Artificial intelligence is moving beyond answering questions and generating content. Increasingly, startups are developing AI agents that can take action, manage customer relationships, coordinate workflows, and help businesses operate with fewer manual steps.
This shift is creating opportunities for founders who understand where traditional business software falls short. From customer support and customer success to proactive e-commerce engagement and company-wide automation, these five entrepreneurs are building AI-powered tools designed to turn everyday business processes into more intelligent, scalable operations.
Dawson Chen — Co-founder and CEO of Letterbook
Dawson Chen‘s experience building consumer AI products helped him identify a different opportunity: improving how startups handle customer support. After working on Martin, an AI personal assistant that attracted a substantial user base, Chen encountered the operational challenges that came with growth. Support requests involving refunds, subscriptions, and account issues required switching between multiple tools, inspiring the development of Letterbook, an AI-native customer support platform.
Letterbook connects customer support conversations with business systems such as payment platforms, databases, and knowledge bases, allowing its AI to prepare responses and recommend or execute resolutions. The goal is to reduce the repetitive work involved in handling customer inquiries without forcing growing companies to adopt complicated enterprise support infrastructure. Chen’s approach reflects a broader opportunity in AI software: building products around the actual work businesses need to complete rather than simply adding an AI chatbot to existing tools.
Ilya Valmianski — Co-founder and CEO of Signals
Ilya Valmianski brings experience in conversational AI and healthcare technology to his work at Signals. Before founding the company, he helped develop AI-powered patient intake systems at Kaiser Permanente, worked on conversational medical AI at Curai Health, and co-founded MDandMe. That background in building systems that communicate with people informs his latest venture, which applies proactive, personalized conversations to e-commerce customer engagement.
Signals is developing a revenue channel that helps direct-to-consumer brands reconnect with customers through iMessage after a purchase. Rather than waiting for shoppers to initiate contact, the platform enables two-way conversations intended to improve the post-purchase experience, encourage repeat purchases, and address potential returns. The company’s thesis is that customer engagement can create measurable business value when it is timely and personal, turning conversations into an opportunity to build loyalty and generate additional revenue.
Deepak Pillai — Founder and CEO of Centive AI
Deepak Pillai is building Centive AI around a challenge facing many growing businesses: delivering consistent customer success without expanding teams at the same pace as their customer base. Traditional customer success models often rely on dedicated employees to onboard accounts, encourage product adoption, monitor renewals, and identify expansion opportunities. That approach can become difficult to sustain when businesses serve large numbers of customers with varying needs and levels of engagement. Centive AI positions autonomous agents as a way to address this operational gap.
Centive AI describes its platform as an autonomous customer success system that manages onboarding, adoption, renewals, and account expansion. Its agents are designed to deliver personalized engagement at scale, helping companies support customers who might otherwise receive limited attention because of resource constraints. The opportunity extends beyond reducing manual work: by making customer success more proactive and consistent, the platform aims to help businesses strengthen relationships and uncover growth opportunities across their customer base.
Chyngyz Dzhumanazarov — Co-founder and CEO of Kodif
Chyngyz Dzhumanazarov co-founded Kodif with the conviction that customer support technology should do more than direct people to help articles or generate automated replies. A repeat entrepreneur with experience in private equity and logistics, he joined forces with co-founder and CTO Norm Usenkanov, who previously built customer support automation at Uber. Their combined experience shaped a product designed to handle complex support interactions while giving businesses more control over how customer issues are resolved.
Kodif develops AI agents for e-commerce customer experience, covering tasks such as processing refunds, managing returns, handling subscription cancellations, and helping customers find appropriate products. Its platform integrates with existing business systems so agents can check customer information, apply company policies, and take action rather than simply recommend a next step. Dzhumanazarov’s broader vision is to make customer support a source of business intelligence and revenue, using customer conversations to improve retention and reveal opportunities to serve shoppers more effectively.
Adhit Sankaran — Co-founder and CEO of Janet AI
Adhit Sankaran is building Janet AI to help businesses deploy AI agents that can perform work across their existing tools and workflows. His background includes founding engineering and generative AI work at an earlier startup, as well as internships at Google. With Janet AI, he is applying that technical experience to a broader operational problem: enabling AI systems to retain organizational context and carry out recurring tasks without requiring employees to repeatedly provide the same information.
Janet AI allows companies to configure agents around specific workflows using natural-language instructions, connected applications, and defined permissions. Its system is designed to remember information across tools and retrieve relevant context when needed, enabling agents to assist with work such as account research, documentation, and other business operations. The emphasis on persistent memory and controlled execution addresses a key challenge in enterprise AI: creating agents that can work reliably over time while allowing employees to review important actions and retain control over decisions.
Turning AI From Assistance Into Action
These founders are approaching AI adoption from different directions, but their products share a common ambition: enabling software to complete meaningful work rather than merely provide information. Whether the task involves resolving a customer issue, nurturing a commercial relationship, supporting an account, or coordinating an internal workflow, each company is exploring how AI agents can take on more responsibility within business operations.
As these systems mature, their value will depend on more than the sophistication of their underlying models. Reliable integrations, access to the right context, clearly defined permissions, and measurable outcomes will determine whether AI agents become trusted parts of everyday work. For businesses, the next phase of AI adoption may be defined by how effectively these tools translate intelligence into action.
