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Zoca raises $6M to unlock the future of neighbourhood businesses with AI — TFN

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2 months ago

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In a digital economy where enterprise adoption of AI is accelerating, local service businesses often find themselves left behind, struggling with outdated marketing tools and ineffective strategies. But Tempe-based startup Zoca is flipping that script. 

The $6 million round was led by Accel and joined by GTMfund, Elevation Capital, and Better Capital. This brings Zoca’s total funding to $8.8 million, following an earlier pre-seed round. The company is building an AI-first growth platform to help hyperlocal service businesses get discovered, booked, and rebooked, promising one outcome: more paying clients, guaranteed. 

A founding story rooted in real needs

Zoca’s origin story is as grounded as its mission. Founders Ashish Verma and Robin Chauhan, friends and classmates from IIT Kharagpur, realised a systemic failure: local service providers were not struggling due to a lack of talent but due to a lack of intelligent infrastructure. The rise of independent beauty and wellness professionals further highlighted this disconnect, as more individuals ventured away from salon chains but were left unsupported by legacy marketing systems.

Their insight led to a product designed specifically for the everyday needs of small business owners. This end-to-end system replaces what previously required multiple software tools, marketing consultants, and countless hours of manual effort. Both founders bring deep technical expertise: Verma previously led AI security initiatives at a Fortune 500 company, while Chauhan built scalable data platforms at two unicorn startups.

Built for the underserved

Local service businesses, such as salons, massage therapists, and wellness providers, think they are masters of their trade but frequently lack the marketing resources needed to grow. Zoca recognises this gap. Instead of offering another complicated tool or pricey agency alternative, it delivers a fully automated, AI-driven platform that manages the entire client acquisition process. From identifying demand to booking appointments and re-engaging past customers, Zoca handles it.

Since launching in 2024, the platform has already onboarded over 1,000 local businesses, generated over 120,000 bookings, and driven over $10 million in revenue without requiring owners to lift a finger.

The AI agent advantage

Zoca’s use of AI agents to automate every layer of the growth funnel sets it apart. These agents don’t just offer insights, but also take action. They analyse hyperlocal demand trends, optimise discovery efforts, convert leads into confirmed appointments, and maintain client engagement with personalised follow-ups.

For example, Zoca’s proprietary engine picks up on nuanced neighbourhood-level patterns, identifying that demand for services like “glycolic facials” or “lymphatic drainage massages” can vary significantly even within a few miles. The AI then adjusts marketing strategies to match local search behaviour, ensuring relevance and better conversion rates.

Zoca’s AI also dynamically adjusts service pricing based on real-time demand, helping businesses maximise revenue during peak hours. The platform integrates seamlessly with tools like Google Business Profile, Fresha, and Vagaro, so owners don’t have to overhaul their existing workflows.

Zoca isn’t stopping at lead generation. The platform recently rolled out AI agents for managing paid advertising and social media, automating the entire marketing value chain. These tools enable local businesses to compete on the same playing field as large enterprises without the burden of complex campaign management or costly third-party services.

With features like mobile-first websites, 24/7 conversational AI for lead response, and personalised SMS/email retention campaigns, Zoca provides the equivalent of a full-time marketing team at a fraction of the cost. A new “Social Agent” feature even suggests trending TikTok and Instagram content ideas tailored to local audiences.

Looking ahead

Zoca’s roadmap includes expanding its AI agent ecosystem, integrating deeper with other platforms, and reaching into new verticals beyond beauty and wellness. Whether it’s dental practices, pet groomers, or personal trainers, the vision is the same: let local professionals focus on their craft while AI works invisibly in the background to drive growth.

Rather than flashy dashboards and overwhelming analytics, Zoca’s model is rooted in simplicity and results. The future it imagines is one where small business success doesn’t require digital fluency, just the decision to turn on a platform built to work for them, not the other way around.

The company is also exploring predictive staffing tools to help owners optimise employee schedules based on historical and forecasted demand, reducing idle time and boosting profitability.

In a world where AI is often synonymous with complexity, Zoca is proving that its most powerful use might be in making things easier, not harder, for the businesses that keep our neighbourhoods vibrant.

