If you frequent our blog (firstly, thank you), you already know that Faraday loves data, transparency, and [explainability](https://faraday.ai/blog/faraday-score-explainability). These ideas are at the center of our modeling and predictive agents—but it doesn't stop there. That emphasis permeates every aspect of our business.

So when we talk about return on investment (ROI), we’re not just talking about showing a nice number at the end of your campaign. We’re talking about helping you actually understand how Faraday is generating value for your business—not just the number, but where it came from, what influenced it, and how it can best inform your next move.

That’s actually why Faraday hired me.

Hi—I’m [Chris Waite](https://www.linkedin.com/in/christopher-waite-b3548015/), and I’m a data nerd who specializes in evaluating ROI for all our clients. My role is to make sure the value Faraday drives for your business is accurate, understandable, and, most importantly, guiding you towards further insights.

At Faraday, we don’t think of ROI as a final number you get at the end of a campaign. We treat it as a living, evolving data point—a signal that tells a story over time. And just like with any good story, you should be able to follow the plot. My job is to make sure you can.

ROI isn’t only about justifying past spend—it’s about helping you make better, faster, and more confident decisions moving forward. Knowing how things went is good… But knowing where to go next? Even better.

## ROI isn’t a score—it’s a strategy

A lot of companies say they help you track ROI—but what that actually looks like in practice can vary *a lot*.

Some treat ROI like a final grade. You get a slide at the end of a campaign with a nice round number and a chart pointing up and to the right. Others hand you a projection during onboarding that’s... let’s just say, aspirational. It’s a best-case scenario, not a reflection of what’s actually happening in your campaign.

At Faraday, we think that misses the point.

We treat ROI as a process, not a product. The real value of ROI isn’t just in a number at the end—it’s in what you learn along the way.

Done right, ROI helps you:

* Identify what’s working and what’s not  
* Catch issues and optimize while the campaign is live  
* Challenge assumptions you didn’t even realize were there  
* Build confidence in your strategy—not just in hindsight, but in real time

Because in a landscape where budgets are under pressure and every dollar has to count, ROI isn’t just about proving value. It’s about unlocking more of it.

## What to expect from Faraday’s ROI assessment process

Faraday takes ROI personally. Not just because it proves our value, but because—done right—it helps you uncover where your growth really comes from. And that starts long before the first campaign goes live.

When you work with Faraday, ROI isn’t an afterthought or a once-a-quarter deliverable. It’s built into the process from day one. Here’s what that looks like in practice:

#### We start measuring early

ROI modeling is part of onboarding—not something we tack on at the end. From the beginning, we’re thinking about what success looks like and how we’ll measure it.

#### We define assumptions together

ROI analysis is only as good as the logic behind it. We work closely with clients to make sure the “what if” scenarios we’re comparing against are grounded in data, reasonable, and transparent.

#### We track and adjust in real time

Our goal isn’t just to see if something worked—it’s to learn what’s working *while* it’s happening, so we can adapt. Think of ROI as a feedback loop, not a final grade.

#### We keep you involved

You won’t just get a chart in a deck and a polite “any questions?” We walk through the analysis with you, answer your questions, and collaborate on what the numbers actually mean for your business.

We know not every client comes in with a fully built-out internal analytics team. That’s okay. Whether you’ve got a room full of data analysts or none at all, we meet you where you are. Our job is to make ROI not just accurate, but clear and actionable—so you can make smarter, faster decisions with confidence.

## So what’s next

You might’ve noticed this post is the first in a new series covering all aspects of how Faraday approaches ROI—not just as a reporting tool, but as a driver of real, measurable value.

So what can you expect?

We’ll be diving into real client examples where ROI analysis didn’t just prove our impact—it helped uncover new opportunities and improve campaign performance. We’ll also get into the weeds with technical posts that break down the actual methods I use to assess ROI, from modeling assumptions to attribution logic and more.

In our next post, we’ll share a case study that shows how one client’s ROI review sparked new strategic decisions and unexpected insight.

Because if you’re investing in growth, you deserve more than a black-box metric. You deserve to know what’s working, why it’s working, and how to keep improving.

Let’s make that the standard. We can’t wait to see you here for the next one.

Faraday treats ROI not as a final score but as an ongoing strategy—helping clients understand, optimize, and expand on the value generating strategies we're driving throughout the entire campaign lifecycle.

ROI isn’t a scorecard—it’s a strategy

So, you’ve signed up for Faraday’s lead rejection. Great choice—your bottom line will thank you.

Our [lead rejection](https://faraday.ai/templates/lead-rejection) helps customers buy smarter, not necessarily buy less. With [AI agent](https://faraday.ai/templates)\-powered filtering based on real conversion data, you're now able to pass on leads that aren't likely to perform—and reinvest in the ones that are. It’s a win for your marketing efficiency, your conversion rate, and your budget. But if you're working with third-party lead sellers, they might have questions when they see the change go into effect. That’s totally fair—and totally manageable. 

Because we’re all about making this process as smooth as possible, we’ve also thought about the vendor side of the equation. So to keep things streamlined from end to end, we’ve pulled together a few talking points you can use if (or when) they ask what’s changed. Think of it as your lead vendor cheat sheet: no awkward conversations, just clear communication.

[![Parks and Rec meme about awkward conversation](/images/blog/parksandrec-annperkins.gif)](/images/blog/parksandrec-annperkins.gif)

## Talking points to use with lead sellers

1. **We’re using AI agents to improve our lead buying.** Machine learning models help us predict which leads are most likely to convert, based on real conversion data.  
2. **This helps us buy more effectively, not necessarily buy less.** We’re reallocating the budget from lower-probability leads to higher-probability ones so we’re still investing in leads, just more strategically.  
3. **Stronger conversion rates mean a stronger partnership.** By improving our success rate, we can continue to grow and buy more leads from you over time.  
4. **If we pass on certain leads, it’s about efficiency, not cutting spend.** Our goal is to optimize performance, which ultimately benefits both of us.  
5. **Better results for us mean more long-term business for you.** The more effective our lead buying is, the more we can reinvest in your leads and scale our partnership.   
6. [**We can share insights on which attributes make a good lead.**](https://faraday.ai/blog/faraday-score-explainability) If you are looking to improve your sourcing strategies, we can share details on the top attributes we see as having positive or negative correlation with a lead converting.

## In conclusion

Making the switch to smarter lead buying doesn’t have to mean friction with your partners. In fact, it’s an opportunity to build stronger, more aligned relationships based on performance and transparency.

If you ever need help communicating with vendors—or want to dig into the data behind what’s working—we’ve got your back. The goal is simple: better leads, better results, better outcomes for everyone involved.

Please reach out if you have any other questions. We’d love to hear from you\!

After signing up for Faraday, your lead vendors might notice a change in your bidding habits. Here are some talking points to facilitate the conversation.

Talking to your lead vendors about AI-driven lead optimization

At Faraday, we talk a lot about [Forward-Deployed Data Science (FDS)](https://faraday.ai/blog/FDS-changes-the-game)—our team of data scientists who work directly with clients to build predictive models, validate results, and ensure you get the most out of AI agents. This service (although new) has already proven invaluable to our clients. It means that they get not only accurate predictions, but comprehensive strategic plans to ensure that our agents actually drive business value.

