs a human. This isn’t a generated response, it’s taking direction,” Shumer said.
The $2.6 million seed round was led by Madrona Venture Group, with Active Capital, Hustle Fund, Chapter One and more participating. It’s all going toward building the team so the company can build a full-scale product.
Ultimately, they envision this as a small-scale test for a larger system of interlocking AIs that can safely and securely connect with one another, answering questions and providing information in a human-like way but with only the minimum human involvement. Obviously that’s somewhat of a long-term goal, but given all the talk for a decade or so about replacing email has come to nothing, perhaps it’s time to embrace it but let someone (or something) else take on a bit of the load.
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