I have been in product management for twelve years. Not twelve years at one company, twelve years across the full range of what product work looks like: large enterprises, early-stage startups, hardware manufacturing, software, mobile. Different industries, different team sizes, different engineering cultures. The one constant across all of it was what happened after the PRD was signed off and before implementation.
In an engineering team, that step looks like this: a tech lead runs a spike, sometimes alongside a solutions architect, and together they take the business problem, think through the system, map the architecture, surface the dependencies, and create a plan, a clear picture of what gets built when and why. The decisions made in that step determine whether the product holds together or falls apart three months in.
I've seen what happens when that step is done badly. Tech debt that compounds until someone stops everything to fix it. Features that ship with cracks already in them. Products that work at launch and fall apart six months later.
The gap I kept hitting
When I started building with AI, I quickly noticed a pattern. Every step in the product workflow had a purpose-built tool. Market research: Manus, Gemini Deep Research. Requirements: ChatPRD. Design: Magic Patterns, Coding: Claude Code, Cursor. Each one had a clear job and did it well.
But the planning step (the tech lead spike) had nothing.
So I did what a technical PM does when they know the output isn't good enough: I tried to close the gap manually. ChatPRD for implementation plan. Claude and Gemini for the planning. Iterating between sessions, filling in what each tool left behind, trying to assemble something coherent from pieces that were never designed to connect.
It took many rounds. And even after all of it, the output was never quite right.
The bugs that were never really bugs
Project after project, the same problems showed up once coding started: A frontend that didn't connect to the backend because the API contract had never been fully defined. A button that called an endpoint that hadn't been created yet, because the two were built in isolation and neither AI caught the dependency. Entire flows that worked on their own but broke the moment they needed to talk to each other.
These didn't show up as missing features. They showed up as bugs, which I spent days chasing down, only to find that the problem wasn't in the code. The code was fine. The plan was broken.
The coding agents did exactly what they were told. The requirements were simply incomplete.
Every time I hit one of these I had to stop, figure out where the plan had failed, patch it, and re-prompt. Sometimes that meant regenerating work that had already been done. Sometimes the agent had gone in a direction that conflicted with earlier decisions and the whole thing had to be untangled.
I knew what the problem was. I didn't have the right foundation before I started building. But I couldn't find a tool that gave me that foundation.
Building what I kept looking for
After my last project, where the planning failures were bad enough that I spent more time fixing the bugs than building the product, I decided to build the thing I kept looking for. Something that filled that gap properly, purpose-built for exactly this step.
I called it Zalcro.
The part I didn't plan for
Here's the thing I didn't expect.
I built Zalcro using the same AI-assisted workflow I'd been using for every other project. Which meant that at the start, before Zalcro existed, I was once again trying to plan a complex product without the tool I was building to solve exactly that problem.
I experienced the gap while building the solution to the gap.
The early versions were planned the hard way. As the product matured and I could start using Zalcro to plan its own development, the quality of the output improved noticeably. The tickets got tighter. The bugs got smaller and fewer.
That feedback loop (build with the tool, find what it gets wrong, fix it, build more) is how Zalcro became what it is today. By the time I'm done with final end-to-end testing, Zalcro will have taken itself through over 600 pull requests and 700 tickets. A tool that planned its own construction.
What Zalcro does
Zalcro sits between your idea and your coding tool. You describe what you want to build, answer a few questions, and get paste-ready prompts for Lovable, Bolt, or Replit, one per build phase, so your AI builds in the right direction from the first prompt. If you're building with Cursor, Claude Code, or Codex, you get dependency-aware tickets via MCP, or export to Linear, Jira, and ClickUp.
Under the hood, Zalcro runs a structured planning conversation (the questions a tech lead would ask) and generates the architecture, the system diagram, and the phased implementation plan that makes those prompts and tickets actually work.
The goal is simple. Work out how the pieces connect before your first prompt, and the agent builds in the right direction from the start. The loop where fixing one thing breaks something else, (because each piece was built without knowing what it needed to connect to) doesn't get started.
Plan first. Build with the structure already in place.
Try Zalcro free, no credit card required. Your first prompt and ticket set are free every month. Get started here.