AI Tools Are Everywhere – But Who’s Behind Them?

Let’s face it: AI tools are a dime a dozen in 2025. With the explosion of generative AI, it feels like every week another startup launches a new AI tool. OpenAI’s token costs have dropped 90% in the last year, meaning even “a college student in Bangalore can build and deploy a specialized [AI] model for less than the cost of their textbooks”. No wonder the number of companies working on generative AI has skyrocketed – by late 2023 there were an estimated 50,000+ generative AI startups globally, a figure that surged to over 67,000 by 2025. In short, AI is everywhere.

But here’s the kicker: most of these AI tools are built by teams with zero deep experience in the domains they’re trying to serve. Sure, anyone can fine-tune a language model nowadays – but have they ever managed a real sales funnel? Scaled an e-commerce platform? Built a search engine that serves millions of users? In many cases, no. The barrier to entry for building an AI product has never been lower, and that means a lot of inexperienced teams jumping into the fray. When everyone can spin up a basic AI, what separates the winners from the gimmicks? It comes down to the people behind the product. The team’s expertise (or lack thereof) directly influences an AI platform’s performance, trustworthiness, and how it evolves over time.

Trust and Performance Come Down to Team Experience

Customers and businesses are waking up to the fact that who built an AI tool matters as much as what it does. Why? Because trust and results aren’t guaranteed by the label “AI” – they’re earned through rigorous design, domain knowledge, and continuous improvement, all of which depend on the creators’ experience. Consider the trust factor: AI adoption may be rampant, but confidence in AI outputs is shaky. According to a 2025 Stack Overflow survey, a whopping 84% of developers now use AI tools – yet 46% don’t trust the accuracy of AI’s output. As one tech CEO put it, “AI tools are everywhere, but skepticism is growing”. In other words, people are embracing AI, but they’re double-checking everything it does. Why the skepticism? Because many AI products, built by teams that lack deep expertise, tend to produce “almost right” answers that create as many problems as they solve. When an AI platform’s suggestions have to be second-guessed, productivity suffers and users lose faith.

Now imagine an AI platform built by a team that truly knows what they’re doing – experts who understand the nuances of the domain and the technology. You’re likely to trust that platform more, right? There’s data to back this up. A recent study on AI startups found that “a leadership team with deep industry knowledge and a track record of success is better equipped to navigate challenges and scale the business”. In fact, investors actively look for founder–product fit: “The founder’s experience in the relevant industry reassures investors they can navigate the complexities of scaling a business”. In plain terms, a team that’s been there, done that is more likely to build an AI product that actually delivers. They know what pitfalls to avoid, which features matter to end-users, and how to evolve the tool as needs change. Experience becomes a moat – it’s the secret sauce that turns an AI solution from a neat demo into a reliable, high-performance platform you can bet your business on.

Think about the difference this makes in product evolution too. An AI platform isn’t a static widget; it’s a living product that needs to learn and improve. Teams with real-world domain experience will iterate based on meaningful insights (not just hype), prioritize the right improvements, and foresee problems before they hit customers. They’ll build trust into the system – through transparency, robust testing, safety checks, and aligning the AI’s behavior with real business goals. On the other hand, a team without that background might not even know what good looks like for a given industry, leading to an AI that flops in real deployment. When you choose an AI tool, you’re implicitly choosing the team behind it. And if that team has deep e-commerce, search, or sales experience, you’re choosing a tool that’s far more likely to earn your trust and drive results over the long haul.

Meet the Exception: Envive’s Founding Team (Experience Included)

In a crowded AI market, we built Envive to break that mold. We didn’t set out to ride the hype wave - we built Envive from the ground up with a team that combines hardcore AI research pedigree with real-world e-commerce and product expertise. (How often do you see that?) We are researchers and builders with deep academic roots and retail expertise – a blend that’s incredibly rare in today’s AI startups. Meet our founding team - and see for yourself how our experience shapes everything we build.