But data science is only half of the equation. The best predictive AI agents also require another type of data specialist to operate at maximum efficiency: **Data Engineers.** 

And just like our data science team, Faraday’s data engineers don’t disappear behind the curtain—as is often the case at our competitors. Instead they work directly with clients, in a “forward-deployed” capacity. They’re the ones helping you format and structure your data correctly, making sure your onboarding process is as smooth as possible, and reducing or resolving errors  to ensure your pipeline stays live and healthy. If you’ve ever submitted a dataset and gotten feedback on how to improve it, or had someone help troubleshoot an ingestion issue, you’ve already worked with our data engineering team.

Take [Chris Thomas](https://www.linkedin.com/in/krys-tomis/), one of our incredible data engineers, [who coincidentally just wrote a whole blog about how to best avoid pitfalls while importing your data into Faraday](https://faraday.ai/blog/ingesting-data-pt2). His job—and the job of all our data engineers—is to make sure your data is clean, structured, and ready for predictive modeling. And in just the last weeks he’s helped numerous clients avoid pitfalls that could have set their timelines back by months—thanks Chris\!

So what’s the difference between data science and data engineering, and how do both help you get the most out of Faraday? Let’s break it down like Raygun\!

[![Raygun break dancing](/images/blog/raygun.gif)](/images/blog/raygun.gif)

### Data science: turning data into predictions

Forward-Deployed Data Scientists (FDS) focus on understanding your business goals and applying AI to help you achieve them. They understand the nuances and behaviors of the predictive AI agents that power our business, but also can provide strategic advice on the implementation of agents that ensures that your predictions drive value. 

Their job is to help you:

* **Define and refine your use case**—what do you want to predict? How can AI drive better decisions for your team?
* **Data discovery and solicitation**–what data is needed for the use case.
* **Build predictive models** that identify key audiences, behaviors, and trends.
* **Validate model performance** to ensure accuracy and impact before deployment.
* **Interpret and apply insights** so you can confidently take action based on data-driven predictions.

For example, if you’re a solar company looking to expand into new markets, an FDS won’t just give you a model—they’ll help you determine the right questions to ask in the first place. How do we ensure that the data used for training isn’t biased by work in existing markets? What is the logical cut off for differentiating promising new markets from a likely low performing market? Which of our [powerful AI agents](https://faraday.ai/templates) can increase your conversion rates the fastest? By framing the problem strategically, they ensure that your predictions are aligned with real business objectives—helping you make informed decisions that drive sales, optimize marketing spend, and accelerate growth.

### Data engineering: optimizing your data and models

Data Engineers on the other hand focus on the infrastructure that makes data science possible, while working directly with you to ensure your data is in top shape. 

Their job is to help you:

* **Structure your data correctly** before ingestion so it matches well against the [Faraday Identity Graph](https://faraday.ai/features/faraday-identity-graph) (FIG).  

* **Troubleshoot data issues** so you don’t lose time on formatting errors.  

* **Automate through properly configured integration** so your data flows seamlessly into and out of Faraday without constant manual updates.

Where data science can be viewed as more strategic and high-level—focusing on where you want to go, why, and how it will affect your business—data engineering is tactical, focused on how to get there efficiently. Engineers ask questions like, “How can we best implement this idea?” or “What changes will make this process more efficient?” 

At Faraday, a Forward-Deployed Engineer (FDE) like Chris undertakes tasks like supporting integrations with your data warehouse, reviewing your data during ingress, flagging potential issues, and suggesting optimizations to improve matching and model performance. 

### At Faraday, you benefit from both types of data experts

We don’t offer both Forward-Deployed Data Science and Forward-Deployed Data Engineering just because it sounds nice (although doesn’t it?)—we do it because it’s the fastest way to get actionable insights into our clients’ hands.

Making data engineering a client-facing function is a huge step that moves the process forward, ensuring that costly holdups and mistakes don’t happen before your predictive models even have a chance to work and that they keep working at peak efficiency after deployment. 

Here’s what that means for you:

🤔 **No more guessing if your data is good enough.** FDEs provide hands-on support before and during ingestion, catching issues early so they don’t slow you down later. After deployment they’re the ones making sure your models stay healthy.

⏳ **Faster time to value.** Instead of back-and-forth troubleshooting that delays model deployment, our FDEs work with you upfront to get data right the first time.

📊 **Stronger, more accurate predictions.** The best AI models in the world can’t fix bad data. By optimizing your data at the start, we ensure your models perform at their peak.

💰 **Fewer costly mistakes.** If your data isn’t structured correctly, you could end up with mismatched records, poor model performance, or wasted budget. FDEs help prevent that before it happens.

🤝 **A seamless experience.** Whether you’re working with an FDS to interpret predictions or an FDE to streamline integrations, you have direct support from an expert who understands your business.

In short, data science helps you make smarter decisions, and data engineering ensures your data is ready to support them. While forward-deploying both roles isn’t yet standard practice in the industry, we believe transparency, explainability, and speed to deployment aren’t just nice to have—they’re essential. And we’re not in the habit of waiting for the industry to catch up.

Our clients see the impact firsthand. Numerous businesses have leveraged our forward-deployed approach to streamline their data, improve model performance, and drive real results. You can explore a few of their [success stories here](https://faraday.ai/stories).

If you’re already working with Faraday, you know what this means for your business and bottom line. And if you’re not—shouldn’t you be?

Let’s chat about how we can help you put your data to work.

Faraday’s forward-deployed data scientists and engineers work directly with clients to ensure clean, structured data and precise predictive models—maximizing accuracy, efficiency, and business impact.

Data science + data engineering: How Faraday’s forward-deployed team fulfills all your data needs

In today’s fast-paced, data-driven world, many businesses struggle to harness the full potential of predictive analytics. From disjointed data systems to resource constraints, the complexities of implementing machine learning in-house can be overwhelming. At Faraday, we’ve developed a solution to this challenge: forward-deployed data science. Making our experts directly accessible to your team, we make advanced analytics actionable, driving measurable business growth and transforming how you make decisions. 

This approach has been so impactful that even industry giants like [OpenAI](https://openai.com/) have since adopted similar models, recognizing the value of integrating specialized expertise directly into client operations. Here's how this innovative model is changing the game for our clients.

## The power of embedded expertise

Forward-deployed data science means our experts work closely with clients—integrating directly into their existing workflows, providing hands-on support, and ensuring that predictive models are not just built, but actively refined and applied to achieve business goals. Rather than a one-size-fits-all solution, this approach allows us to tailor insights and strategies that align with each client’s unique needs, systems, and data environments.

By embedding data science expertise directly into our clients’ operations, we eliminate bottlenecks, accelerate implementation, and ensure that predictive models are continuously optimized. This means businesses don’t need to hire and train an in-house team or struggle with the complexity of data preparation, model iteration, and deployment on their own—just to get started. Instead, they gain a direct pipeline to experts who can streamline the entire process.

