vPlan

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  AI Consultant

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  Overview

Inspiration

vPlan was inspired by a simple problem: founders often move forward with an idea before they’ve actually pressure-tested whether the business is feasible. We wanted to build something that helps people slow down, think structurally, and make better pre-launch decisions before they spend time and money building the wrong thing.

What it does

vPlan is an AI business planning platform for founders who are still in the pre-launch stage. Users work through a 9-chapter feasibility worksheet, and once that’s complete, it triggers a 5-agent AI pipeline that helps turn raw ideas into a more structured business plan.

How we built it

We built vPlan as a Next.js SaaS with a remote-first workflow. The product is designed around a guided planning experience: first the founder fills out the worksheet, then the AI pipeline processes the input and generates planning guidance. We kept the scope focused on the core workflow so we could ship a lean MVP and validate the concept quickly. We also dogfood the product internally — vPlan uses vPlan to plan vPlan — which makes the workspace a real test environment for improving the product as we build it.

Challenges we ran into

One of the biggest challenges was keeping the product focused. It would be easy to add more AI features, but the real question is whether founders actually want a structured planning tool before launch. Another challenge was balancing depth and usability in the worksheet. We wanted enough structure to produce useful output, but not so much friction that users drop off before completing it.

Accomplishments that we're proud of

We’re proud of building a product that’s centered on a real founder pain point rather than AI novelty. The 9-chapter worksheet and 5-agent pipeline give vPlan a clear workflow instead of feeling like a generic text generator. We’re also proud that the product is already being used to plan itself. That kind of dogfooding helps us catch issues early and keep the product grounded in real usage.

What we learned

We learned that structure improves output quality. The better the input framework, the more useful the AI becomes. We also learned that pre-launch founders need clarity more than complexity. They want help answering the hard questions: Is this worth building? Who is it for? How risky is the launch?

What's next for vPlan

Next, we want to keep refining the onboarding and activation flow, validate willingness to pay, and make the workspace even more useful for repeat planning and iteration. Longer term, the goal is to turn vPlan into a planning system founders keep coming back to, not just a one-time questionnaire.

Built With

• bizcrush
• openaiapi
• python
• supabase
• typescript
• vercel