AI Operations

Enterprise AI infrastructure: n8n workflow automation, agentic AI systems, MCP integration, and vector database solutions.

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About this service

Build and deploy enterprise-grade AI operations infrastructure. From visual workflow automation with n8n to autonomous AI agents and vector-powered knowledge retrieval, we help you create intelligent systems that automate complex business processes at scale.

AI Operations Platform

Enterprise-grade AI infrastructure: workflow automation, agentic systems, MCP integration, and vector database solutions.

n8n Workflow Automation

Self-hosted • Open source • 400+ integrations

Platform Capabilities

●Visual workflow builder with drag-and-drop nodes
●400+ pre-built integrations (Slack, GitHub, Jira, Salesforce)
●Custom JavaScript/Python function nodes
●Webhook triggers and scheduled executions
●Error handling with retry logic and fallback paths

Example Workflows

Lead Qualification

Form submit → AI scoring → CRM update → Slack notification

Document Processing

Email attachment → OCR extraction → Database insert → Report

Security Monitoring

Log ingestion → Anomaly detection → Alert → Incident ticket

Agentic AI Workflows

Autonomous AI agents that plan, execute, and iterate

Agent Architecture

User Goal

Natural language

Planner Agent

Breaks into tasks

Executor Agent

Tools + actions

Result

Verified output

Longevus (Internal)

Our proprietary AI model built for enterprise workflows. Privacy-first, runs on your infrastructure with full data sovereignty and custom training.

Claude Agent SDK

Build multi-step agents with Anthropic's Claude that can use tools, browse the web, write and execute code, and manage complex workflows.

OpenAI Assistants

GPT-5 powered assistants with code interpreter, file search, and function calling for automated data analysis and report generation.

Custom Agents

LangChain/LangGraph, CrewAI, and AutoGen frameworks for building specialized agents with memory, tools, and collaborative capabilities.

Model Context Protocol (MCP)

Universal protocol for AI-tool integration

What is MCP?

The Model Context Protocol (MCP) is an open standard that enables AI models to securely connect to external tools, data sources, and services through a standardized interface.

●Standardized tool definitions with JSON Schema
●Secure resource access with permission controls
●Works across Claude, ChatGPT, and other LLMs
●Local and remote server deployment options

MCP Server Examples

Database MCP

Query PostgreSQL, MySQL, SQLite directly from AI conversations

GitHub MCP

Create PRs, review code, manage issues with AI assistance

Stripe MCP

Manage payments, subscriptions, and invoices via AI

Custom MCP

Build domain-specific tools for your business workflows

Vector Databases & RAG

Retrieval Augmented Generation for enterprise knowledge

RAG Pipeline Architecture

Ingest

Documents, PDFs, APIs, databases

Chunk & Embed

Split text, create vector embeddings

Retrieve

Semantic search for relevant context

Generate

LLM answers with citations

Supported Vector Databases

Pinecone — Fully managed, serverless vector DB

Weaviate — Open-source with hybrid search

Qdrant — High-performance with filtering

ChromaDB — Lightweight, developer-friendly

pgvector — PostgreSQL extension for embeddings

Milvus — Scalable, distributed vector DB

RAG Use Cases

●Internal knowledge base Q&A for employees
●Customer support chatbots with product documentation
●Legal document search and contract analysis
●Code documentation and API reference assistant
●Research paper analysis and literature review
●Compliance document retrieval and audit support

Technology Stack

LLM Providers

Longevus (Internal)
Claude (Anthropic)
GPT-5 (OpenAI)
Gemini (Google)

Orchestration

n8n
LangChain
LangGraph
CrewAI

Vector DBs

Pinecone
pgvector
Weaviate
ChromaDB

Infrastructure

Docker
AWS
PostgreSQL
Redis