[![Image of Faraday data science team](/images/blog/FDS-team.png)](/images/blog/FDS-team.png)
_Some of Faraday’s wonderful data science team_

## What forward-deployed data science enables

By taking a hands-on approach, our data science team enables businesses to:

* **Ensure data readiness:** Clean, structured, and well-prepared data is the foundation of any successful predictive model. Our team assists in transforming disparate data sources into a cohesive, usable format—eliminating inconsistencies, standardizing fields, and enriching datasets where needed.  
* **Optimize predictive models:** Many organizations have valuable data but lack the resources to develop, test, and refine predictive models. Our experts work iteratively to build, optimize, and validate models, ensuring that they accurately reflect real-world business scenarios and drive meaningful outcomes.  
* **Drive smarter marketing & outreach:** Whether through digital channels or direct mail, predictive analytics can significantly improve targeting and campaign efficiency. Our data scientists help businesses identify the most valuable audience segments, test and optimize strategies, and measure ROI with precision.  
* **Streamline data science without internal constraints:** Companies without a dedicated data science team—or those with limited capacity—can now access cutting-edge predictive modeling without the burden of managing the complexities themselves. Our team handles the heavy lifting, allowing businesses to focus on execution and strategy.

## Real clients, real wins

This sounds great in the abstract, but how does it look in practice?

Well, for one of our clients, a national financial services brand looking to optimize its direct mail program through lead suppression, the wins have already been rolling in thanks to our forward-deployed data science program. Let’s unpack how:

#### **Refining lead suppression strategies** 

At Faraday, good enough is never good enough. That’s why, even after a strong initial ROI showing, we’ve continued to work closely with this client. Our ongoing partnership means updating models weekly with fresh data to provide the most accurate and actionable lead cutoff recommendations. This constant refinement ensures we only suppress the least-likely-to-convert individuals, saving money on poor-fit leads while retaining valuable opportunities that might otherwise fall below a less precise cutoff.

#### **Turning predictions into strategy** 

Our partnership is about more than just predictions—it’s about using them to drive real value. From day one, we’ve collaborated to ensure that this client understands how to strategically choose the best models for their needs and the right data to fulfill their business objectives. 

Because, after all, our models (and other predictive models for that matter) are extremely powerful, but their true value is only realized when you know how to leverage them effectively. At Faraday, you get both the models and the expertise. And through our mentorship, clients like this financial services provider are empowered to align predictive insights with their unique goals, ultimately maximizing their ROI.

## The impact on businesses

The move to forward-deploy our data science team is already proving to be a major advantage for our clients. Businesses that once struggled with fragmented data, inefficient modeling processes, and underperforming marketing initiatives now have a seamless way to integrate predictive insights into their decision-making. With ongoing expert support, they can adapt models to shifting market conditions, validate results with confidence, and unlock new growth opportunities.

At Faraday, we believe that data science should be accessible, effective, and deeply embedded in how businesses operate. By bringing our expertise directly to our clients, we’re ensuring that predictive analytics isn’t just a tool—but a true competitive advantage.

Ready to explore how forward-deployed data science can transform your business? [Let’s talk](https://faraday.ai/talk-to-sales).

Forward-deployed data science at Faraday embeds expert support directly into client operations, ensuring predictions are actionable, continuously optimized, and drive measurable business growth.

How forward-deployed data science drives real business outcomes for Faraday’s clients

A lot of our blogs end with a similar CTA: “Want to get started for free? [Sign up today](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)\!” 

And indeed, if you're looking to get started with Faraday, the first step is to create an account. 

But how exactly does that process work? Whether you're new to predictive AI or just eager to see what Faraday has to offer, this guide will walk you through the steps so you can quickly gain access to our powerful platform.

### Step 1: Visit faraday.ai

To begin, navigate to [Faraday’s website](https://faraday.ai). This is the official platform where you can sign up and explore our predictive AI solutions.

### Step 2: Click on "Sign up for free button"

Once you're on the homepage, look for the purple, "[sign up for free button](https://login.faraday.ai/u/signup?state=hKFo2SBTU2FQa0dIbmg4QzBfV0kwTDczbXdiVHc5Wm1mOVB5TqFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIHk0S3JUSGlUMExSSFVPejduNDdBQnpFeHBjZVVjN00xo2NpZNkgMDF6SWFodVNaV203SmRVMFNtWmVNUHMzZ2ZHcklqWTI)" button in the top-right corner. Click on it to begin the registration process.

[![Sign up button location](/images/blog/signingup-with-fdy-blog/sign-up-button.png)](/images/blog/signingup-with-fdy-blog/sign-up-button.png)


### Step 3: Enter your information

You'll be prompted to enter some basic details, including:

* Your full name  
* Work email address  
* Company name  
* Password (ensure it meets security requirements, we do love our security)

[![Dashboard view](/images/blog/signingup-with-fdy-blog/integrations.png)](/images/blog/signingup-with-fdy-blog/integrations.png)

### Step 4: Verify your email

After submitting your details, you'll receive a verification email. Open your inbox and click the confirmation link to activate your account. If you don’t see the email, check your spam folder or request another verification email.

### Step 5: Log in & get started

Once your email is verified, return to [Faraday’s login page](https://faraday.ai/login), enter your credentials, and start exploring the platform. 

At this point you’ll find yourself on our Dashboard (although all this information can also be accessed through our API).

[![Integrations landing page](/images/blog/signingup-with-fdy-blog/dashboard.png)](/images/blog/signingup-with-fdy-blog/dashboard.png)

### Step 6: Add your data

Now that you're in the Faraday app, the next step is to add your customer data. A great starting point is uploading transaction data, which allows you to generate customer-focused predictions. The easiest way to do this is by using a CSV file exported from your data infrastructure. Ideally, this file would include first name, last name, address, and phone number to facilitate matching but give us what you have and we’ll work from there.

Simply upload your file, map the relevant fields, and let Faraday begin building your transaction event stream in the background.
Most users eventually connect directly to their data warehouses or cloud storage for automated data ingestion—learn more about that in our [documentation](https://faraday.ai/docs/how-to/streams/transaction). Once your data is in, you're ready to start making predictions and uncovering valuable insights\!

### Step 7: Explore agents and integrations

Now that you have access to Faraday, take some time to explore how you can apply our powerful predictive models to your business case. Browse our pre-built [templates](https://faraday.ai/templates) (or AI agents) to see how Faraday can help you make smarter decisions with AI-driven insights.

Additionally, check out our wide range of [integrations](https://faraday.ai/integrations) that seamlessly connect with your existing tech stack. Whether you want to add a decisioning layer to your data warehouse (like [Snowflake](https://faraday.ai/integrations/predictions-with-snowflake)) or send predictions directly to your sales and advertising platforms like [Shopify](https://faraday.ai/integrations/predictions-with-shopify) or [Google Ads](https://faraday.ai/integrations/predictions-with-google-ads), Faraday makes it easy to integrate predictive AI into your workflow.

