“We don’t just ship a model and walk away. We put it in front of users and then use those real interactions to make the model better.” — Matt Peters, Co-Founder & Chief Architect, Envive

Meet Matt Peters, Chief Architect 

When you work with Envive, you’re working with one of the leading voices in natural language processing. Matt Peters, Envive’s Chief Architect and co-founder, co-created ELMo, the first widely adopted contextual word representation model. His research has been cited more than 30,000 times and earned awards including Best Paper at NAACL. Before Envive, Matt led research at the Allen Institute for AI, and today he focuses on making AI systems not just powerful, but also reliable and controllable. That same rigor shapes the way Envive builds and improves your customer-facing models. Matt leads Envive’s technical team alongside Sameer Singh (former Head of AI Safety at Google Deepmind) and Iz Beltagy (former Lead Researcher at the Allen Institute for AI), adding deep expertise in brand safety and model fine-tuning.

Starting with a model that understands your brand

From day one, Envive trains a model specifically for your catalog, your brand voice, and your customer journey. This isn’t about dropping in a generic chatbot. Instead, Envive deeply integrates with your product data, promotions, pricing, policies, and FAQs so your agent has accurate, real-time information about your store.

As Matt puts it, “We do deep integration into the merchant’s own systems, making sure the model has real-time product information and is speaking in the brand’s own voice.” The result is a conversational agent that feels like a true extension of your team.

The real differentiator: continuous tuning with live customer data

Envive is the only team providing each merchant partner with a custom model, trained specifically for each merchant’s unique qualities. Other tools on the market are built on ChatGPT. Each merchant doesn’t get any customization for their business, and the sales agent model never gets better over time. When Envive trains a custom model for each of our merchant partners, the model itself gets optimized for customer conversion while remaining within brand and safety guidelines of that partner. In other words, while other sales agents are equivalent to pulling a stranger off the street, handing them your product catalog, and putting them on the retail sales floor, Envive puts that new sales associate through thousands and thousands of simulated customer interactions before the sales associate (custom model) interacts with customers.

Once your model goes live, the real advantage begins. Envive doesn’t let your agent stagnate—it improves by learning from how your customers actually use it.

Matt explains: “We look at what types of interactions lead to higher conversion and use that to guide further tuning. The idea is that the next conversation should be more effective than the last.”

Envive captures signals from live interactions—what customers click, which answers drive confidence, what ends up in the cart—and uses them to refine your model. Over time, your agent becomes sharper, more persuasive, and more aligned with your shoppers.

Guardrails that protect your voice

Even as the model adapts, your brand voice and policies never drift. From the start, Envive encodes your tone, compliance rules, and safety boundaries so the agent always speaks in a way you can trust.

“We’re continuing to improve the model,” Matt says, “but we’re doing it within the merchant’s guardrails. The safety layer is always present.” 

That means you get the benefit of ongoing learning without losing control.

Moreover, merchants using a combination of Envive’s agents (ex., Sales + Search + SEO/GEO agents) get the benefit of each of the agents feeding in new data to improve the performance of all the others.

Measuring progress you can point to

To make sure improvements are meaningful, Envive sets a baseline conversion rate during your trial period. Every update is measured against that benchmark, so you can see exactly how the agent is moving the needle.

As Matt notes: “We do an initial trial period where we check our conversion rate, then measure improvements against that starting point.”

With this process, you’re not just told the model is improving—you can show it with data.

Why this matters for you

Ecommerce leaders are tasked with driving growth, increasing conversion, and protecting their brand. Envive is built to help you do all three:

• Relevance from day one. Your agent is trained on your catalog, your policies, and your tone of voice.
  
  
• Ongoing improvement. Every customer interaction becomes a training signal, so your model gets better with time.
  
  
• Safety and control. Your voice and compliance guardrails stay intact through every iteration.
  
  

Accountable results. You measure improvements against a baseline you set at launch.

The bottom line: Envive gives you a conversational agent that isn’t just accurate on day one—it grows more effective with every interaction. It’s an AI system shaped by your products, your brand, and most importantly, your customers.

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“Rather than relying only on curated datasets before launch, we treat live engagement as the most truthful training signal. If a particular phrasing consistently helps your shoppers, we want the model to learn from that.” — Matt Peters, Co-Founder & Chief Architect, Envive

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