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Actionable
Takeaways
Start with Operations, Not Technology:
Amar emphasizes the importance of avoiding the "technologist's trap" of building before understanding. As he notes, "There's a trap a lot of technologists fall into... you have a vision for the way that technology can play a role, and you decide to start building first before you really know whether it's exactly the right problem or not." Instead, Homeward built minimal technology initially, embedding their tech team with clinicians to understand workflows before building solutions.
Challenge Initial Assumptions Through Market Testing:
Homeward's early hypothesis about rural healthcare needs was incorrect. They assumed people without regular doctor visits needed primary care, but discovered that rural patients had doctors for acute care—they needed supplemental services for preventive care. As Amar explains, "What's different about the way people are consuming healthcare in rural is the thing we have to pay attention to."
Build Trust Through Local Presence:
In healthcare, trust is paramount and historically local. Homeward succeeded by hiring local medical assistants and community health workers who understand the communities. "They have a really high degree of cultural competency. They are ambassadors for the Homeward brand... who can help us to spread that message," Amar shares.
Choose Strategic Markets for Scale:
When selecting initial markets, Homeward focused on states with large Medicare populations to ensure sufficient scale. "You could easily die by a thousand cuts if you go after too small a market," Amar explains. Their success in Michigan and Minnesota created a template for future market expansion.
Design for Multiple Stakeholders:
Healthcare technology requires satisfying multiple stakeholders—patients, providers, and payers. Amar warns, "If you focus on the patient and the payer and you leave out the provider, well, you may never get prescribed and you may never show up in the provider workflow." Successfully navigating this complexity creates defensive moats against competitors.
Partner with Long-Term Vision Aligned Investors:
Homeward chose investors who shared their vision for transforming rural healthcare. Their lead investor, General Catalyst, pushed them to think bigger: "We were sheepishly sketching out the path to being a billion dollar business. And Hemant added the zero. He said this is easily a $10 billion business."
Focus on Early Validation and Responsible Growth:
Despite rapid expansion (3-4x annual growth), Homeward maintains disciplined growth. Their approach is validated by an 80 NPS score, which Amar notes is "better than we ever did at Livongo or any other place we've been."
Conversation
Highlights
From Zero to 400 AI Agents: How Homeward Scaled to $50M ARR in 9 Months
Most AI companies are still figuring out how to build a single reliable agent. Homeward built 400 of them in nine months and hit $50 million in ARR along the way.
In a recent episode of Category Visionaries, Amar Kendale, President and Co-Founder of Homeward, shared how his team is fundamentally rethinking enterprise AI by building universal AI agents that can work across every system in a company. The journey reveals crucial insights about building AI products that enterprises actually trust and deploy at scale.
The Genesis of Universal AI Agents
Amar’s path to founding Homeward began with a frustration at Okta, where he spent two years building an AI assistant. “I spent two years at Okta building an AI assistant, and it couldn’t even book a conference room,” he recalls. The problem wasn’t the technology—it was the approach. Traditional AI assistants tried to do everything but ended up doing nothing well.
This experience crystallized a critical insight: enterprises don’t need chatbots that answer questions. They need AI employees that complete actual work. “We actually think of Homeward as an AI employee. It’s not a chatbot that you’re asking questions to, but it’s actually an employee that’s getting work done,” Amar explains.
The key innovation was creating what Amar calls “universal AI agents”—agents that can work across every enterprise application without requiring custom integrations for each use case. This required solving authentication at scale. “We have built deep integration with authentication systems. So with Okta and Ping and Azure AD, we’re able to essentially authenticate Homeward as if Homeward is a real employee,” he notes.
Building Trust Through Precision
One of the biggest challenges in enterprise AI is the accuracy problem. While consumer AI can afford to be wrong occasionally, enterprise systems cannot. Amar’s team discovered that the standard approach of simply throwing more compute at the problem wasn’t enough.