“We saw a fundamental disconnect,” said Ashish Verma, Zoca’s co-founder and CEO. “These entrepreneurs are selling time, not products. Every empty chair is revenue they’ll never recover. The real challenge isn’t just visibility anymore—it’s everything that comes after it. Being found is one part, but converting leads, filling schedules, and retaining clients is where businesses either grow or stall. What makes this space unique is the business model itself. These service professionals aren’t focused on selling more units—they’re maximising their available time. So the ROI on every lead, every appointment, and every repeat visit directly impacts their bottom line. That’s why we built a system that addresses the entire customer journey, not just one piece of it.”

Gail Aungst, Co-owner of Ohana Sun Tanning, commented: “Google search is really important and so key words are critical for us. One of them is “tanning near me”. Zoca’s helped us stay on top and win. They are totally helping us with our rankings”. 

Latasha Seawood, Owner of Slay by Vashae, said: “The first 30 days of using Zoca, my business went from maybe 3-4 people a day, into a queue. I was forced to turn people away. No lie. It was like a 30 day turnaround. If you search “sew-in” in your area, I pop up. And that’s because of Zoca.”

“Zoca is driving business outcomes for the underserved $750B local services market: said Manasi Shah, Partner at Accel. Ashish and the team have the highest customer obsession with a deep understanding of AI in automating a significant number of use cases applicable to the local services market. Growth AI agents are just the beginning, Zoca will create an agent-led OS for every hyperlocal business to achieve full potential.”

Paul Irving, Partner at GTMfund, added: “These businesses are the cornerstones of their communities, but most are being left behind in a digital and AI-first world. Zoca allows them to focus on what matters most: delivering an exceptional service to their customers. The growth, the customer retention and engagement – all on auto-pilot with Zoca’s AI platform. It’s a total game changer for these local businesses, and we couldn’t be more excited to support the Zoca team on their mission.”

Poorvi Vijay, Principal, Elevation Capital, said: “Zoca arms every neighbourhood business with a 24/7 AI growth engine. Ashish and Robin’s passion and unique insights to serve this underserved market and help them transform with AI is truly outstanding. We are thrilled to back them as they build the operating system for hyperlocal services worldwide.”





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Funding & Business

Hunter Point CEO on the PE Investor Pivot

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13 minutes ago

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July 7, 2025

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Last month, Hunter Point Capital agreed to acquire a 16% stake in Equitix, an international infrastructure investor and fund manager, at a valuation near $2 billion. Since the deal, Equitix’s portfolio has grown to nearly $16 billion in assets under management. CEO and co-Founder Avi Kalichstein joined Wall Street Beat on Bloomberg Open Interest to talk about the partnership. (Corrected guest name and title) (Source: Bloomberg)



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How Capital One built production multi-agent AI workflows to power enterprise use cases

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35 minutes ago

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July 7, 2025

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How do you balance risk management and safety with innovation in agentic systems — and how do you grapple with core considerations around data and model selection? In this VB Transform session, Milind Naphade, SVP, technology, of AI Foundations at Capital One, offered best practices and lessons learned from real-world experiments and applications for deploying and scaling an agentic workflow.

Capital One, committed to staying at the forefront of emerging technologies, recently launched a production-grade, state-of-the-art multi-agent AI system to enhance the car-buying experience. In this system, multiple AI agents work together to not only provide information to the car buyer, but to take specific actions based on the customer’s preferences and needs. For example, one agent communicates with the customer. Another creates an action plan based on business rules and the tools it is allowed to use. A third agent evaluates the accuracy of the first two, and a fourth agent explains and validates the action plan with the user. With over 100 million customers using a wide range of other potential Capital One use case applications, the agentic system is built for scale and complexity.

“When we think of improving the customer experience, delighting the customer, we think of, what are the ways in which that can happen?” Naphade said. “Whether you’re opening an account or you want to know your balance or you’re trying to make a reservation to test a vehicle, there are a bunch of things that customers want to do. At the heart of this, very simply, how do you understand what the customer wants? How do you understand the fulfillment mechanisms at your disposal? How do you bring all the rigors of a regulated entity like Capital One, all the policies, all the business rules, all the constraints, regulatory and otherwise?”

Agentic AI was clearly the next step, he said, for internal as well as customer-facing use cases.

Designing an agentic workflow

Financial institutions have particularly stringent requirements when designing any workflow that supports customer journeys. And Capital One’s applications include a number of complex processes as customers raise issues and queries leveraging conversational tools. These two factors made the design process especially complex, requiring a holistic view of the entire journey — including how both customers and human agents respond, react, and reason at every step.

