The AI Industry Has a Data Problem

AI is advancing at an unprecedented pace, but there’s one major flaw holding it back: access to real-time, high-quality data.

Right now, AI models have no built-in way to retrieve fresh, live information. Instead, they rely on scraping, outdated training data, or expensive third-party APIs to fill the gap.

Scraping: The Default for AI Builders & LLMs Today

Since AI models can’t access up-to-the-minute data, AI developers are resorting to scraping websites, paying for sophisticated scraping tools, or even buying scraped datasets.

🔹 Even major AI companies have been caught scraping — some LLM providers have quietly admitted to pulling data without permission.
🔹 Developers pay for web scraping services to feed their AI agents real-time information.
🔹 Legal and ethical concerns are mounting — but scraping remains the default because there hasn’t been a better solution.

LLMs Are Cut Off From Fresh Knowledge

Most large language models (LLMs) have a knowledge cutoff date — meaning they lack awareness of recent events, stock prices, sports scores, or breaking news.

This leads to:
❌ Outdated responses for time-sensitive queries
❌ Hallucinations where the AI makes up facts to fill the gaps
❌ A poor user experience for anyone relying on AI for real-time decisions

To truly be useful, LLMs need live, real-time data at the point of inference — not just during training.

RAG and MCP: The Rails for AI Data Access

What is RAG (Retrieval-Augmented Generation)?

RAG is a technique that retrieves relevant, real-time data and injects it into the AI’s context window before answering.

1. An AI model receives a user query.
2. It searches for real-time, relevant data from external sources.
3. The retrieved data is injected into the model’s memory before generating a response.
4. The AI answers using both its pre-trained knowledge and the live data.

This method limits hallucinations and improves accuracy, making it essential for AI agents that need fresh information.

What is MCP (Model Context Protocol)?

The Model Context Protocol (MCP) is an open standard developed by Anthropic that facilitates the connection of AI assistants to various data systems.

🔹 MCP enables AI to access external data seamlessly — enhancing its ability to provide real-time responses.
🔹 It’s designed to work with anyone that integrates this protocol, making it a universal method for AI-data interaction.
🔹 By combining RAG with MCP, AI systems can query live data sources in a standardized way.

Together, RAG and MCP serve as the foundational rails for real-time AI data access. But they need an infrastructure layer that makes it easy for AI developers to tap into permissioned, high-quality data.

🚀 At Dappier: We are on a Mission to Provide Ubiquitous Data Access to AI with a Sustainable Monetization Framework

AI builders need permission’ed, real-time data pipelines, not scraping tools.

At Dappier, we’re using RAG and MCP to build the infrastructure that connects AI models to real-time data sources — from frontier models to consumer AIs and agentic systems.

Instead of scraping, AI developers should have direct access to permissioned, high-quality, real-time data.

🎉 Announcing: Free, Real-Time AI Data Access

Starting today, our Real-Time Data Model is free, with the plan to in the future introduce ads natively in AI responses, ensuring a sustainable business model that democratizes data access for all builders.

We’re effectively taking our real-time, internet-connected RAG model — previously priced per query — and making it free for developers to use. This ensures a sustainable business model that democratizes data access for all builders, while ultimately helping publishers and data providers monetize in a fair and transparent way.

No more paywalls. No more scraping. Just free, high-quality data for AI builders — with a clear path for data owners to earn revenue.

Why Free?

We’re making Dappier’s Real-Time Data Model free because we believe in building the right framework for AI data access — one that encourages AI builders to use permissioned, real-time data instead of scraping. RAG and MCP are the true rails for data at the point of inference, and by making this model accessible, we’re educating developers on the best way to integrate live, high-quality data into their AI systems.

But it’s not really about “free” — it’s about creating a sustainable, equitablebusiness model. Soon, we’ll be introducing native ads inside AI-generated answers, ensuring that data providers, publishers, and AI builders all benefit. This approach compensates all parties fairly while making real-time data scalable for the AI economy.

What’s Next?

Our next step is to expand beyond general real-time search and add unique, permissioned data access, including:

✅ Stock market financial news & data
✅ Publishers who opt-in to permissioned content access
✅ Sports data and real-time game insights
✅ Additional verticals where AI builders need real-time information

Soon, we’ll be rolling out innovative native ad formats inside AI answers and announcing major partnerships in this space that will change the game for data access in AI.

Dappier’s Real-Time Data Model Is Live Across 6 Major AI Platforms

Each AI platform now has direct access to Dappier’s real-time data model, enabling live data retrieval across AI assistants, LLMs, and automation tools.

