Platforms to connect apps that wouldn’t normally talk to each other have been around for a minute (see: Zapier). But they have not gotten dramatically simpler to use if you’re nontechnical. Generative AI has lowered the barrier to entry somewhat. However, getting the most out of these platforms — and fixing things when they break — still requires a bit of programming know-how.

Software developers Sam Brashears and Edward Frazer perceived this to be the case as well. During internships at tech giants like Meta and Stripe, they struggled to get automations working using some of the more popular app-linking tools.

“I’d been dealing with the pain of designing integrations and automations from scratch,” Frazer said in an interview. “And Sam believed that generative AI models would solve the biggest problem in integrations — transforming data between APIs.”

So Brashears and Frazer, longtime friends who’d been building software together since elementary school, decided to try their hands at a streamlined, easy-to-use app-to-app integration platform.

DryMerge is the fruit of their work. A chatbot for building workflows, DryMerge lets you describe an automation you want between apps — for instance, “Whenever I get an email from a new prospect, ping the team on Slack and add them to HubSpot” — and handles the necessary technical scaffolding.

“Currently, IT departments use complicated no-code tools to automate workflows on behalf of non-IT teams,” Frazer said. “A natural language interface opens up automation to nontechnical people.”

It sounded like a neat idea, a chatbot that can string apps together for you — particularly if you, like me, have spent countless hours wrestling with IFTTT. So, I decided to give DryMerge a go, hoping to replace my old and rickety automations once and for all.

DryMerge’s UI is quite clean and minimalist. It reminds me a bit of ChatGPT; there’s not much to look at besides a text bot. Each new request (e.g., “Text me a summary of my calendar meetings every morning”) starts a new chat session, and these sessions can be revisited at any time from a list on the left-side panel.

DryMerge hooks into an expanding library of apps, including Gmail, Microsoft Outlook, Salesforce, storage services like Dropbox and OneDrive, social media platforms (e.g., X), and messaging clients (e.g., Discord). Once the platform creates an automation with these, it plops that automation into a dedicated window showing when the automation last run and whether DryMerge encountered any errors.

I tried setting up a few automations I thought might be useful for a reporter with an overfull schedule, like one to throw Gmail contacts into a spreadsheet and add dates from recent email invitations to a Google Calendar. Things started out promising — DryMerge had me log into the relevant apps and asked whether I’d like to test the automations to ensure everything was working properly.

But then, problems started to crop up.

Several times, DryMerge’s chatbot stopped responding altogether. Other times, it missed key details in a request. I tried repeatedly to get DryMerge to understand that I wanted to copy Gmail contacts to my Google Calendar, but every attempt, it thought I wanted to manually enter contacts into a spreadsheet.

The setbacks didn’t completely ruin my DryMerge experience. Giving credit where it’s due, the platform’s nifty when it works. For example, I successfully got DryMerge to set up an automation that copies posts from my X account to the personal Discord server I use to aggregate various notifications. A niche use case? Perhaps. But it’s going to save this reporter a lot of task switching.

The bugs, Frazer assures me, will be addressed in time. He and Brashears are DryMerge’s only employees, so there’s lots on the to-do list.

“We think we’re well-positioned to iterate quickly and nimbly,” Frazer said.

Assuming Frazer and Brashears can get DryMerge’s platform in good working condition, the bigger challenge the duo will have to face is staying relevant in the fiercely competitive integration-platform-as-a-service (iPaaS) space. According to recent poll released by IDG and TeamDynamix, iPaaS is one of the fastest-growing software markets, projected to reach $2.7 billion this year.

AWS has its own iPaaS called AppFabric. IBM recently acquired iPaaS tech from Software AG. A growing number of startups aside from DryMerge are attempting to break into the segment, while incumbents like Zapier and IFTTT are aggressively deploying generative AI capabilities.

Frazer makes the case that DryMerge’s differentiator is — and will remain — “being 10x easier to use” than drag-and-drop integration builders.

“Our users include online fashion retailers, school administrators, and asset managers — the vast majority of which have never touched a line of code,” he said. “They use us to save hours a day on tasks ranging from customer support automation to customer relationship management data entry.”

Frazer’s not wrong about the opportunity. Per the IDG and TeamDynamix poll, 66% percent of companies said that they’ll invest in iPaaS to address internal automation and data integration challenges.

