“App Market Media saw a 300% increase in Revenue with Affise. The сompany commits to generate efficient user-engagement strategies through performance-based models to ensure the maximum ROI for clients.”
Performance marketing is evolving at lightning speed. AI is no longer a buzzword – it’s the competitive edge that can turn vast amounts of campaign data into actionable insights in real time.
At Affise, we’re pushing this evolution forward by introducing Model Context Protocol (MCP) servers into our technology stack, enabling a new era of context-aware AI for affiliate and partner marketing.
Think of MCP as the “middleware for AI”—a layer that ensures your model can stay updated, relevant, and actionable by fetching live data and triggering operations without losing context.
Unlike traditional APIs or ad-hoc integrations, MCP creates a unified interface between LLMs (Large Language Models) and live data sources, tools, and workflows.
Why MCP Matters for Performance Marketing
Performance marketing generates an overwhelming volume of signals—clicks, conversions, bids, impressions, fraud detection triggers, and more. Historically, managing this required a patchwork of APIs, scripts, and human monitoring.
MCP changes the game by:
Real-time reporting: AI agents can provide the insights and real-time reports about caps, conversions, and best performing offers.
Cross-system orchestration: MCP enables AI to tap into CRMs, analytics platforms, fraud detection systems, and DSPs in one unified workflow.
Predictive insights: Models can continuously enrich their decision-making with live feed data rather than relying solely on historical training sets.
Affise MCP Server
This MCP server bridges the gap between AI assistants and affiliate marketing data, enabling intelligent analysis and optimization of your Affise campaigns using natural language.
Affise brings you an MCP server that can:
Simplify your daily routine tasks
Bring enterprise-level insights
Make automations with other MCP servers and tools like n8n.
If you think “this is too hard for me to understand,” it’s not!
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