Why we're building SuperPlan, and what we think is broken.
The problem
We've all seen it happen: Claude or Cursor writes 200 lines of clean code that solves the wrong problem.
Your product's goals are scattered. There's a Slack thread from last Tuesday, a Notion doc that hasn't been updated in weeks, and half the constraints live in someone's head. When you point an agent at a task, it's working with maybe 20% of what it actually needs.
And it's not just coding agents anymore. Sales teams are using AI to pull customer data. Marketing is generating campaigns. Product is drafting specs. Every department has agents now, and none of them are looking at the same information.
The result is a lot of deleted code and repeated work. We talked to 50+ teams about this, from early startups to large enterprises. The pattern was the same everywhere.
of AI time savings lost to fixing mistakes
of workflow spent just setting up context
teams we talked to while building this
What we're building
SuperPlan is basically a structured layer that sits between your messy docs and your AI agents.
The core idea: Agents don't need more context. They need the right context at the right moment, with constraints attached. Just dumping your wiki into a prompt creates noise. We wanted something that could actually enforce decisions, not just store them.
So if an agent starts building something that contradicts your spec, SuperPlan flags it before code gets written. If two agents are working on conflicting things, we catch that too.
Canvas is where your product truth lives. It's not a wall of text for humans. It's structured and queryable, designed for machines to pull from.
Mission Control turns specs into units of work. When an agent picks up a task, it already has the constraints, the context, and the reasoning behind the decisions.
Adherence MCP is the bridge to your coding tools. It sits in Cursor or Claude's context window and monitors what's being built against what was planned.
The bigger picture
Right now, most teams treat AI like a fast junior dev. You give it a task, it does the task, you review and fix the mistakes.
We think the bottleneck isn't the AI's capability. It's that your plans, constraints, and decisions aren't in a format AI can actually use. If you fix that, the whole dynamic changes. Agents can work more autonomously because they have real context. Humans spend less time babysitting and more time on the stuff that matters.
This isn't about replacing anyone. It's about giving both humans and AI a shared memory to work from.
Who we're building for
Mostly small teams that are already shipping with AI:
- Founders building products with Cursor, Claude, or Copilot as core workflow
- Dev agencies juggling multiple client specs across tools and timezones
- Engineering leads who want AI leverage but can't afford constant rework
If you've spent an hour debugging because your agent forgot what you told it two sessions ago, or found out a basic requirement was missing after you'd already built the feature, you'll get why we're building this.
Where we are
Early. We're learning a lot from the teams using it. The thesis is simple: give your agents a plan, not just a prompt.
If that resonates, we'd love to talk.