• Aniket Deosthali (Co-founder & CEO) – The commerce visionary. Aniket leads Envive with a bold, customer-first vision shaped by hands-on retail experience. Before Envive, he was Head of Product for Conversational Commerce at Walmart, where he spearheaded the creation of Walmart’s LLM-driven shopping experiences from scratch and scaled them to millions of users. He’s managed AI products at one of the world’s largest retailers, forging partnerships with Apple, Google, and Meta along the way. In short, Aniket knows how to build AI that actually sells products – and he wasn’t satisfied with the status quo. Envive exists because he saw how hard it was for customers to find the perfect product online, and he knew AI could do better with the right team behind it.
• Sameer Singh (Co-founder & CTO) – The safety & reliability guru. Sameer is synonymous with AI safety, robustness, and trustworthiness. He’s not just an engineer; he’s a world-class machine learning researcher (and a Professor of Computer Science at UC Irvine) known for groundbreaking work in making AI more interpretable and reliable. Ever heard of LIME (a popular technique to explain AI decisions) or CheckList (a method for testing NLP models)? Sameer co-authored those innovations. He’s published research cited nearly 50,000 times and won accolades from the National Science Foundation, NAS, DARPA – you name it. He’s worked at Google, Microsoft Research, and was a research fellow at AI2, advising several AI startups before co-founding Envivei. When it comes to trusting an AI system, having Sameer at the helm is like having a security guarantee. He bakes reliability and transparency into Envive’s DNA.
• Iz Beltagy (Co-founder & Chief Scientist) – The open-source legend. Iz is the mastermind behind Envive’s advanced AI algorithms – a recognized leader in the open-source LLM movement. Before Envive, he led the OLMo project at Allen Institute for AI, a flagship effort to build a truly open state-of-the-art language model (earning two Best Paper awards at ACL 2024 in the process). Iz has helped narrow the gap between open models and Big Tech’s proprietary models, contributing to projects like Longformer (a breakthrough model for long documents) and BLOOM (the first open multilingual LLM) that are downloaded millions of times a month. With a PhD in NLP from UT Austin and stints as an early ML engineer at Quora, Iz brings deep technical chops and a mission-driven ethos. He ensures Envive’s AI agents are not black boxes, but powerful systems grounded in cutting-edge research and built with transparency.
• Matthew Peters (Co-founder & Chief Architect) – The foundational model pioneer. Matt is the architect of Envive’s “LLM brain.” He actually invented one of the first foundational language models – ELMo, which in 2018 was the first general-purpose AI model to achieve state-of-the-art across a range of NLP tasks. (If you’re keeping score, ELMo is cited as one of the top 10 most foundational papers in AI history, and Matt won Best Paper at NAACL 2018 for it.) He was a Senior Research Scientist at AI2, contributing to projects that have over 33,000 citations. In other words, when it comes to large language models and AI architecture, Matt has been pushing the boundaries from the very beginning of the trend. At Envive, he’s scaling the platform’s AI capabilities to billions of interactions, with an eye on safety and control. Matt’s presence means Envive’s tech isn’t built on sand – it’s built on bedrock innovations he helped create.

Take a step back and you’ll see how extraordinary this team is. Envive’s four co-founders aren’t just ticking boxes on a org chart; they embody a fusion of talent you almost never see in one startup. We have: a product leader from a retail giant, a top-tier AI safety researcher, an open-source AI trailblazer, and a pioneer of modern NLP models – all working together. This is not the norm. Many AI companies out there have brilliant researchers but no retail experience, or savvy business folks but no deep AI innovations of their own. Envive has both, and that’s by design. It’s the blend of “research + applied ecommerce AI” that the industry has been missing, and it’s exactly what sets Envive apart.

Building a Different Breed of AI (Grounded, Connected, and High-Performing)

What does all this mean for someone shopping for an AI solution? It means Envive’s team is building a different kind of AI platform – one that’s grounded in real-world use from day one, not a science project or a repackaged chatbot. Envive specializes in connected AI agents for commerce: think of AI sales assistants that can genuinely understand customer needs, navigate complex product catalogs, and deliver results (conversions, revenue, satisfied customers) in ways earlier tools simply couldn’t. Because of the team’s experience, Envive’s agents are built for performance and trust in equal measure. The platform connects to real e-commerce data and systems (no “black box” hallucinations running loose) and is fine-tuned to handle the nuances of retail – from product discovery and recommendations to handling customer queries safely and accurately.

This isn’t just marketing fluff; the outcomes speak for themselves. Early adopters like Spanx and others have seen Envive’s AI drive millions in incremental revenue by turning product search into an intelligent conversation. And importantly, these agents operate with a level of reliability and brand alignment that gives companies peace of mind – a direct result of the safety-first, research-driven approach of Envive’s team.

The bottom line? In a sea of AI platforms, look at the crew steering the ship. Envive’s founding team proves that when you mix battle-tested industry veterans with AI pioneers, you get something greater than the sum of its parts. You get AI that’s not only innovative but practical and trustworthy. The team behind your AI platform determines how fast it learns, how well it performs, and whether it earns your trust. Envive’s team set out to build an AI platform they themselves would trust to run a business – and it shows in the product’s capabilities.

Ready to see the difference that experience makes? Take a closer look at Envive and the work this team is doing. You can learn more about their approach and see success stories of their AI in action – it might just change how you think about what AI can do when the right people are behind it.