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Architeq

Inspiration

Every software project begins with an idea, but transforming that idea into a well-structured system architecture remains one of the most time-consuming and collaborative parts of software engineering.

Teams constantly switch between requirement documents, whiteboards, chat applications, and diagramming tools before writing a single line of code. As projects evolve, architecture diagrams quickly become outdated, dependencies are overlooked, responsibilities become unclear, and design decisions are lost.

We wanted to eliminate this gap between product thinking and system design.

Our vision was simple: what if AI could understand a product idea, automatically generate a production-ready software architecture, allow teams to collaboratively refine it, and preserve every design decision through version control?

That vision became Architeq.

What it does

Architeq is an AI-powered collaborative software architecture workspace that transforms raw product ideas into interactive, editable system architecture diagrams.

Users can provide product ideas, feature specifications, PRDs, or requirement documents. A multi-agent AI pipeline analyzes the requirements, extracts software components, maps dependencies, identifies architectural risks, and generates a fully editable architecture graph.

Unlike traditional AI-generated diagrams, the generated architecture is not static.

Teams can:

Edit nodes and connections
Add new components
Assign implementation ownership
Receive AI-powered architectural recommendations
Collaborate in real time
Save immutable architecture versions
Restore previous system designs whenever required

Instead of becoming outdated documentation, the architecture evolves together with the project.

How we built it

AI Multi-Agent Pipeline

Rather than relying on a single LLM response, Architeq uses a specialized multi-agent pipeline where each agent owns a distinct engineering task. We deliberately consolidated what began as four separate agents into two high-throughput agents, each performing two tightly-related jobs in a single pass:

Blueprint Agent – extracts the product's software components (services, APIs, core features, authentication, databases) and maps the dependencies between them in one pass, producing the structural skeleton of the system as nodes and edges. It is intentionally product-focused, modeling the components a team would actually name and build rather than low-level infrastructure plumbing.
Refine Agent – performs gap & risk analysis (surfacing missing components, architectural bottlenecks, and design risks) and generates implementation ownership and build-priority assignments in one pass, completing the skeleton into a build-ready plan.

Collapsing four sequential agents into two roughly halves the number of LLM round-trips. Each call runs on Gemini 2.5 Flash with reasoning/"thinking" disabled and a constrained JSON response schema for fast, deterministic structured output, plus an automatic OpenAI fallback for resilience. The result is a dramatic reduction in architecture-generation latency without sacrificing quality.

These outputs are merged into a single structured graph instead of plain text.

Interactive Visual Workspace

The generated graph is automatically laid out using ELK.js (the Eclipse Layout Kernel) — a battle-tested layered-graph layout engine that produces a clean hierarchical arrangement with orthogonal, node-avoiding edge routing. The laid-out graph is rendered inside an interactive collaborative canvas (built on React Flow) where users can:

Move components freely
Edit architecture nodes
Create or remove services
Modify dependencies
Assign team members
Update metadata
Continuously evolve the design

This makes the AI-generated architecture a living engineering workspace rather than a static diagram.

Production-Ready Cloud Infrastructure

Instead of relying on a local database, we built Architeq on Amazon Aurora PostgreSQL (AWS RDS) to provide a scalable, production-ready persistence layer.

Aurora stores:

User accounts
Projects
Architecture versions
Graph nodes
Graph edges
Team assignments
Project metadata

We designed a normalized relational schema using Prisma ORM, enabling type-safe database access while efficiently modeling graph-like relationships and immutable architecture snapshots.

The frontend is deployed on Vercel, while Next.js API Routes communicate with Amazon Aurora PostgreSQL through Prisma, creating a clean separation between presentation, business logic, and persistence.

To accelerate frontend development, we leveraged Vercel v0 to scaffold and refine key interface components before integrating them into our production application. These generated components were then extensively customized to fit Architeq's collaborative workflow, architecture editor, and design language.

Version-Controlled Architecture

One of Architeq's core capabilities is immutable versioning.

Every save creates a new architecture snapshot instead of overwriting existing work.

This enables teams to:

Restore previous architectures
Compare design evolution
Experiment safely
Track architectural decisions throughout development

Version history transforms architecture diagrams from temporary documentation into long-term engineering assets.

Why Amazon Aurora PostgreSQL?

Architeq manages highly relational data including users, projects, versions, graph nodes, edges, assignments, and project metadata.

We chose Amazon Aurora PostgreSQL because it combines PostgreSQL compatibility with fully managed cloud infrastructure, allowing us to focus on building the product instead of managing database operations.

Aurora provides:

Managed backups
High availability
Automatic scaling
Production-grade reliability
Native PostgreSQL compatibility

This allowed us to prototype quickly while building on infrastructure capable of supporting real-world collaborative workloads.

Challenges we ran into

One of our biggest engineering challenges was designing a persistence model for editable architecture graphs.

Graph structures are naturally connected, while relational databases are optimized for structured tables. We redesigned our persistence layer into a normalized schema capable of representing nodes, edges, projects, assignments, and immutable architecture versions without sacrificing query performance.

Migrating to Amazon Aurora PostgreSQL also required configuring secure cloud networking, relational modeling, Prisma integration, and production-ready database connectivity.

Another significant challenge was orchestrating multiple AI agents into a single coherent architecture instead of producing disconnected outputs. Consolidating the pipeline from four agents into two — each handling two related tasks per call — meant carefully designing prompts and response schemas so component extraction, dependency mapping, risk analysis, and task allocation stayed consistent while keeping generation fast.

Automatically arranging the generated graph was a challenge in itself. We initially built a custom hierarchical layout engine, but achieving clean, readable diagrams with non-overlapping nodes and orthogonal edge routing proved difficult to maintain. We migrated to ELK.js, which gave us reliable, production-grade layered layouts and let us focus on the editing experience instead of layout math.

Finally, enabling collaborative editing while preserving architecture history required careful state management and versioning strategies.

What we learned

Building Architeq taught us that production AI applications require far more than prompt engineering.

Throughout this project we gained hands-on experience with:

Multi-agent AI orchestration and pipeline consolidation for lower latency
Structured LLM output with response schemas and model fallbacks
Automatic layered graph layout with ELK.js
Graph-based application design
Cloud-native database architecture
Amazon Aurora PostgreSQL
Prisma ORM
Secure database connectivity
Version-controlled persistence
Collaborative application state management
Production deployment using Vercel

One of our biggest takeaways was learning how to migrate from a document-oriented persistence model to a normalized relational architecture while preserving the flexibility required for editable graph data — and, on the AI side, that fewer, well-scoped agents can outperform a longer chain of narrow ones.

Most importantly, we learned that AI is most valuable when it augments engineering workflows instead of replacing engineers.

What's next for Architeq

We envision Architeq becoming a complete software architecture operating system for engineering teams.

Our roadmap includes:

GitHub integration for automatic architecture synchronization
Infrastructure-as-Code generation
Cloud deployment recommendations
Security and compliance analysis
Cloud cost estimation
Architecture quality scoring
Sprint planning integration
Multi-project dependency visualization
Export to production-ready engineering documentation

By combining AI-powered architecture generation, ELK.js layout, Amazon Aurora PostgreSQL, Vercel, v0, and modern collaborative workflows, we built a platform that transforms software ideas into living, version-controlled system architectures that continuously evolve alongside the products they represent.