Aniket Deosthali

The focus at Envive AI is on scaling what the next generation of online shopping looks like. The foundation is already in place: AI agents that guide shoppers the way a skilled sales associate would. The work now is expanding that across more brands, more use cases, and more complex customer journeys — making the experience more natural, more helpful, and more effective at driving real business outcomes.

That work happens in close collaboration with partners — understanding where traditional ecommerce falls short, and how Envive’s agents can show up across more of the journey. Not just on-site, but anywhere shopper intent is happening. That includes extending agents into earlier funnel touchpoints, like traffic coming in from ads, where there’s often a disconnect between intent and experience.

The broader question driving the work: how does AI become an active participant in shopping — not just something customers interact with when they have a question, but something that helps guide decisions from discovery all the way through purchase.

Envive AI operates closest to AI systems that work directly in the shopping journey — sales agents that guide product discovery, search agents that understand natural language, and customer experience agents that handle support in real time. These systems are connected, pulling signals from across the funnel and continuously learning from real customer behavior to improve outcomes like conversion, retention, and discoverability.

The focus is on workflows where agents aren’t siloed — they work together and improve through reinforcement learning, using live feedback loops to understand what’s actually driving the right outcomes and adjusting in real time. Over time, the system becomes more coordinated, more context-aware, and more effective, rather than just a collection of features.

The use cases that matter most are the ones where AI is directly tied to outcomes, not just information retrieval — helping a shopper make a decision faster, increasing conversion, reducing drop-off, and continuously learning what works to drive those results.

For shoppers, finding the right product often means navigating endless options with very little guidance. For merchants, understanding what customers actually want requires stitching together insights across multiple tools and systems — making it hard to act on intent in real time.

Envive AI’s agents close that gap by guiding customers through the journey — helping them discover, evaluate, and decide faster — while capturing that intent and learning from what customers actually choose to buy. That feedback loop is what allows the system to continuously improve and align more closely with real behavior.

The constraint is that all of this has to happen in a way that is accurate, brand-safe, and fully controllable, while operating in real time and driving measurable business outcomes — not just engagement.

Getting AI to work in the real world has a lot less to do with the model itself, and a lot more to do with how well it fits into the existing system around it. The biggest unlock has been treating AI as part of the operating layer, not a standalone feature — which means it has to plug into real data, real workflows, and real business logic: product catalogs, inventory, merchandising rules, brand voice.

Control and trust matter more than raw capability. Teams need to understand what the AI is doing, be able to shape its behavior, and rely on it to be consistent — especially in customer-facing environments.

And most importantly: success comes down to iteration. The systems that actually work are the ones that are constantly learning from live interactions and improving over time, not the ones that are designed once and left alone.

The clearest operational impact comes when AI is embedded directly into decision moments — not just supporting them. When it’s actively helping a shopper choose between products, narrowing options, or removing hesitation in real time, you start to see meaningful shifts in conversion, speed to purchase, and return rates.

At Envive AI, that impact comes from systems that are continuously learning from live behavior, not just static data, and adjusting how they guide each customer accordingly. That’s where AI starts to feel less like a feature and more like a core part of how the business operates.

Where reality has fallen short is in the expectation that AI alone is the solution. In practice, the hard part isn’t generating responses — it’s making those responses reliable, context-aware, and actually effective at helping convert the customer. A lot of early implementations look impressive but break down when they have to operate at scale, in real environments, with real customers.

Today, most production AI is still scoped to tasks — generating content, answering questions, or supporting decisions. But the transition is already underway toward systems that are measured and optimized against real business metrics: conversion, revenue, retention.

At Envive AI, that shows up in how agents are evolving from reactive interfaces into systems that continuously learn from live interactions and adjust in real time to improve performance. As that becomes more common, AI will be embedded much more deeply into core operations — with clearer accountability, tighter constraints, and a much stronger link between what the system does and the results it drives.