PEAR'S REQUEST FOR STARTUPS - PEAR VC

Pear’s Request for Startups Written by: Pear Team Published: March 7, 2026 in Perspectives We’re now accepting applications for PearX, our exclusive small-batch program for pre-seed companies. Our S26 cohort kicks off in July 2026 and runs for 12 weeks. We welcome founders at the idea stage as well as teams already gaining early traction to apply. Below are some of the ideas and opportunities we’re most excited to see founders tackle next. Plan mode for every knowledge worker By Shravan Reddy One of the most underappreciated design breakthroughs in AI tooling is a workflow change. Claude Code introduced “plan mode,” where the agent and developer iterate on an approach before any code gets written. This simple forcing function — make a plan, pressure-test it together, then execute — dramatically improves output quality because it surfaces ambiguities, bad assumptions, and missing context before they compound into wasted work. Meanwhile, the chatbots most knowledge workers use today are optimized for agreeability. Ask Claude Chat or ChatGPT to write a sales deck, build a financial model, or draft a marketing brief, and it will happily produce something that looks polished but is built on a stack of unchallenged assumptions about your audience, your constraints, and your goals. The result is output that’s 60-70% of the way there: impressive enough to feel useful, but not good enough to actually ship without heavy rework. We believe there’s a large opportunity to build vertical AI tools that bring the plan-then-execute paradigm to domains like sales, marketing, and finance. In practice, this means an agent that behaves less like an eager intern and more like a sharp collaborator: one that asks clarifying questions, flags where it’s uncertain, proposes a structured plan of attack, and only moves to execution once the human has weighed in on the approach. For a sales team, this might look like the agent and rep co-developing an account strategy before drafting outreach. For a finance team, it could mean aligning on assumptions and edge cases before building a forecast. This is a wedge that could evolve into something much bigger. A tool that earns trust in the planning phase naturally becomes the system of record for how decisions get made, and eventually the agent that executes on them autonomously. If you’re building the agentic workflow layer for a specific vertical, we want to talk. • Financial-grade agent infrastructure By Ryan Sells AI agents are already useful. But they’re useful in the way a smart advisor with no signing authority is useful. They can research, summarize, and recommend, but the moment you need an agent to actually move money, execute a contract, or make a credit decision, the entire stack breaks down. Today’s agent infrastructure was built for a world of reversible, low-stakes actions: drafting an email, writing code, searching the web. But financial systems demand auditability, policy enforcement, liability attribution, and the ability to handle irreversible actions where mistakes aren’t fixable with an undo button. The gap between “ChatGPT-style agent that suggests things” and “autonomous agent that can execute in production financial systems” is enormous. We’re looking for founders building the trust, policy, and execution layer that makes AI agents safe to deploy in financial contexts. This could span identity and authorization frameworks that let agents act on behalf of humans with scoped, revocable permissions; policy engines that enforce compliance constraints and spending limits before an action is taken, not after; audit and explainability infrastructure that satisfies regulators and counterparties; or composable transaction primitives that let agents interact with banks, payment rails, and financial APIs with the same reliability we expect from production software. The design space is rich because every financial vertical — payments, lending, treasury, insurance, procurement — has its own set of regulatory and operational requirements, but the underlying need is shared: a common infrastructure layer that makes agents both capable and trustworthy. The companies that build the trust layer for agentic finance will sit at the center of an enormous amount of economic activity as agents move from advisors to actors. The teams that solve this will define how AI participates in the real economy. If you’re building here, we’d love to hear from you. • Verified consumer health marketplaces By Warren Shaeffer Consumer health is flooded with products that promise results but rarely prove them. Supplements, protocols, devices, and programs are marketed with glossy claims, thin evidence, and little