Agent.AI — AI assistants that fetch real-time stock, travel, and news updates
Anthropic MCP — AI agents powered by Claude with real-time, accurate answers.
Camel AI — AI agents that can tap into live data.
LangChain — AI retrieval systems that fetch news, finance, and more.
OpenAI GPTs — GPTs that stay updated with live information
Zapier — AI-powered workflow automation with real-time data.

Each integration allows AI models to query Dappier’s real-time data model instantly, making live data available at the point of inference.

How AI Builders Can Use Dappier’s Real-Time Data Model

Agent.AI — AI Assistants with Live Data

Agent.AI’s AI-powered assistants can now access real-time stock market, travel, and news data instantly.

Integration Steps:

Set Up API Request in Agent.AI
URL: https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15
Request Type: POST
Headers:
{
“Authorization”: “Bearer your-api-key”,
“Content-Type”: “application/json”
}

JSON Body:

{ “query”: “{{user_message}}” }

Replace your-api-key with your actual API key from Dappier.
Save the API response to use within your AI agent.
Test and Deploy – Ensure the assistant fetches real-time stock, travel, or news data based on user input.

🔗 Explore the possibilities and cookbook recipes!

Anthropic MCP — Smarter Claude-Powered AI

Dappier’s MCP server connects any compliant LLM like Claude or AI agent to become an expert in anything by providing access to specialized models, including Real-Time Web Search, News, Sports, Financial Stock Market Data, and exclusive content from premium publishers.

Sample integration:

{
    "mcpServers": {
        "dappier": {
            "command": "uvx",
            "args": ["dappier-mcp"],
            "env": {
                "DAPPIER_API_KEY": "YOUR_API_KEY_HERE"
            }
        }
    }
}

🔗 Explore the possibilities and cookbook recipes!

Camel AI — AI-Powered Real-Time Search

Building your AI app with CAMEL? Supercharge your app with immediate access to real-time data, spanning news, entertainment, finance, market data, weather, and more.

Sample code snippet:

from camel.toolkits import DappierToolkit

response = DappierToolkit().search_real_time_data(
query=“latest news on CAMEL AI”
)

print(response)

🔗 Explore the possibilities and cookbook recipes!

LangChain — Real-Time AI Retrieval

LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. Building your AI app with LangChain? Supercharge your app with Dappier’s retriever and tools for real time data!

Sample code snippet:

from langchain_dappier import DappierRealTimeSearchTool

tool = DappierRealTimeSearchTool()

response = tool.invoke({“query”: “Latest news on Langchain AI”})

print(response)

🔗 Explore the possibilities and cookbook recipes!

OpenAI GPTs — AI Agents That Stay Updated

OpenAI’s Custom GPT models allow developers to create AI assistants tailored to specific use cases. These models can be fine-tuned with additional instructions, personality settings, and access to external tools or APIs, making them highly adaptable.

Dappier empowers AI with verified and up-to-date information for a wide range of applications.

Example integration:

curl -X POST "https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "Find the best flight and hotel deals to Miami next weekend. Include weather updates."}'

🔗 Explore the possibilities and cookbook recipes!

Zapier — Automating AI-Powered Alerts & Workflows

Zapier is a powerful automation platform that connects your favorite apps and services, enabling you to create workflows without writing any code. With Dappier’s Zapier integration, you can automate tasks using real-time data, AI insights, and personalized recommendations.

Example integration:

Integration Steps:
Create a New Zap

Log in to Zapier and click Create a Zap.
Select a Trigger:
Search for “Schedule by Zapier” and set it to run daily.
Click Continue, then Test Trigger to confirm.
Fetch Real–Time Data

Click the “+” icon to add an action.
Search for Dappier and select it.
Choose the action “Get Real–Time Data.”
Configure the fields:
Query: “Today’s weather and top events”
Click Continue, then Test the Step to fetch real–time data.

🔗 Explore the possibilities and cookbook recipes!

Create Your Free Dappier Account Today

Dappier’s Real-Time Data Model is now free for AI developers. Get an API key and start building with real-time, permissioned data right now.

Sign up for a free Dappier account

I’ll be posting frequently with new cookbooks and tutorials for each of these platforms we are integrated with and future platforms on how to build powerful AI agents using Dappier’s data across various agentic platforms, enabling real-time access at the point of inference.

If you have ideas for new platforms or data sources you’d like to see integrated with Dappier, drop a comment!

Exciting Stuff!