![][image4]

## More things to do

### Meet the Faraday team

But our technology isn’t our only core differentiator, another one is our people. Our data science team is forward-deployed, ensuring that you get hands-on expertise to tailor AI solutions to your needs. And our sales team? They’re truly the best in the business. Exploring the platform on your own is great, but if you really want the lowdown on Faraday, reach out to talk to Dave, Nate, Charles, or any of our other incredible team members who are ready to help.

### Learn about the Faraday Identity Graph

At the heart of our predictive AI is the [Faraday Identity Graph](https://faraday.ai/docs/abstractions/traits)—a robust dataset containing over [1,500 rich attributes and insights on nearly every U.S. consumer and household](https://faraday.ai/features/faraday-identity-graph). This consented third-party dataset includes the demographic, behavioral, and geographic information you need to push your models to the next level. Whether you're looking to enhance your predictions or simply enrich your data with appends, the Faraday Identity Graph is here to help.

### Troubleshooting & FAQs

**Q: I didn’t receive a verification email. What should I do?** A: Check your spam folder or click on "Resend Verification Email" from the sign-up page.

**Q: Can I use a personal email to sign up?** A: Faraday is designed for businesses, so we recommend using a work email for a smoother experience.

**Q: Where can I get help if I run into issues?** A: Visit our [support page](https://faraday.ai/support) or contact our team at support@faraday.ai.

### Get started today\!

Creating a Faraday account is quick and easy. Follow these steps to get started, or if you'd rather talk to someone, say hi to [Nate](https://www.linkedin.com/in/nathan-novotny-3484227a/)!

This guide walks you through the simple steps to sign up for Faraday, add your data, explore AI-driven insights, and integrate predictive models into your workflow.

How to create a Faraday account: A step-by-step guide

You know how valuable predictive analytics could be to your business, but you haven’t signed up yet. So, what’s holding you back?

For many businesses, and especially those in notably security-conscious industries like healthcare and finance, data security is the biggest concern. You want the insights that AI and predictive analytics can provide, but you need to know your customer data is safe. At Faraday, we understand that concern. That's why we’ve built our entire platform around safeguarding your sensitive data, from collection to analysis.

In this blog, we’ll walk you through the steps we take to ensure your data remains secure, private, and ethically handled, so you can trust us with the insights that power your business growth. But first, we want to talk about our Faraday Identity Graph and why we take our security so seriously.

### The Faraday Identity Graph, and why we take privacy so seriously

The [Faraday Identity Graph (FIG)](https://faraday.ai/docs/abstractions/traits) is at the core of everything we do. It’s a powerful tool that [allows us to generate accurate, actionable insights from U.S. consumer data](https://faraday.ai/blog/how-faraday-enriches-first-party-data-with-fig), helping brands understand their customers at a deeper level (we also sell this data with [data appends](https://faraday.ai/blog/data-appends-blog)). But with great power comes great responsibility. We recognize that the data we work with is highly sensitive, and we take every precaution to handle it with the utmost care.

FIG isn’t only concerned with unlocking insights; it’s also designed with privacy as a top priority. [From the very beginning, we’ve built it with robust security features to ensure that consumer data is kept safe and secure.](https://faraday.ai/security) Whether it’s protecting personal information or ensuring compliance with privacy laws, our mission is to uphold the trust you place in us by following the strictest data handling practices.

So let’s start at the top and take a look at some of the foundational security practices we follow to safeguard your data.

### SOC 2 compliance

[SOC 2 (System and Organization Controls 2\)](https://secureframe.com/hub/soc-2/what-is-soc-2) is a security framework designed to ensure that organizations protect customer data from unauthorized access and security incidents, and is often viewed as the gold standard for this purpose. It evaluates five key areas: security, availability, processing integrity, confidentiality, and privacy.

At Faraday our compliance with SOC 2 compliance standards means we undergo rigorous audits of our security practices every year, with an independent third party verifying that our data protection measures meet the highest industry standards.

Our security policy is built around important principles like the "principle of least privilege" (where users only have access to the data they absolutely need), prevention of supply chain attacks, and strict access controls. We also participate in  [SOC 2 Type II](https://secureframe.com/hub/soc-2/type-1-vs-type-2) audits, which not only check that security controls are in place but also assess their effectiveness over time. This ongoing process ensures that we continuously meet top-tier security protocols and maintain a secure environment for your data.

### Compliance with privacy regulations

To protect your consumer data, we comply with key privacy regulations, including [HIPAA](https://www.cdc.gov/phlp/php/resources/health-insurance-portability-and-accountability-act-of-1996-hipaa.html), [CCPA](https://oag.ca.gov/privacy/ccpa), and [GDPR](https://gdpr-info.eu/), to ensure your data is secure and handled responsibly.

For CCPA and other state privacy laws, we offer an easy opt-out system, allowing individuals to remove themselves from our datasets. What’s more, we extend this opt-out through a reciprocal process to our vendors, so when either Faraday or a vendor receives an opt-out request, the data is removed from both systems—ensuring full compliance across our ecosystem.

As for GDPR, since our Faraday Identity Graph focuses on U.S. data, we proactively filter out non-U.S. data at the "perimeter," ensuring we only process data we are authorized to use.

When handling Client data, Faraday operates as a Data Processor/Service Provider, meaning we will only use client data for the specific contracted purpose and will support our client’s own compliance with state data privacy laws. 

### Data vendor standards: permissioned and ethically distributed 

Ensuring responsible data handling goes beyond secure storage and processing—it also means sourcing data from reputable vendors. At Faraday, we take extra care in selecting the data providers we work with.

We only collaborate with vendors who explicitly affirm in their terms of service that their data is properly permissioned, legally obtained, and authorized for use and distribution. We are committed to maintaining the highest standards of ethical data usage, ensuring that every piece of data we use aligns with the proper permissions and legal requirements so you can confidently leverage accurate, responsible insights.

### Addressing bias in consumer data

As a data provider, Faraday acknowledges the reality that consumer data is inherently shaped by [historical and systemic biases](https://developers.google.com/machine-learning/crash-course/fairness/types-of-bias). We acknowledge these biases but take active steps to mitigate their impact on our models and predictions.

For example, mortgage lending and credit scoring have historically exhibited bias, with certain demographic groups, particularly minorities and women, facing higher rejection rates or higher interest rates due to systemic biases embedded in financial models. These models often relied on historical data that reflected past discriminatory lending practices rather than objective risk. 

To avoid perpetuating these systemic biases, Faraday takes a range of actions. For instance, we normalize data to reduce geographic disparities and offer bias detection and mitigation strategies to address disparities along sensitive dimensions such as age and gender. 

Responsible AI and ethical data practices are at the forefront of our methodology, ensuring that our models and insights promote fairness and inclusivity.

### Secure data handling practices

Faraday accepts data exclusively through secure channels—no emailed files or unprotected transfers. We also support hashed data ingestion and egress, allowing brands to share encrypted email addresses and phone numbers for added security. While hashing is not a foolproof privacy solution, it remains a valuable safeguard, and we ensure it is an option for those who prioritize additional data protection.