“We think a gigantic enterprise opportunity is in increasing the simplicity of automation and delivering easy-to-use tooling that empowers nontechnical folks,” Frazer said.

It’s very early days for DryMerge, which only has around 2,000 users at present. But the company was accepted into Y Combinator’s Winter 2024 batch, and DryMerge this past summer closed a $2.2 million seed round led by Garage Capital with participation from Goodwater Capital, Ritual Capital, and angels whose names Frazer wouldn’t reveal.

Frazer says that the funds are being put toward adding new app integrations and doubling the size of DryMerge’s team in the next few months.

A startup whose product competes with GitHub Copilot and other AI-powered coding assistants has achieved unicorn status.

On Thursday, Codeium said it closed a $150 million Series C round led by General Catalyst that values the company at $1.25 billion post-money. The round, which also saw participation from existing investors Kleiner Perkins and Greenoaks, brings the company’s total funding raised to nearly a quarter-billion dollars ($243 million) a mere three years since its launch.

Codeium’s co-founder and CEO, Varun Mohan, said that Codeium hasn’t even touched the $65 million Series B tranche it raised in January yet. Back then, just eight months ago, Codeium was valued at half a billion dollars.

“Even though we’ve barely made a dent in our existing funding, we believe that this injection of capital will allow us to significantly ramp up R&D and growth while making even larger strategic bets,” he said.

Codeium was founded in 2021 by Mohan and his childhood friend and fellow MIT grad, Douglas Chen. Prior to Codeium, Chen was at Meta, where he helped to build software tools for VR headsets like the Oculus Quest. Mohan was a tech lead at Nuro, the autonomous delivery startup, responsible for managing the autonomy infrastructure team.

The startup began as a radically different company called Exafunction, focused on GPU optimization and virtualization for AI workloads. But in 2022, Mohan and Chen sensed a bigger opportunity in generative coding and decided to rebrand — and pivot.

“Despite the influx of generative AI tools, developers are still struggling with time-consuming coding tasks,” Mohan said. “Many of the AI-driven solutions provide generic code snippets that require significant manual work to integrate and secure within existing codebases. That’s where our AI coding assistance comes in.”

Codeium’s platform, powered by generative AI models trained on public code, serves up suggestions in the context of an app’s entire codebase. It supports around 70 programming languages and integrates with a number of popular development environments, including Microsoft Visual Studio and JetBrains.

To attract devs away from Copilot and other rivals, Codeium has released a generous free tier to start. The strategy seems to have worked: Today, the startup has more than 700,000 users and over 1,000 enterprise customers, including Anduril, Zillow and Dell.

Quentin Clark, managing director at General Catalyst, implied that Codeium won some of its larger contracts by embracing a steadfastly client-centric approach to product research.

“The team’s approach has always been to follow its customers, leading the company to build solutions on their terms — deployable in any environment and supporting more languages than anyone else,” Clark said in a statement. “What Codeium has created isn’t just a demo, an announcement, or an idea — this is a fully scaling business, with large enterprises adopting the product across their entire organizations.”

Businesses are often wary of exposing proprietary code to a third party — for instance, Apple reportedly banned staff from using Copilot last year, citing concerns about confidential data leakage. To attempt to allay such fears, Codeium began offering a self-hosted installation option alongside its standard software-as-a-service plan.

Companies can now deploy Codeium’s service on their own hardware if they wish. Or they can adopt a hybrid setup, keeping their data on their own devices while using Codeium’s servers for computing needs.

There’s always some risk involved in data transfers to the cloud, but Mohan claimed that Codeium leverages strong encryption. “We never train our proprietary generative autocomplete model on user data, never sell data and ensure all data transmission is encrypted,” he added.

Codeium has also taken steps to remove “non-permissively” licensed code (e.g., code under copyright) from the datasets it used to train its AI models. Some code-generating tools trained using restrictively licensed or copyrighted code have been shown to regurgitate that code when prompted in a certain way, posing a liability risk (i.e., developers that incorporate the code could be sued). Mohan said that’s not the case with Codeium, thanks to its training data prep and filtering approach.

