Following the release of TypeSafe's Jev, many AI players have launched competitors, and many similar open-source projects from the likes of Microsoft, OpenAI, and many more have since emerged. Yet Nandakishor Mukkunnoth, of ConvAI Innovations, said that he'd come up with the base notions for Jev's architecture over a year before. So who copied who?
As a quick recap, Jev and its clones are APIs driven by decision models, for use in your own code. You feed them input data like a customer's online shopping transactions, alongside questions. For example: "is the user looking for a return?" for a yes/no answer, and "Which item are they referring to?" with a multiple-choice answer. A Jev-like AI model will answer each of the questions or choices with a probability that can then be used in your own "if" statements. While the concept by itself isn't strictly new, this practical application is indeed novel.
The release made a good portion of the internet abuzz, as the seemingly simple concept is extremely useful and neatly sidesteps the usual problems with trying to coax an LLM into giving a direct answer. There's no token-by-token text generation, no conversations, no re-feeding of conversation history, no feeding it gobs of information for context, and no conversational hallucination — just direct data in the form of probabilities. It's really fast too, answering questions in parallel, and exceedingly cheap compared to standard LLMs.
Along with Jev, TypeSafe also released a brief description of its System One architecture. Only days later, Nandakishor Mukkunnoth, founder of ConvAI Innovations, explained in a blog post that he'd actually "designed the Jev architecture one year back." He referred to his arXiv paper on SalesRLAgent, describing how he created a model for probability estimation, published on March 30, 2025. Along with the paper, he published his model weights and dataset. He later followed up with a September paper that more closely aligns with how Jev operates.
In his write-up, Mukkunnoth expressed his frustration, describing how he got overlooked "because [his idea] was built for a vertical use case," meaning SalesRLAgent was made to fix one specific problem, while TypeSafe made Jev a general-purpose decision engine. Notably, the company released Jev as a complete, hosted product, with its own servers, API, documentation, developer kits, and naturally, pricing.
Nevertheless, he said he opted to not remain bitter about the development, and ultimately released the open-source Laya roughly the same day. Laya is essentially a Jev-style model and interface that you can run on your own hardware, complete with multi-language support. Likely adding to the confusion, both TypeSafe and Mukkunnoth called their models "System 1/One" types, likely as a reference to Daniel Kahneman's book, Thinking, Fast and Slow, which describes System 1 as a faster, emotional mode of thought, whereas System 2 refers to slower, more logical thought.
Regardless, there was no stopping the hype generated by this new use case for AI, so players in this space did what they do best: copied the concept and rolled out their own services. In the space of only a few weeks after Jev was released, the world saw Microsoft Decision, OpenAI Decisions API, and Fastino Glide. Both Cloudflare Clef and Perplexity Decider arrived with open weights, meaning you can run them on your own gear, assuming it's up to the task. Over on the open-source aisle, there's Amazon's Strands Decider 2B, Kev, Von, Jeff, jevlike, SemIf, OpenJev, NanoJev, Ollaya, and many others.
Why so many? Well, because most of these projects, including the commercial ones, share the same root: taking an existing lightweight model, usually always a variant of Alibaba's open-source Qwen, and adding the extra layers that shape the input, output, and decision-making procedure. Mukkunnoth's Laya is based on encoder-only models from the Bert family, rather than an LLM like Qwen. And yet it's not too hard to imagine that many folks might have cribbed at least some notes from his work, given how quickly the alternatives materialized.