### Customer data is never shared between clients

One of the most common questions we receive is whether we share customer data across different client accounts. The answer is unequivocally no. Our terms and data processing agreement (DPA) explicitly state that client data is used solely for delivering services to that client, and it is never shared, pooled, or combined with other clients' data in any way. We believe that maintaining clear, stringent data boundaries is essential for protecting client trust and upholding our ethical standards.

### Conclusion: a commitment to ethical data handling and security

At Faraday, we don’t just meet industry standards—we set them. From rigorous security audits to responsible data sourcing and compliance with privacy regulations, we prioritize the safety, privacy, and ethical handling of your data at every step. Our commitment to transparency and secure practices ensures that your customer data is protected, and our proactive efforts to address biases help foster fairness in every insight we generate.

By choosing Faraday, you’re not just getting advanced predictive analytics—you’re gaining a trusted partner dedicated to upholding the highest standards of data security and privacy. We believe that responsible data use is the foundation of meaningful insights and lasting business success.

If you have any questions about our data security policies or compliance measures, don’t hesitate to reach out. We're here to help you navigate the complexities of data protection, so you can focus on what truly matters—growing your business with confidence.

Safe and responsible handling of data is a primary priority at Faraday, but what does that really mean? In this blog, we'll unpack some of the ways we ensure your data remains secure, private, and ethically managed throughout our platform.

How Faraday ensures secure and responsible customer data handling

*Note to reader: The following is the perspective of a lowly marketer who has followed these debates closely, but it’s worth noting that these discussions have passed the desks of far more knowledgeable people. So if you want to know what [Spinoza](https://homepages.uc.edu/~martinj/Spinoza_&_Hobbes/English/Spinoza%20-%20Complete%20Works%20\(2002\).pdf) and [Descartes](https://yale.learningu.org/download/041e9642-df02-4eed-a895-70e472df2ca4/H2665_Descartes%27%20Meditations.pdf) really said, read their work\!*

Imagine a world where a computer can write a poem that ‘rivals’ Shakespeare, but struggles to understand the simple concept of a joke. This is the paradoxical reality we often find ourselves in when working with large language models (LLMs) like [OpenAI’s](https://openai.com/) [GPT-4](https://openai.com/index/gpt-4/). 

We’re all hearing a lot about LLMs—AI systems that generate text, answer questions, and even compose blogs (although we promise this one comes from a certified [real boy](https://giphy.com/gifs/uyG3UwkbgTaOevKe9C)). These models are rapidly reshaping workflows across industries, automating tasks that once required human effort—from customer support to legal document review to coding assistance. But as they become more embedded in our work and lives, it’s worth asking: Are they truly intelligent? And, more importantly, what do we even mean by ‘intelligence’ when it comes to machines?

To unpack this question, let’s compare two different popular approaches to AI: **conversational** and **predictive**. While both are powerful, they operate in very different ways. Let’s take a closer look at how Conversational AI like GPT-4 works and how it compares to Predictive AI like [Faraday](http://Faraday.ai).

### Conversational AI vs. Predictive AI

[Conversational AI](https://cloud.google.com/conversational-ai?hl=en), like GPT-4, excels at generating human-like text. Trained on vast amounts of data, it learns patterns in language by analyzing how words and phrases relate to each other across different contexts. At its core, it predicts the most statistically likely next word in a sequence—similar to the predictive text on your phone’s keyboard, but on a much grander scale. This allows it to mimic conversation convincingly, but it doesn’t necessarily understand what it’s saying. It can generate fluent responses, but it struggles with complex logic, [math](https://garymarcus.substack.com/p/math-is-hard-if-you-are-an-llm-and), or real-world reasoning, which requires understanding beyond mere pattern recognition.

Think of it like a high-speed version of Mad Libs. While it can generate coherent and even insightful responses, it doesn’t understand the meaning behind them—it’s simply identifying statistical patterns and filling in the blanks. On that basis, one could argue that its intelligence is primarily performative—more like an advanced pattern recognition game than genuine comprehension or thought. 

Of course, this raises a deeper question: What actually counts as thought? Philosophers, cognitive scientists, and AI researchers have debated this for decades, and we’re not here to settle it today. But for the sake of this discussion, let’s focus on the practical distinction between AI that emulates behavior and AI that augments decision-making.

[Predictive AI](https://faraday.ai/templates) (like [Faraday](http://faraday.ai)), on the other hand, isn’t built to simulate conversation. Instead, it’s designed to analyze complex patterns in structured data and forecast outcomes, helping humans to make smarter, faster decisions. While Conversational AI generates responses based on linguistic patterns, Predictive AI identifies meaningful relationships between behaviors, demographics, and other variables—surfacing insights that would be difficult or impossible to detect manually. In that sense, Conversational AI is designed to  *sound human*, whereas Predictive AI is designed to *enhance human decision-making* by providing precise, data-backed foresight.

Let’s unpack how predictive AI works with a parallel from science fiction.

### A mathematical model for predicting the future

In Isaac [Asimov’s 1940s Foundation series](https://en.wikipedia.org/wiki/Foundation), mathematician Hari Seldon develops a science called psychohistory—[a statistical method capable of predicting the future.](https://media.tenor.com/TrAsjYbL720AAAAM/chris-pratt-wow.gif) Psychohistory doesn’t predict individual actions but instead foresees large-scale societal trends with remarkable accuracy, enabling leaders to anticipate and alter the course of history.

If you’re already using Faraday, you probably see a connection here. Like psychohistory, predictive AI helps businesses affect the future by anticipating customer behaviors. By analyzing vast amounts of data, predictive AI can forecast outcomes such as which leads are most likely to convert or what offer will resonate with a particular customer. This ability empowers businesses to make precise, data-driven decisions that can directly influence future outcomes.

But unlike psychohistory, which only understood the broad movements of society, predictive AI is powerful enough to make accurate, actionable predictions at the individual level. And in this sense **our product actually beats a system designed to save the galaxy**  (sorry Hari)\!

### Turing, Descartes, and machines that learn

But we came here to talk about intelligence. 

And when discussing intelligent machines, our minds often turn to the famous, controversial, and (critically) often misunderstood [Turing Test](https://www.techtarget.com/searchenterpriseai/definition/Turing-test), a benchmark proposed by the great [Alan Turing](https://en.wikipedia.org/wiki/Alan_Turing) to measure whether a machine can exhibit behavior indistinguishable from human intelligence. 

“So it *basically* tested if the machines were sentient right?”

Well, not exactly. Turing himself viewed the test as more of an “[imitation game](https://plato.stanford.edu/entries/turing-test/)” than a definitive way to assess whether a machine truly “thinks”. His focus was less on the idea of thought itself, and more on the appearance of it—the ability of a machine to convincingly mimic human behavior and language.

The goal of the Turing Test wasn’t to prove that a machine could reason or understand in the way humans do, but rather to test if it could generate responses so human-like that an observer might be fooled into believing it was also actually a person. Passing the test would mean a machine could emulate the behaviors and language associated with human reasoning, but not necessarily that it possesses any true understanding behind them.

