Runable has raised $21 million in Series A funding to move its AI agent beyond creating websites, apps, and presentations and into customer acquisition for small businesses. The Bengaluru-based startup says its broader goal is to let an owner request a business outcome—such as gaining new customers—without separately configuring software for deployment, analytics, advertising, search optimization, and social media.

The all-equity round was co-led by Susquehanna Venture Capital and Nexus Venture Partners, with existing backers Together Fund and Array VC participating. Co-founder and CEO Umesh Kumar told TechCrunch that the investment values Runable at $65 million after the funding. The company, founded in 2025, has a 15-person team.

From AI creation to business growth

Runable began as an AI infrastructure company focused on browser technology for large-scale data scraping. According to Kumar, users increasingly asked the browser-based agent to produce slide decks and websites, prompting a shift toward a general-purpose AI agent.

The platform now lets users create websites, apps, presentations, and other content from natural-language instructions. Runable also manages some of the supporting infrastructure, including deployment and analytics. Its next product direction adds tools for ad campaigns, social media management, SEO, and improving a company’s visibility in AI chatbot results.

That positioning puts Runable between AI coding tools and marketing automation software. The startup is not presenting itself as a direct replacement for developer-focused products such as OpenAI’s Codex or Anthropic’s Claude Code. Instead, it is targeting nontechnical small-business owners who may need a functioning online operation rather than source code alone.

The competitive field is already crowded. Runable’s market includes AI companies such as Anthropic and OpenAI, coding platforms including Cursor, Lovable, and Replit, and general-purpose agents such as Manus and Genspark. Kumar identified the latter group as Runable’s closest competitors because they pursue similar users and workflows.

What the funding and usage claims show

Kumar said Runable reached a $2 million annualized revenue run rate within three weeks of launching payments in March. He did not disclose current revenue or the number of paying customers. The company says it has about 1.7 million registered users, with the United States, United Kingdom, and Japan among its largest markets; it also has users in Brazil.

Runable also reported more than 1 trillion tokens consumed during the past 90 days, with 60% to 70% of that usage coming from paying customers. These are company-reported adoption and usage figures, not independently audited results, and the available reporting does not establish how much revenue or business activity those users generate.

The startup acknowledged that it currently operates with negative gross margins, partly because it subsidizes AI usage. Runable uses a mix of external models and models it is developing. Kumar said the company expects lower inference costs to improve its economics and claimed it sees a path to delivering comparable inference quality at nearly one-tenth the cost. That cost projection remains a company expectation rather than a verified financial result.

A TechCrunch test illustrated the gap between generating a campaign and executing one. Runable created and deployed a website for a fictional coffee-subscription company and prepared an advertising campaign aimed at attracting 100 visitors with a $25 budget. It did not launch the campaign because an advertising account had not been connected.

Runable said advertising without a customer-connected account is currently possible for ads on ChatGPT through partnerships whose names it declined to disclose. That capability, described by the company as a “soft wedge,” was not independently detailed in the report.

The execution problem for AI agents

Runable’s central proposition is that an AI agent should handle the operational chain around a digital product, not stop when the website or application has been generated. That could be valuable for small businesses that lack technical staff and cannot afford to coordinate developers, agencies, analytics vendors, and advertising platforms.

But the advertising test points to a practical constraint: agents still require credentials, payment methods, permissions, and access to third-party systems before they can take consequential actions. This is not unique to Runable. TechCrunch reported similar limitations when testing Cursor, which prepared an ad campaign but required access to a Meta Ads account, a payment method, and an external deployment service.

For builders and enterprise buyers, the distinction matters. An agent that drafts assets can be evaluated like a productivity tool. An agent that spends money, changes campaigns, or represents a business needs stronger controls around approval, account access, budgets, attribution, and error recovery. Runable’s integrated infrastructure may reduce setup work, but it does not eliminate those governance requirements.

The business model also remains unresolved. High token usage can signal engagement, but it can simultaneously increase inference expense. Runable will need to show that customers will pay enough for completed business outcomes to cover model calls, infrastructure, support, and the costs of operating marketing integrations.

What to watch next

The clearest signals will be Runable’s conversion from registered users to paying customers and its ability to disclose revenue or retention without relying mainly on token consumption. Buyers should also watch whether the company can demonstrate campaigns that run end to end, rather than only producing websites, advertisements, or recommendations.

Other indicators include the rollout of its SEO, social, and AI-chatbot distribution features; the extent of its unnamed advertising partnerships; and whether its internally developed models reduce gross losses without lowering output quality. Japan’s expected growth as a leading market, alongside the United States and United Kingdom, will also test whether the product travels across different languages, search environments, and small-business needs.

Creati.ai perspective

Runable’s funding reflects a sharper direction in the agent market: the valuable unit may be a completed business workflow, not an isolated artifact such as code or a presentation. For small companies, combining creation, deployment, measurement, and distribution could be more useful than adding another standalone coding assistant.

The harder question is whether Runable can reliably cross the boundary from preparation to action. Its funding gives the company room to build those integrations, but the reported negative gross margins and dependence on external accounts show that the outcome-based promise is still a work in progress. Until agents can act safely, measure results, and sustain viable economics, “growing” a business will remain a more demanding claim than building one.