“We also remove any remaining data that looks similar to code that is explicitly non-permissively licensed just in case other people copied code without providing the proper attribution and licensing,” he added. “On top of this, we have state-of-the-art, post-generation attribution filtering and logging in the case that these large probabilistic models produce code that is similar to public code, whether permissively or non-permissively licensed.”

But what about hallucinations? Most AI coding tools are notorious for making stuff up, which can be quite destructive in an enterprise environment.

An analysis by developer tooling startup GitClear found that generative AI tools have resulted in more mistaken code being pushed to codebases over the past few years. And a Purdue study found that over half the answers that OpenAI’s ChatGPT gives to programming questions are incorrect. Security researchers have warned of the potential for such tools to amplify existing bugs in software.

A recent survey from cybersecurity firm Snyk found that nine in ten developers worry about the broader security implications of using AI coding platforms. But Mohan claimed that Codeium’s supposedly superior, deep context-rich tech yields more trustworthy results than most.

“Our context awareness engine is able to ground results in what is already existing in a user’s codebase, leading to suggestions with fewer hallucinations and more adherence to existing syntax, semantics and standards,” he said.

Whether benchmarks back that up or not, Codeium’s sales pitch seems to be resonating with the right execs: Revenue hit eight figures this year. Mohan said the 80-person, Mountain View-based startup plans to expand headcount to 120 by 2025 as it aims to make a bigger dent in a market with formidable competitors like Tabnine, Anysphere and Poolside.

Catching up to Copilot, which had over 1.8 million paying users as of April, probably isn’t in the cards for Codeium — at least not imminently. It doesn’t have to be. As Mohan rightly noted, given the widespread adoption of AI coding tools among developers (despite their reservations), even a small slice of the nascent segment is bound to be lucrative.

Polaris Research projects that the AI code tools market will be worth $27.17 billion by 2032.

“An overabundance of hype is a challenge the industry faces,” Mohan said. “This will make it harder for every company to truly convince end users that they are at the forefront of possibility. But we believe that truth-seeking and realistic AI companies like Codeium will eventually cut through this noise.”

Jam & Tea Studios is the latest gaming startup implementing generative AI to transform the way players interact with non-playable characters (NPCs) in video games. 

Traditionally, video game NPCs are directed by predetermined scripts, which can feel repetitive, unrealistic and boring. It also may restrict the number of potential experiences for players. However, when generative AI is involved, players can engage in casual conversation and interact with NPCs how they want to (within reason).

Founded by gaming veterans from Riot Games, Wizards of the Coast and Magic: The Gathering, the company announced on Friday its first game that will leverage generative AI tools to help with gameplay mechanics, content generation, dialogue and item generation. 

Jam & Tea’s debut game, Retail Mage, is a roleplaying game that allows players to take on the role of a wizard working as a salesperson at a magical furniture store. The main goal of the game is to earn five-star reviews by helping customers. But it’s really up to the players to decide if they actually want to work or cause chaos. With AI NPCs as customers and human players being able to say and do almost whatever they want, the possible outcomes should vary widely.

In Retail Mage, players are approached by customers who each have their own requests. Instead of selecting from preset phrases, players can type in the text generator how they’d like to respond. The player can ask the AI to “say something charming,” and it will offer four different dialogue options. 

Jam & Tea is among several companies competing in the AI-powered NPC space, alongside Artificial Agency, Inworld and Nvidia. Ubisoft’s AI-powered “Ghostwriter” tool writes NPC dialogue for some of its games. 

The new game also comes at a time when there’s concern among creatives about the potential challenges posed by the prevalence of generative AI. Last month, SAG-AFTRA — the union comprised of voice actors and other talent — initiated a strike against major game publishers over AI concerns.

However, Jam & Tea claims it’s taking a balanced approach to the inclusion of AI, and wants to protect artists, writers and other creatives working in game design. 

“Our philosophy is that we believe creatives are going to be only more essential as we move forward in using this technology and in bringing new experiences to players,” co-founder and chief creative officer M. Yichao, who was the former narrative designer for Guild Wars 2, League of Legends and other titles, said.

“AI will generate all this dialogue, and you can talk to characters endlessly… but it’s going to take the creative eye and lens to really add meaning to that and to craft that into an experience that matters into something with impact, depth and emotion that carries through stories. That’s going to become more important than ever,” Yichao added.  