This idea interacts interestingly with the philosophy of [René Descartes](https://plato.stanford.edu/entries/descartes/), who famously said “[I think, therefore I am](https://www.britannica.com/topic/cogito-ergo-sum)”. Descartes believed  true human intelligence was defined by the ability to reason and, importantly, to use language to express reason. For him, language wasn’t just a tool for communication—it was the ultimate marker of conscious thought. He argued that only beings capable of reason, expressed through language, could be considered truly conscious. In this context, Descartes viewed machines and (sadly) animals, as [Automata](https://academic.oup.com/book/32221/chapter-abstract/268391060?redirectedFrom=fulltext)—[sophisticated mechanical systems that operated according to predetermined laws](https://www.amazon.com/Top-Race-Robot-Dog-Programmable/dp/B08G6PT5DC?source=ps-sl-shoppingads-lpcontext&ref_=fplfs&psc=1&smid=A17FAJD38FG3OS&gQT=1), but are incapable of thought or reason. 

Even though Automata might perform tasks that appeared purposeful, Descartes believed their actions were purely mechanical, driven by physical processes rather than any understanding or intention. For Descartes, the difference between humans and these objects was clear: humans could reason and engage with the world in a way that reflected true thought.

*Fast forward* to the world of modern AI, and we now have machines that can produce remarkably human-like language, making it seem as though they are reasoning or thinking. But are they really doing so?

While LLMs can string together words and phrases in ways that sound intelligent, they don’t truly “understand” the meaning in the way humans do. In this sense, LLMs seem to align with Descartes’ view of machines as sophisticated [mimics](https://media.tenor.com/c4HtDmmYreUAAAAM/mimic-animation.gif)—producing outputs that seem intelligent but, in reality, are merely a performance of an extremely complicated script.

But this also presents a challenge to Descartes’ assertion that language is the definitive marker of reason, as they demonstrate the ability to produce seemingly intelligent language without underlying comprehension.

And to be fair, it is also of course worth noting that Descartes was writing in the [17th century](https://www.marxists.org/reference/archive/descartes/1639/meditations.htm). A time when machines were more likely to resemble windmills than the complex computers we know today. So his idea of “machines” was far more limited, even if his philosophical view of them as unthinking, mechanical objects persists in many ways today.

But Turing didn’t just talk about AI that could communicate, he also discussed those that could learn from experience. In a 1947 lecture, he stated, [“What we want is a machine that can learn from experience,” and that the “possibility of letting the machine alter its own instructions provides the mechanism for this.”](https://www.britannica.com/science/history-of-artificial-intelligence) Instead of just following pre-written rules, such a system would improve over time through experience and trial-and-error. This is where the concept of [heuristics](https://www.merriam-webster.com/dictionary/heuristic) in problem-solving comes into play. Heuristics are rules of thumb that allow people (or models) to quickly find solutions based on past experiences and probabilities of success. Rather than seeking the perfect solution, which could be time-consuming and resource-intensive, heuristics prioritize finding a good-enough solution more efficiently.

Predictive AI operates on this principle. When these models analyze new data, they adjust their internal calculations, improving their accuracy based on what has worked in the past. Unlike LLMs, which don’t fundamentally change after their initial training unless retrained on new datasets, predictive AI systems evolve dynamically. They continuously optimize their decision-making to deliver better, faster outcomes as they learn from new data and experiences.

### The future of AI: beyond emulation

In this author's opinion the future of AI should aim to augment, not just replicate, human behavior. 

Predictive AI, which excels in analyzing data and uncovering hidden patterns, serves as a prime example of this. By turning complex data into actionable insights, these models empower businesses to make more informed decisions. For instance, in marketing, predictive models can identify high-conversion customers and suggest tailored strategies, tasks that would be impossible for humans to undertake or scale efficiently on their own. Its continuous learning and evolution further refine its predictions, unlocking new levels of efficiency and impact over time.

But while the focus of this article thus far has been primarily on how LLMs focus on mimicking human behavior, that’s not the only thing they can do. For instance, LLMs help us interact with and quickly manage complicated systems, as is the case with AI agents. 

Consider a scenario where a patient needs to schedule a complex series of medical appointments. An AI agent, integrated with the hospital's scheduling system, can understand the patient's needs, check doctor availability, coordinate with insurance requirements, and send reminders, all through a simple conversational interface. This not only streamlines the process for the patient but also frees up hospital staff to focus on critical patient care. 

These tools can automate processes and save time by performing repetitive tasks or helping us interact more efficiently. They don't replace us or try to copy us; they help us do more and do it better. And they free up human effort for higher-value work, enabling creativity and decision-making. 

In this sense, both predictive AI and LLMs, despite their differing focuses, can amplify human abilities when used in the right contexts, showing that the key to successful AI implementation is using the right tool for the job.

### And really, what even is intelligence?

At this point in the article you might be asking, “Wasn’t this whole thing supposed to be about whether these AIs were intelligent?” 

The concept of intelligence, though, isn’t as simple as a binary choice between “smart” and “dumb”. As another enlightenment philosopher, [Baruch Spinoza](https://www.britannica.com/biography/Benedict-de-Spinoza) proposed with his philosophy of [monism](https://ndpr.nd.edu/reviews/spinozas-argument-for-substance-monism-why-there-is-only-one-thing/), consciousness and reason are not exclusive to humans. They are defined through an object's interactions with its surroundings, which exist on a spectrum throughout nature, from the simplest forms of interaction (those of a rock) to the complex reasoning of humans. 

And indeed, both predictive AI and LLMs, though different in their design and function, are part of this spectrum. Each possesses a distinct form of intelligence that, when used thoughtfully, can augment human decision-making, creativity, and problem-solving.

Ultimately, both tools—predictive AI and LLMs—are intelligent in their own right, but each excels in different areas. By approaching AI with the mindset of enhancing human abilities rather than simply emulating human behavior, we unlock its true potential. Predictive AI excels at providing insights and optimizing decision-making, while LLMs offer a powerful tool for generating human-like text, facilitating communication, and automating tasks (among a range of other applications). By understanding their strengths and applying them wisely, we can harness AI to not only complement human abilities but to push the boundaries of what is possible, opening up new opportunities for innovation, efficiency, and growth.

### Conclusion

Both LLMs and predictive models are intelligent, but in different ways. It’s about using them thoughtfully—recognizing the unique strengths each brings to the table.

In the end, the true value of AI isn’t in replicating human thought, but in extending our own potential. Predictive AI can enhance decision-making and strategic thinking, while LLMs offer powerful ways to facilitate communication and automate tasks. When used in harmony, these technologies can open up new opportunities for innovation, efficiency, and growth, creating a future where humans and AI work together to solve complex problems and drive meaningful change.

At the end of the day, it’s not much different from working with any other tool; drills are great for drilling, but don’t ask one to write an essay (that draft was terrible).

What makes a machine intelligent, and how can we harness its capabilities to improve our lives? This essay explores various approaches to that question, from Descartes to Turing.

AI to empower human intelligence, not just emulate it

What if you could embed AI-driven predictions into your platform—without the massive investment in data, engineering, and ongoing model maintenance?

