How We Built Vortex IQ to Be Fast, Lean, and Future-Ready

Startups live and die by three levers: Speed, Cost, and Scale.

At Vortex IQ, we’re building an AI agentic platform that transforms e-commerce APIs into intelligent agents. From staging environments to SEO automation, our users rely on us to move fast, stay affordable, and scale predictably.

That meant choosing a tech stack that matched our ambition — without overengineering or overspending.

Here’s a transparent look at how we built our stack to deliver on all three fronts.

Speed: From Prompt to Production in Minutes

Frontend
  • Framework: React with Vite
  • Styling: Tailwind CSS
  • UI Components: shadcn/ui, Radix UI
  • Bundling: Vite → Blazing fast HMR and build times
  • Realtime: WebSockets for live agent updates

Why it works:
Rapid iteration. Reusable components. Sub-50ms UX on mission-critical views like dashboards, logs, and prompt interfaces.

Backend
  • Framework: Laravel (PHP) + Octane
  • Worker Layer: Laravel Horizon (queue-based)
  • Web Server: RoadRunner or Swoole for concurrency
  • Auth: Laravel Sanctum (API token-based)
  • Rate Limiting: Custom throttling at agent and endpoint levels

Why it works:
Battle-tested, clean architecture. Great dev velocity + async processing out of the box.

Prompt/Agent Layer
  • LLMs: OpenAI (GPT-4o), Claude 3, Llama 3 (Meta)
  • Routing Logic: Custom Model Context Protocol (MCP)
  • Execution Engine: Multi-agent orchestration with fallback, retry, reasoning chain
  • Logging: Full visibility of agent state, actions, errors, and outcomes

Why it works:
Every prompt is routed through our MCP layer for context-rich, safe execution across APIs.

Cost: Optimising for Burn, Not Just Brilliance

Cloud Infrastructure
  • Primary Hosting: Vultr (for cost/performance edge)
  • Container Orchestration: Docker + Docker Compose (simple, no Kubernetes overhead)
  • CI/CD: GitHub Actions + Laravel Envoy
  • Image Optimisation: Squoosh CLI + AVIF + WebP pipelines
  • Database: PostgreSQL with pgbouncer pooling

Why it works:
Every component was selected to avoid vendor lock-in and reduce compute/storage waste.

Model Cost Control
  • Prompt-level cost monitoring
  • Fallback to open-source models (LLaMA, Mistral) when task complexity allows
  • Token caching and deduplication to prevent redundant calls

Why it works:
LLMs can eat your margin. Our routing logic balances performance with cost efficiency.

Scale: Designed to Handle 10x Without Rewrites

Modularity
  • All AI agents are microservices with individual repos, scopes, and tests
  • Agents register themselves with the Agent Registry and auto-sync to our UI
  • Internal CLI tools to scaffold new agents in minutes
API Layer
  • JSON Schema-based validation for every incoming/outgoing API interaction
  • API Gateway abstraction to support Shopify, BigCommerce, Adobe Commerce, Google Analytics, etc.
Observability & Rollback
  • Centralised logging with Papertrail
  • Real-time alerts for agent anomalies
  • Rollback system for every action (e.g. undo a price change, revert SEO update)

Why it works:
We scale horizontally. Agents are disposable and restartable. Nothing is hardcoded.

Final Thought

In a world racing towards agent-led systems, the stack is not just tech — it’s strategy.

We didn’t chase the shiniest tools. We optimised for:

  • Developer velocity
  • Infrastructure cost
  • Future-proof execution
  • Real-time performance

This is the architecture that helps us ship weekly, serve global merchants, and stay capital-efficient — even as we scale to 3,000+ retailers.

Want to know how our agentic platform could power your use case?
Visit vortexiq.ai or write to [email protected].