AIdeas: Archly — Think First, Build Right
Stop building the wrong thing. Archly is an AI workspace that validates your product idea before you write a single line of code, then helps you design the architecture visually. A Bedrock-powered research consultant challenges your assumptions early. A visual canvas lets you drag and wire components while an AI advisor flags missing auth, error handling, and scalability gaps in real-time. The output? Specs detailed enough that Kiro and other AI coding tools generate correct code on the first try.
App Category: Workplace Efficiency
Archly Vision
We kept running into the same cycle. Have an idea, get excited, start building, realize halfway through that we skipped something important — authentication, error handling, a clear data model — and then spend more time fixing than building. And with AI coding tools, it got worse in a weird way. They're fast, but they're only as good as what you tell them. Vague instructions give you vague code, and then you're stuck in a loop of "almost right" fixes.
Archly exists because we wanted to break that cycle.
It's two things. First, it's a thinking tool — it walks you through defining what you're actually building, who it's for, what already exists, and whether the idea holds up. There's an AI research consultant powered by Amazon Bedrock that asks the uncomfortable questions early, so you don't discover the answers late.
Second, it's a visual architecture canvas. You drag components, services, endpoints, and data models onto a canvas and wire them together. While you design, a Bedrock-powered assistant watches your architecture in real-time and flags what's missing — forgot rate limiting? No auth layer? Single point of failure? It tells you before you ship it.
The end result is a spec so detailed that when you hand it to Kiro, Cursor, or any AI coding tool, it actually generates the right thing. First try. And with the MCP server integration, Kiro stays in sync with your project context across sessions — no more re-explaining what you built yesterday.
Why This Matters
Most projects don't die because of bad code. They die because someone built the wrong thing, or built the right thing with missing pieces.
Archly is for the solo dev at 1am with a new idea. The indie hacker who's tired of rewriting the same auth flow. The small team that keeps losing context between AI chat sessions. The founder who wants to validate before burning a weekend.
It sits in the gap between "I have an idea" and "I'm writing code" — the part most people skip, and most tools ignore.
How We Built This
We used Kiro throughout the entire build process — from planning the architecture to writing and debugging the code. Kiro's agentic capabilities helped us move fast: we'd describe what we needed, Kiro would scaffold it, and we'd iterate from there. For a project built in roughly a day, having an AI IDE that actually understands project context made a real difference.
The frontend is React with React Flow powering the architecture canvas — the same library behind tools like Excalidraw's node-based UIs. We studied how they handle state, dragging, and connections, then built Archly's canvas on top of that.
The core has a few moving parts:
- A five-step validation workflow that forces you to think through problem, audience, competition, risks, and metrics before touching architecture
- A visual canvas with draggable nodes, shift-click connections, and a dot-grid background — feels like a whiteboard, works like a spec
- Three scoped AI assistants powered by Amazon Bedrock: a main project chat, a research consultant (for validation), and an architecture advisor (for the canvas) — each one only sees what's relevant to its job
- Business model canvases (lean, BMC, SWOT, value prop, empathy map) built into the problem definition flow
- A task system with kanban boards, milestones, and a bucket zone for unassigned work
- An MCP server so tools like Kiro can pull project context, architecture decisions, and specs directly — keeping everything in sync across sessions
The backend is fully AWS: Lambda and API Gateway for the API, DynamoDB for storage, S3 and CloudFront for hosting the frontend, Cognito for auth, and Amazon Bedrock for all three AI assistants. EventBridge, SNS, and SES handle async events and notifications.
What We Learned
Kiro changed how we build. Instead of context-switching between docs, terminals, and chat windows, we stayed in one place. Kiro's specs and steering files kept the project coherent even when we were moving fast and making changes across multiple files. The CLI was especially useful for quick iterations — test something, see it break, fix it, move on.
On the AWS side, we got a crash course in the real stuff — IAM permission scoping, service quotas (and the difference between the number you see and the number that matters), SPA deployment with CloudFront, and wiring up Bedrock models through Lambda. The moment the Bedrock-powered consultant flagged a missing error handling layer in our own architecture, we knew the concept worked.
Building a tool that solves your own problem is a different kind of motivation. Every time we've fought with AI-generated code that missed the mark, we wished something like this existed. Now it does.
Demo
Try it out: https://archly.returnfeed.com
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