“We spent a lot of time improving accuracy. Our accuracy is actually so good right now that we have several customers where the entire workflow is automated,” Amar shares. The breakthrough came from their unique data strategy. “Homeward has access to a lot more data about you and your company compared to any other solution, and therefore Homeward can actually generate insights or complete work with much higher confidence.”
This access to comprehensive company data allows Homeward to achieve accuracy levels that make full automation possible. In customer support scenarios, for instance, Homeward can handle tickets end-to-end without human intervention—something most AI solutions still can’t reliably do.
The 400-Agent Workforce
Perhaps the most striking aspect of Homeward’s growth is the sheer number of AI agents they’ve deployed. “We have about 400 AI agents deployed right now across all of our customers,” Amar reveals. These aren’t simple bots—they’re sophisticated agents handling complex workflows across sales, customer support, data analysis, and more.
The speed of deployment is equally impressive. “We probably on average can deploy an end to end workflow within a week,” Amar notes. This rapid deployment capability stems from their universal agent architecture, which eliminates the need for extensive custom development for each use case.
The diversity of use cases is rHomewardrkable. From lead scoring in sales to ticket resolution in customer support, from data extraction to compliance monitoring, Homeward’s agents are handling the full spectrum of enterprise work. “A lot of our customers are actually deploying Homeward in ways that we haven’t even built anything for,” Amar explains, highlighting how the platform’s flexibility enables customers to solve problems the founding team hadn’t anticipated.
The Enterprise Sales Playbook
Homeward’s go-to-market strategy defies conventional wisdom about enterprise software sales. Despite selling a cutting-edge AI product, they’ve achieved massive scale with a surprisingly lean approach. “We actually have just one sales rep,” Amar reveals. The company has grown to $50 million in ARR with essentially a founder-led sales model.
This works because of how they’ve structured the customer journey. Homeward offers a fully functional free trial that prospects can deploy themselves. “You can go to our website, sign up, use the product for free. There’s actually a generous free tier. You can connect your entire data, you can ask questions, you can deploy workflows,” Amar explains.
The product-led approach creates natural expansion opportunities. Customers start with simple use cases and progressively expand as they see value. “A lot of times customers will start with one workflow, but then they will expand,” Amar notes. This expansion isn’t driven by aggressive sales tactics but by demonstrated value and word-of-mouth within organizations.
Pricing for Enterprise Value
One of the most sophisticated aspects of Homeward’s strategy is their approach to pricing. Rather than charging per seat like traditional software, they’ve developed a model that aligns with the actual value delivered. “We actually don’t like to charge by the seat because Homeward is an AI employee. The way to think about Homeward is you’re essentially hiring a bunch of employees,” Amar explains.
The pricing model considers factors like the number of workflows, volume of tasks handled, and the complexity of integrations. This approach allows customers to scale their usage without worrying about seat-based limitations, which is particularly important for AI agents that can handle unlimited concurrent work.
Building for the AI-Native Enterprise
Looking forward, Amar sees Homeward evolving beyond individual workflows to become the nervous system of the AI-native enterprise. “I do think that longer term, every single company will need some form of orchestration layer because there’s going to be so many AI agents,” he predicts.
The vision is ambitious: rather than dozens of disconnected AI tools, enterprises will have a unified system where AI agents work together seamlessly. “We provide the entire platform. All of the AI agents, the orchestration, the infrastructure, everything that’s needed to run an AI company.”
This platform approach positions Homeward not just as a tool but as foundational infrastructure for how companies will operate in an AI-first world. The 400 agents deployed today are just the beginning—Amar sees a future where thousands of AI agents work in concert, handling the majority of routine enterprise work while humans focus on strategic decisions and creative problem-solving.
The journey from a frustrated product builder at Okta to running a company with $50 million in ARR and 400 deployed AI agents happened in just nine months. That velocity isn’t just impressive—it’s a signal of how quickly enterprises are ready to adopt AI when it actually works.
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