Raval AI

Inspiration

Marketing has become fragmented across dozens of tools. A founder may need one tool for strategy, another for content, another for social media, another for SEO, and increasingly, another for understanding how their brand appears inside AI systems.

We wanted to build something closer to an AI marketing operator, not just another content generator. That led us to Raval AI, an AI-native marketing platform designed to understand a business, remember its brand context, and help turn strategy into execution from one workspace.

What We Built

Raval AI combines marketing strategy, content creation, brand intelligence, social workflows, analytics, and AEO/GEO diagnostics in one platform.

At the center is Ravi, our Marketing Reasoning Core. It uses four perspectives: Brand, Customer, Competitor, and Market. A persistent Brand DNA layer gives the system structured context about a company's voice, audience, visual identity, and preferences so generated work stays consistent.

We also built an AI Visibility layer that helps businesses understand and improve how they appear in AI-generated answers, alongside an Agency OS for managing multiple clients from one command center.

How We Built It

The project was built as a full-stack web application using TanStack Start, React, TypeScript, Vite, Tailwind CSS, Supabase, Cloudflare Workers, and OpenRouter.

Our AI architecture uses different models for different tasks. Qwen 3 Max powers the main conversational experience, Gemini 2.5 Pro handles structured extraction and intelligence tasks, and image generation is integrated through OpenRouter.

We designed the system around a single AI gateway, structured Brand DNA, persistent memory, tool-aware chat, server-side AI operations, and a human approval step before generated content can be scheduled or published.

What We Learned

One of our biggest lessons was that building an AI product is not simply about connecting an LLM to a UI. The quality of the product depends heavily on context, memory, workflow design, and when the AI should or should not make decisions.

We also learned that combining strategy and execution requires careful product architecture. Instead of creating isolated AI features, we focused on connecting them into a continuous workflow.

Challenges

The biggest challenge was building a system that could maintain useful business context across different marketing tasks without creating unnecessary complexity or AI costs.

We addressed this with structured Brand DNA, memory extraction, relevance-ranked context, compacted conversation history, token limits, caching, request deduplication, and separate models for different workloads.

Another challenge was making AI-generated work reliable enough for real marketing workflows. We therefore designed a Needs Approval stage so users remain in control before anything is scheduled or published.

Raval AI is still evolving, but the project gave us a strong foundation for building an AI-native marketing system where intelligence and execution work together.

Built With

• aeo
• ai-agents
• ai/llm
• brand-intelligence
• cloudflare-workers
• framer-motion
• gemini-2.5-pro
• generative-ai
• geo
• marketing-automation
• openrouter
• playwright
• postgresql
• qwen-3-max
• radix
• react
• seo
• shadcn/ui
• social-media-automation
• supabase
• tailwind-css
• tanstack-start
• typescript
• vite