Your customers want smarter insights: predictions on which leads will convert, which territories will perform best, and which prospects match their ideal profile. But building AI agents from scratch is a massive undertaking—one that requires specialized expertise, vast amounts of data, and months (or even years) of development. For many SaaS companies, the cost and complexity make it an unrealistic endeavor.

So what is to be done?

[![SalesRabbit partnership with Faraday landing page](/images/blog/pbf-blog/salesrabbit-story-header-image.png)](/images/blog/pbf-blog/salesrabbit-story-header-image.png)


## Introducing Powered by Faraday

This is precisely why we launched **Powered by Faraday**—a turnkey solution that allows SaaS companies to integrate AI-powered customer predictions into their platforms without having to build or maintain machine learning models themselves.

We’ve spent over a decade refining predictive models that help businesses understand and anticipate customer behavior and with **Powered by Faraday**, we bring these capabilities directly into the hands of product teams, enabling them to enhance their software with advanced AI-driven insights quickly and cost-effectively.

## What Powered by Faraday does

With **Powered by Faraday**, companies can:

* **Enrich their customer data** with 1,500+ built-in consumer attributes, adding valuable context to their existing datasets.  
* **Predict customer behavior** by using pre-built machine learning models that score leads, prioritize outreach, and identify high-conversion opportunities.  
* **Accelerate product development** by embedding plug-and-play AI models, cutting multi-year build times down to weeks or even days.  
* **Optimize costs and scalability** with transparent pricing and predictable expenses, avoiding the uncertainty of large-scale computational costs.

For SaaS companies looking to offer predictive capabilities without reinventing the wheel, **Powered by Faraday** is a game-changer.

## Faraday’s competitive edge

Many companies offer AI solutions, but **Powered by Faraday** stands out due to several key advantages:

* [**Seamless integration with a robust API**:](https://faraday.ai/docs/abstractions/traits) Powered by Faraday is designed for seamless implementation. Faraday’s well-documented, developer-friendly API makes embedding AI-driven insights into a product straightforward and efficient.   
* [**FIG (Faraday Identity Graph)**](https://faraday.ai/docs/abstractions/traits): A vast, privacy-compliant third-party dataset with 1,500+ attributes, enabling richer predictions than first-party data alone.  
* **Forward-deployed data science & professional services**: Hands-on support from data scientists who fine-tune models and ensure seamless deployment.  
* **Ethical, privacy-first AI**: A commitment to consumer privacy, avoiding personally identifiable information while delivering accurate predictions.  
* **Industry-specific models**: Pre-trained, vertical-specific machine learning models that accelerate time-to-value.  
* **Flexibility to design tailored solutions:** Faraday enables companies to integrate predictive capabilities without the constraints of rigid, one-size-fits-all AI solutions—offering the perfect balance between ease of adoption and customization.

By combining rich third-party data with hands-on AI expertise, Faraday helps SaaS companies deliver world-class predictive features faster and with greater accuracy than in-house or off-the-shelf solutions.

## Real-world use cases

Many companies have already leveraged **Powered by Faraday** to enhance their platforms. 

Here’s how two of them—[AccuLynx](https://faraday.ai/blog/acculynx-lead-intelligence-powered-by-faraday-api) and [SalesRabbit](https://faraday.ai/blog/salesrabbit)—are making AI-driven predictions a core part of their products:

### AccuLynx: Smarter lead prioritization for roofing contractors

[AccuLynx](https://acculynx.com/), a leading provider of business management software for roofing contractors, needed a way to help their customers prioritize sales efforts more effectively. They integrated [Faraday’s prediction API](https://faraday.ai/docs/reference) into their new [Lead Intelligence feature](https://acculynx.com/roofing-sales-lead-intelligence/), which automatically scores leads within AccuLynx’s platform, based on hundreds of demographic, financial, and property data points.

The result? Roofing contractors using AccuLynx no longer have to guess which leads to focus on. Instead, they get **AI-powered rankings** that help them close more deals efficiently—without needing in-house data science expertise.

As their VP of Product [Mark Rattin](https://www.linkedin.com/in/markrattin/) said, “Lead Intelligence is a game-changer for roofing companies,” … “AccuLynx customers no longer have to guess which leads to call first, or only rely on their experienced sales people to quickly close deals. Now they can improve their sales win rates by focusing their efforts on the best leads.”

Sounds like a win to us\!

[![AccuLynx landing page](/images/blog/pbf-blog/acculynx-landing-page.png)](/images/blog/pbf-blog/acculynx-landing-page.png)

### SalesRabbit: AI-Driven insights for field sales teams

[SalesRabbit](https://salesrabbit.com/), a leader in field sales software, wanted to take their [DataGrid](https://salesrabbit.com/datagrid/) solution to the next level. While their platform already provided canvassers with property and homeowner data, they saw an opportunity to go further—predicting which homeowners were most likely to convert based on behavioral and demographic insights.

By integrating [Faraday’s agentic customer prediction models](https://faraday.ai/templates), SalesRabbit was able to:

* Guide sales reps toward high-potential neighborhoods and properties.  
* Optimize canvassing routes for better efficiency.  
* Improve app performance by caching predictive insights in advance.

By leveraging **Powered by Faraday**, SalesRabbit transformed DataGrid into DataGrid AI, offering their customers a predictive lead scoring tool in a fraction of the time it would’ve taken to build in-house.

[![SalesRabbit DataGrid AI landing page](/images/blog/pbf-blog/salesrabbit-datagridai.png)](/images/blog/pbf-blog/salesrabbit-datagridai.png)

## **Conclusion: AI-Powered predictions without the headache**

For SaaS companies looking to bring predictive insights to their customers, building AI models from scratch is often too expensive, time-consuming, and resource-intensive. Powered by Faraday removes those barriers, making it easy to integrate best-in-class consumer predictions into your product without the heavy lift.

Whether you want to help your customers prioritize leads, optimize sales territories, or enhance customer insights, Powered by Faraday lets you do it faster, more affordably, and at scale.

Want to see how it works? Check out the full stories from [AccuLynx](https://faraday.ai/blog/acculynx-lead-intelligence-powered-by-faraday-api) and [SalesRabbit](https://faraday.ai/blog/salesrabbit), or reach out to [Dave](https://www.linkedin.com/in/davidtoddsmall/) or the [Faraday](https://faraday.ai/talk-to-sales) team to explore how Powered by Faraday can enhance your platform.

AI is revolutionizing how SaaS companies can leverage predictive insights, and at Faraday, we’re making it easier than ever to integrate powerful, ethical AI-driven capabilities without the hefty overhead.

AI-Powered insights for SaaS companies, the fast way

It's 4pm on a Monday and your boss has just dropped the news: lead conversion rates are tanking, even as volume has grown dramatically. You need a fix _by Friday_.

Your team is drowning in leads ranging from red-hot to ice-cold—but how do you sort through them?

Luckily you have a tool like Faraday in your belt, which springs into action, collecting and transforming all the relevant data, normalizing it for the situation, extracting representative training sets, engineering features automatically, building dozens of candidate models for the problem, tuning hyperparameters on the fly, and applying the right model to each lead at the right time to gauge its quality.