“When we looked at how humans do reasoning, we were struck by a few salient facts,” Naphade said. “We saw that if we designed it using multiple logical agents, we would be able to mimic human reasoning quite well. But then you ask yourself, what exactly do the different agents do? Why do you have four? Why not three? Why not 20?”

They studied customer experiences in the historic data: where those conversations go right, where they go wrong, how long they should take and other salient facts. They learned that it often takes multiple turns of conversation with an agent to understand what the customer wants, and any agentic workflow needs to plan for that, but also be completely grounded in an organization’s systems, available tools, APIs, and organizational policy guardrails.

“The main breakthrough for us was realizing that this had to be dynamic and iterative,” Naphade said. “If you look at how a lot of people are using LLMs, they’re slapping the LLMs as a front end to the same mechanism that used to exist. They’re just using LLMs for classification of intent. But we realized from the beginning that that was not scalable.”

Taking cues from existing workflows

Based on their intuition of how human agents reason while responding to customers, researchers at Capital One developed  a framework in which  a team of expert AI agents, each with different expertise, come together and solve a problem.

Additionally, Capital One incorporated robust risk frameworks into the development of the agentic system. As a regulated institution, Naphade noted that in addition to its range of internal risk mitigation protocols and frameworks,”Within Capital One, to manage risk, other entities that are independent observe you, evaluate you, question you, audit you,” Naphade said. “We thought that was a good idea for us, to have an AI agent whose entire job was to evaluate what the first two agents do based on Capital One policies and rules.”

The evaluator determines whether the earlier agents were successful, and if not, rejects the plan and requests the planning agent to correct its results based on its judgement of where the problem was. This happens in an iterative process until the appropriate plan is reached. It’s also proven to be a huge boon to the company’s agentic AI approach.

“The evaluator agent is … where we bring a world model. That’s where we simulate what happens if a series of actions were to be actually executed. That kind of rigor, which we need because we are a regulated enterprise – I think that’s actually putting us on a great sustainable and robust trajectory. I expect a lot of enterprises will eventually go to that point.”

The technical challenges of agentic AI

Agentic systems need to work with fulfillment systems across the organization, all with a variety of permissions. Invoking tools and APIs within a variety of contexts while maintaining high accuracy was also challenging — from disambiguating user intent to generating and executing a reliable plan.

“We have multiple iterations of experimentation, testing, evaluation, human-in-the-loop, all the right guardrails that need to happen before we can actually come into the market with something like this,” Naphade said. “But one of the biggest challenges was we didn’t have any precedent. We couldn’t go and say, oh, somebody else did it this way. How did that work out? There was that element of novelty. We were doing it for the first time.”

Model selection and partnering with NVIDIA

In terms of models, Capital One is keenly tracking academic and industry research, presenting at conferences and staying abreast of what’s state of the art. In the present use case, they used open-weights models, rather than closed, because that allowed them significant customization. That’s critical to them, Naphade asserts, because competitive advantage in AI strategy relies on proprietary data.

In the technology stack itself, they use a combination of tools, including in-house technology, open-source tool chains, and NVIDIA inference stack. Working closely with NVIDIA has helped Capital One get the performance they need, and collaborate on industry-specific  opportunities in NVIDIA’s library, and prioritize features for the Triton server and their TensoRT LLM.

Agentic AI: Looking ahead

Capital One continues to deploy, scale, and refine AI agents across their business. Their first multi-agentic workflow was Chat Concierge, deployed through the company’s auto business. It was designed to support both auto dealers and customers with the car-buying process.  And with rich customer data, dealers are identifying serious leads, which has improved their customer engagement metrics significantly — up to 55% in some cases.

“They’re able to generate much better serious leads through this natural, easier, 24/7 agent working for them,” Naphade said. “We’d like to bring this capability to [more] of our customer-facing engagements. But we want to do it in a well-managed way. It’s a journey.”



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Houthis Say They Hit Red Sea Ship in First Attack This Year

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1 hour ago

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July 7, 2025

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Yemen’s Houthis have claimed responsibility for an attack on a ship sailing through the Red Sea on Sunday, in their first strike on merchant shipping since December.



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