Your AI needs context. Faraday’s got it

Why do AI tools so often fail to drive ROI in marketing? Frequently, the problem isn't the model—it’s the inputs. To drive real value, AI needs to understand the people it’s engaging with. Without context like financial signals, lifecycle indicators, or real-world constraints, agents miss the meaning behind behavior—like whether someone buying baby clothes is shopping for their own child, signaling future purchases, or buying a one-time gift for a baby shower. Faraday provides essential consumer data so agents have context that allows them to act with nuance and precision.

What AI knows, and what it doesn’t

You likely recall your first conversations with an LLM chatbot. You probably noticed that the AI knows almost everything about the world, but absolutely nothing about you: the customer standing right in front of it. Indeed, if you set this giant brain to complex tasks like writing a short story or generating a video of a monkey, it jumps into action. But ask an LLM a seemingly simple question, like “How should I talk to the person who just landed on my pricing page?” and suddenly you remember that AI doesn’t actually know everything. Without the specific identity and history of that visitor, the world's most advanced AI can do little more than offer a polite, generic greeting.

This is the paradox of the agentic era. The "raw muscle" of Large Language Models (LLMs) has placed every piece of shared human knowledge at our fingertips, but that means that shared knowledge is no longer a differentiator. In a world where every competitor has access to the same models and internet, the superpower isn’t volume of information—it’s relevance.

The winner today isn’t the brand with the most information; it’s the one with the specific knowledge that illuminates the path to value. Faraday gives your AI agents the consumer context they need to stay grounded in reality, enabling them to confidently orchestrate engagement while your competitors are still teaching their agents how to guess.

Your AI needs context

The goal of any agentic AI customer engagement workflow is to move beyond generic automation and toward true situational awareness with 1:1 personalization. In the high-stakes environment of modern commerce, generic anything—whether it is messaging, targeting, or personalization—isn’t just inefficient, it’s a campaign killer. If your AI cannot distinguish between a price-sensitive churn risk and a financing-ready buyer, it will default to a “middle-of-the-road” response that resonates with no one.

Context is the guiding light that turns a cold interaction into a warm conversion. It is the vital distinction between having a list of names and possessing the clarity to know exactly who a prospect is, and how to best engage them. Grounded with this context, the model sees that a specific visitor isn't just a generic web session, but a high-intent lead with a unique persona and a high predicted lifetime value: giving you the clarity to act.

The context budget

But there is a catch. Once you solve the data scarcity problem, you’ve got a new one: too much data increases entropy instead of reducing uncertainty.

When AI underperforms, the natural instinct for many is to "feed the beast" by dumping in every available row of first-party data or loosely stitched third-party attributes; however, this approach hits a hard technical ceiling: the context budget.

Every AI model has a limited attention window. If you flood that window with uncurated information, the agent wastes its processing power on noise rather than signal and starts to hallucinate. What’s worse, even if the AI is not overwhelmed, this internal or uncurated 3rd party data likely lacks the specific answers the AI actually needs to answer the questions that actually drive value. Things like:

• Who is this person?
• Are they a good fit for my brand?
• What’s the best next action to ensure conversion and maximize value?

Think about it like this: if you’re trying to decide whether to market a home renovation project to someone, is knowing their blog click history or whether they have a dog or a cat really helpful? No. You need to know whether they own their home or rent it—and whether they’re likely to have the means and intent to take on a renovation.

Faraday’s context makes your AI better

To succeed, your systems need a complete picture of the consumer, but they also must operate within strict processing limits.

Faraday balances this equation by judiciously infusing your agentic workflows with context. We curate and deliver the deep external knowledge your agents need to thrive. We allow you to solve the context gap while simultaneously compressing that complexity into clean, clarity-driving signals that don’t overwhelm your context budget. We understand how agents operate, so we provide specific context they actually need—tailored to your industry and business goals.

At Faraday, we offer two kinds of context for AI agents:

• Consumer data: We close the "context gap" with carefully curated external details missing in your internal systems. From financial health to lifestyle details and shopping habits, this data helps the agent understand the human behind the screen.
• Predictive intelligence: We distill thousands of signals into clear guidance. Instead of flooding the agent with a tempting smorgasbord of potentially irrelevant details, we use traditional machine learning techniques to boil it all down to predictive indicators like "churn risk," "purchase likelihood.," or “next best offer.”

Finding the right blend of context—one that maximizes lift without overwhelming the context budget—is an art in itself, but Faraday includes analytics and tooling to help you make an informed decision.

Better context, better outcomes

This isn't just a technical theory; we are seeing it work in the wild right now.

Take Hazel, an agentic marketing platform that provides brands with an "AI coworker" to analyze market trends and customer behavior. To fulfill that promise, Hazel’s agent needs to know more than just what a customer bought yesterday; it needs to understand who that customer is outside of the transaction: their demographics, lifestyle, and purchasing power.

Hazel faced a classic "cold start" problem, and traditional data brokers demanded six-figure upfront fees and multi-month engineering projects just to get access to the data they needed. So instead, Hazel integrated with Faraday. By plugging into our platform, they started receiving consumer data from the Faraday Identity Graph immediately, via API and MCP. And this data made a real impact:

• Speed to value: They reduced a potential multi-month build-out to a single day of prototyping.
• Cost efficiency: They eliminated over $100k in upfront data licensing fees, shifting to a usage-based model that scaled with their growth.
• Privacy-first: They gained a built-in privacy compliance infrastructure, allowing their team to focus on agent logic rather than regulatory hurdles (such as building a system to handle data access and deletion requests).

The result? Hazel’s agent now has instant, privacy-safe access to rich consumer context, allowing it to make optimal decisions for their clients without the engineering overhead. This is the blueprint for the next generation of software: specialized agents running on specialized context.

Context and AI: fuel for the engine

If the rest of the AI industry is building the "engine"—the LLMs and autonomous agents—Faraday provides the fuel. An engine without fuel is just a heavy piece of hardware; an AI model without context is just a convenient way to deliver the wrong message.

As we move deeper into the agentic era, the distinction between winners and losers won't be defined by who has the smartest model, but by who has the most relevant context to feed them.

Ready to power your agents with the context they need? Let’s talk.