There Was an AI Tool for Every Step of My Workflow. One Step Had Nothing.
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 the step that happened after the PRD was signed off and before anyone wrote a line of code.
In a real 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 come back with a plan. Epics in the right order. 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 have seen what happens when that step is skipped or done badly. I have lived it on both sides of the table.
The gap I kept hitting
When I started building with AI tools, I noticed a pattern quickly. Every step in the product workflow had a purpose-built tool. Market research: Manus, Gemini Deep Research. Requirements: ChatPRD. Coding: Claude Code, Cursor. Each one had a clear job and did it well.
But the architecture step (the tech lead spike) had nothing.
So I did what a technical PM does when they know enough to know the output isn't good enough: I tried to close the gap manually. ChatPRD for implementation plan structure. Claude and Gemini for the architectural thinking. 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 LLM endpoint that hadn't been created yet, because the ticket for the button and the ticket for the endpoint were written in isolation and nobody 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. Bugs I spent days chasing down, only to find that the problem was never 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 architecture than building the product, I decided to build the thing I kept looking for. Something that filled that gap properly, not another general-purpose AI tool pointed at an architecture problem, but something purpose-built for exactly this step.
I called it Zalcro. The name is a nod to Velcro, a great invention that connects two things together tightly. In this case, your plan and your code.
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 the architecture of 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 agents stopped hitting dead ends. 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 approximately 600 tickets and close to 500 pull requests. What's not tracked is the multiple sleepless nights and cups of coffee.
What Zalcro does
Zalcro sits between your idea and your coding tool. You describe what you want to build or drop in your PRD. Zalcro runs a structured planning conversation, asks the questions a tech lead would ask, and generates four documents: an Architecture Design Document, a system diagram, a phased implementation plan, and granular tickets your coding agent can execute without asking clarifying questions.
If you're using a vibe coding tool like Lovable, Bolt, or Replit, Zalcro generates a Prompt Pack instead: Paste-ready prompts tailored to your platform that give your tool the architectural context it needs to build in the right direction from the start.
The goal is simple. No more bugs that turn out to be planning failures. No more frontend that doesn't connect to the backend. No more buttons that call endpoints that don't exist yet.
Plan first. Then build.
Try Zalcro free, no credit card required. Your Architecture Design Document, diagram, and Phase 1 output are free every month. Get started here.