Founder Fridays: Clarity drives progress with Bo Hu, Bevel and Mudit Goyal, Notion

Bevel is built to close the “data-to-business value gap,” where companies collect and store massive amounts of information but still can’t turn it into usable dashboards, insights, or models for weeks—or even years. The core complaint is operational: data teams face heavy, centralized control processes, while business teams wait too long for answers. Bo Hu frames the problem as a persistent chasm between business value delivery and technical execution, especially in data work, and says Bevel’s mission is to deliver end-user value at the speed of business decisions.

Bevel’s approach differs from traditional BI tools by targeting the entire data lifecycle rather than just making dashboards easier. The product design starts by removing “macro-level steps” that typically slow delivery—so instead of piecemeal work like cleaning, SQL writing, and assembling reports, the system aims to produce end-to-end outputs instantly (for example, generating a full funnel report for “t-shirts sold in the last 60 days”). The second pillar is measurable acceleration: Bevel uses “time to value” (TTV) as a design metric and aims to cut it by 75% or more for key workflows. The third pillar shifts the ideal customer away from technical teams and toward business end users—growth, product, and revenue teams—so they can act directly with sophisticated analytics without needing years of data expertise.

Hu’s founder story centers less on ambition and more on accumulated frustration. After a decade working inside fast-growing organizations—driving change without solving the business/technical gap—he says starting Bevel became a way to relieve the same pain he kept seeing elsewhere. Early on, the most energizing and terrifying realization was that founders are “on your own,” with fewer formal guardrails like HR or legal teams. The safety net becomes different: advisors, other founders, and a network that provides guidance rather than structure.

What keeps him up at night is speed in an AI-driven market. He worries Bevel may not be building fast enough, and he argues that AI’s development has moved from slow compounding to a chaotic “all at once” phase—compressing timelines and making predictions unreliable. That urgency ties back to Bevel’s “last mile data logistics” framing: like Amazon’s last-mile delivery solved the final delivery from warehouse to customer value, Bevel aims to solve the final delivery from centralized data systems to same-day, value-added end-user outcomes.

Operationally, Bevel treats AI as workflow lubricant rather than a replacement for judgment. Hu warns that AI trained on conventional information can only produce conventional thinking; letting it decide everything risks building products that look like everyone else’s. Bevel’s architecture includes an AI layer that orchestrates interactions, while the underlying differentiator remains its data infrastructure, processes, and controls—positioned as harder to replace than a simple “LM wrapper.”

Inside the company, Notion plays a central role: it supports AI-integrated workflows, flexible integrations, and even replaces parts of a CRM by routing inbound leads into Notion, auto-updating records, and enabling enterprise search across notes, Slack, and email. Hu also uses Notion’s AI to generate weekly summaries—sales, product development, production status, and delays—so leadership can quickly decide where to focus. For founders, his advice is to question conventional wisdom, build something impossible, and pursue ceaseless clarity—an “onion” that drives alignment once teams reach an “aha” moment.