Most "AI solutions" are mere API wrappers. RE:AI (AI ERP) is a fundamental shift in enterprise architecture: a Cognitive Operating System powered by an orchestrated swarm of specialized agents. I engineered this system to transition businesses from "Human-in-the-loop" to "Human-on-the-loop," where the AI doesn't just suggest—it executes.

The architecture is built for low-latency reasoning and high-fidelity execution.

Logic Layer: LangGraph for stateful, cyclic multi-agent orchestration. Unlike linear chains, this allows for recursive reasoning loops where agents (CMO, CSO, Brain) peer-review each other’s output before delivery.

Neural Backbone: A hybrid of GPT-4o and Qwen for complex reasoning and Claude 4 Sonnet for high-context coding/data tasks, routed via a custom Model Router to optimize cost-per-token and latency.

Spatial Frontend: Next.js 16 (App Router) with Server Actions and Optimistic UI updates, creating a zero-latency "Desktop" experience in the browser.

Memory Architecture: PostgreSQL with pgvector for High-Dimensional Vector Embeddings. I implemented a Multi-Tier RAG (Retrieval-Augmented Generation) pipeline that separates transient session memory from permanent enterprise knowledge.

Infrastructure: Redis for distributed state caching and Upstash for serverless rate-limiting and task queuing.

The complexity of RE:AI lies in its Directed Acyclic Graph (DAG) workflow. Most systems fail when a task requires cross-departmental logic. I solved this by building:

The Contextual Synapse: A shared state management system where the CMO (Marketing) can programmatically inform the CSO (Sales) of a new campaign, allowing the Sales agent to adjust its outreach tone in real-time based on live marketing assets.

Autonomous Web-Intelligence: Instead of static searches, I built a Distributed Scraping Engine that bypasses bot-detection to synthesize "Live Market Truth," which is then fed into a proprietary Lead Scoring Heuristic.

Headless Social Orchestration: Deep integration with the Facebook Graph API and LinkedIn API, featuring an automated Asset Validation Layer—ensuring that every AI-generated image passes brand-consistency checks before a single packet is sent to social servers.

I transformed a fragmented manual workflow into a Synchronized Execution Loop:

Synthesize: Ingest massive datasets into the "Brain" via Vector Embeddings.

Create: Generate high-fidelity marketing assets using DALL-E 3 with programmatic prompt-engineering.

Distribute: Direct API-level deployment to global social platforms.

Scale: Autonomous lead extraction and scoring with 98% alignment to the target ICP (Ideal Customer Profile).

For more info, please visit: https://re-ai.new-wave.io/