He explained that creatives are heavily involved throughout the development process, including when it comes to crafting NPCs, giving them motivation, interests and backstory, as well as providing example lines to help the AI mimic the tone and generate lines in real-time.

Limitations of AI NPCs

Despite its advantages, generative AI in NPCs has its limitations. One major concern is the issue of AI unpredictability, when the behavior of an NPC becomes excessively erratic, resulting in a frustrating experience for the player. AI can also hallucinate answers, so there’s a possibility that the NPC could say something that’s wrong or doesn’t exist in the world. 

Continuously improving the AI engine will help mitigate unpredictable NPCs, Yichao believes. Players can also rate the characters’ responses, which provides data to help improve the characters’ behavior. Plus, Jam & Tea claims to have put guardrails in place to prevent inappropriate conversations. 

Players are still encouraged to be creative, allowing for inventive and spontaneous interactions to occur. For example, instead of helping a customer, players can choose to engage in activities instead, like playing hide and seek — a real scenario that occurred during playtesting.

“Our lead engineer was playtesting one night and went up to the NPCs and just said, ‘I’m bored.’ And the NPC responded by saying, ‘Well, why don’t we play a game? Let’s play hide and seek.’ And so the other NPCs heard and said, ‘Oh, we’re playing too,’” shared co-founder and CTO Aaron Farr. The NPCs proceeded to follow the rules of the game, with one seeker walking throughout the store to find all the hiders. 

“None of that was programmed; all of that was emergent behavior. That is part of the delight of when we have what a player wants to do combined with its experience to modify the experience in real-time,” added Farr, a former engineering leader at Riot Games and Singularity 6. 

The company has been experimenting with various large language models (LLMs) throughout the testing phase, including OpenAI, Google’s Gemma, Mistral AI and Meta’s Llama, and other open models. It’s currently uncertain which LLM it will ultimately use in the final version of the game, but is fine-tuning the model to train it on how to give better responses that are more “in character.”  

Generate items out of thin air 

Jam & Tea’s AI engine goes beyond dialogue generation. Players can also interact with any object in the game and state their intentions with that object, such as picking it up or dismantling it for parts. They can even create items from scratch. Depending on what they want to do, the game interprets that intention and determines if they’re successful or not. 

In a demo, Yichao interacted with an NPC named Noreen, who asked for an antelope-shaped plush. He then typed a command into an action box and retrieved a pillow resembling an antelope from a crate. The game recognized his action as successful and added the item to his inventory. 

Because the item didn’t previously exist in the game, players won’t physically see an antelope-shaped plush appear. All that happens in the game is the item shows up in the player’s inventory as a default image of a pillow. If the player wants to perform an action, like sitting in a chair, a notification appears on the screen indicating that the action was performed. 

“One of the things that’s really exciting about this technology is it allows for open-ended creative expression. Like, I can take a piece of meat and say, what if I put it in the bowl and I make a delicious fish stew? We might not have a fish stew [image], but one of the things that I’m working with our artists on is coming up with a creative ability to represent that item in a way that’s satisfying in the world and allows the player’s imagination to fill in some of those blanks, and gives players maximum creative freedom to make things that are unexpected,” Yichao said.  

AI technology won’t be used for 2D or 3D asset generation. Real artists will create the images.

Retail Mage is a relatively basic game compared to others. At launch, the company promises to provide a more advanced product than the test version we saw during the demo. 

Jam & Tea states that the game is primarily intended to demonstrate the application of the technology as it continues to experiment. Beyond Retail Mage, the company is also developing another game — currently referred to as “Project Emily” internally — which will showcase their broader ambitions, featuring more environments and a sophisticated storyline.

The startup’s scrappy team of eight has a lot of work ahead to reach the level of bigger gaming companies. However, taking action now while there is momentum allows the company to adapt and grow as AI models advance. 

Jam & Tea raised $3.15 million in seed funding from London Venture Partners with participation from Sisu Game Ventures and 1Up Ventures. It plans to raise another round later this year. 

As for the business model, Jam & Tea will charge $15 to buy the game and offer extra game packs that players can purchase separately. It’ll launch on PCs initially, but the company aims to enable cross-platform functionality within the next few years.

Retail Mage is slated to be released to the public later this fall.