It’s not just a predictive AI tool, what you have is an AI agent. And it's the ultimate asset for engaging customers at scale, whether that's managing leads, messaging new offers, or personalizing an on-site experience.

Faraday has been building and operating AI agents for thousands of brands for more than 12 years. Now that popular terminology has caught up, we thought we'd share a bit more on the agentic phenomenon.

## A little context

AI agents have actually been around for quite some time. In fact, the idea of machines acting independently to achieve goals dates back nearly a century to science fiction stories like Karel Capek's 1920 play Rossum's Universal Robots (which coined the term, ‘robots’) and Isaac [Asimov’s 1940 classic short story, “Robbie,](https://www.wired.com/story/artificial-intelligence-minds-science-fiction/)” But these robot dreams didn’t take long to transition from science fiction to reality.

[![Shakey the robot full body](/images/blog/ai-agents-blog/shakey-the-robot.jpg)](/images/blog/ai-agents-blog/shakey-the-robot.jpg)
Shakey the Robot, photo credit: computerhistory.org

Indeed, as early as the 1960s, real-world examples of rudimentary robots, such as the [Shakey the robot](https://www.sri.com/hoi/shakey-the-robot/) were already starting to appear. Shakey was the first mobile general-purpose robot and could perceive its surroundings and reason with complex commands, breaking them down into basic chunks without needing explicit instructions for each step. [Shakey was also capable of planning and navigation, and significantly influenced modern robotics and AI techniques.](https://www.computerhistory.org/revolution/artificial-intelligence-robotics/13/289) But in reality, Shakey was, by modern standards, a little shaky.

Shakey could solve problems and utilized symbolic reasoning like modern agentic AI but was massively [limited by weak computational power and rigid programming frameworks.](https://aiworldjournal.com/the-evolution-of-agentic-ai-from-concept-to-reality/) For these reasons it couldn’t compare to the complexity and reasoning ability of modern systems. But despite these limitations, Shakey and [other early robots](https://community.hpe.com/t5/ai-unlocked/a-short-history-of-artificial-intelligence/ba-p/7172315) like it marked major steps forward in our ability to produce machines that could solve problems and make decisions independently. However, the progress was slow, the hardware was clunky, and Shakey and his friends all required an entire room of computers to perform simple tasks—so it wasn’t exactly the autonomous future Asimov had imagined.

[![cover of Asimov's Robbie the Robot, short story](/images/blog/ai-agents-blog/robbie-the-robot-cover.jpg)](/images/blog/ai-agents-blog/robbie-the-robot-cover.jpg)

But while Shakey was still breaking down commands, fiction had already imagined AI with greater capabilities. Take Robbie, the robotic nanny from Asimov’s story. Unlike Shakey, Robbie made independent decisions, learned from experience, and adapted his behavior. Though he doesn’t run on data pipelines or neural networks like modern AI agents, at his core, Robbie functions much like they do, by making decisions and fulfilling tasks independently (and, admittedly, with a cool metal body, but we’re getting there). And where Shakey fell short, today’s AI agents don’t face the same limitations, and the gap between fiction and reality continues to shrink.

Ironically, the main theme of this story was the misunderstanding of new and emerging technologies. With that in mind, let’s break down what AI agents really are, no fiction, just facts.

## Why AI agents are everywhere now

Fast forward to 2025, and AI agents are no longer just science fiction. They’re all over the place: managing supply chains, handling customer service, writing emails, and yes, optimizing marketing campaigns. But why the sudden explosion in AI agent adoption?

A few reasons:

- **Computational power**: Advances in cloud computing and GPUs mean that what used to require a warehouse of machines now runs on your laptop.
- **Better data availability**: AI thrives on data, and today, there’s more of it than ever before.
- **Automation hype**: Everyone wants AI to do the boring stuff so they can focus on the big picture (or maybe take a longer lunch break).

## How AI agents get jobs done

Modern AI agents operate by processing vast amounts of data, learning from patterns, and making decisions based on predefined goals. They typically rely on machine learning models, neural networks, and sophisticated algorithms to analyze information, predict outcomes, and take action.

Some agents function through reinforcement learning, continuously improving their performance based on feedback, while others follow rule-based systems with explicit instructions. Whether it’s a chatbot answering customer inquiries, an autonomous robot navigating an alien environment, or even a predictive model sorting through data, scoring it and then forwarding it to [Shopify](https://www.shopify.com/), AI agents work by combining perception, decision-making, and execution—often in real time—to accomplish tasks with minimal human intervention.

## Common misconceptions about AI agents

But as uncle Ben almost said, with great hype comes great misunderstanding. Here are a few common misconceptions about AI agents:

- **AI agents are sentient.** Nope. They analyze data, follow rules, and optimize for objectives, but they don’t “think” or “feel” in any meaningful way (sorry Robbie, we still love you).
- **AI replaces humans.** Not exactly. AI agents assist and augment human decision-making rather than fully replacing it. The best use cases involve AI handling repetitive or overly complex tasks while humans focus on strategy.
- **More data \= better AI.** This is sometimes true, but as the saying goes: 'Garbage in, garbage out.' More data doesn’t always mean better AI and quality matters more than quantity, especially when you’re looking for actionable business results.
- **AI agents are just fancy chatbots.** This one’s a little off too\! While chatbots are one application, AI agents go far beyond simple conversations. They can analyze data, make decisions, and automate complex tasks in a variety of fields.

## AI agents for the customer experience

Faraday is a flexible and fully composable platform that automatically processes and analyzes your data, delivering actionable insights to a range of targets. By using data and AI agents to make informed decisions and automate complex tasks, Faraday helps businesses streamline processes, optimize strategies, and drive growth. We tap into the true potential of AI agents to help businesses make smarter, faster, and more data-driven decisions, without needing a PhD in machine learning.

So how does Faraday work as an AI agent? Let's break it down:

1. **Independent decision-making**: AI agents act autonomously to achieve specific goals. Faraday independently analyzes consumer data, identifies patterns, and makes predictions without constant human oversight.
2. **Goal-oriented actions**: Agents perform tasks to accomplish defined objectives. Faraday's purpose is clear: to predict consumer behavior and optimize customer acquisition strategies to generate value. View some of [our customer stories](http://faraday.ai/stories) to see how much impact this can make.
3. **Learning and adaptation**: True agents improve over time. Faraday continuously refines its models based on feedback and new data, getting smarter with each interaction and iteration.
4. **Environment interaction**: AI agents observe and respond to their environment. Faraday processes vast amounts of customer data, market signals, and business metrics to inform its decisions.

In short, Faraday builds and operates AI agents for businesses that want data-driven growth, without the guesswork. Unlike Robbie the robot, it won't babysit your kids, but it will make sure your marketing efforts aren't stuck in the past (and if you insist, we do have a founder named [Robbie](https://www.linkedin.com/in/robbieadler/)).

Want to see how Faraday's AI agents can optimize your customer acquisition strategy? Let's talk.

AI agents, like the ones Faraday produces, are transforming marketing by automating predictions, optimizing outreach, and continuously learning from customer data to improve engagement and ROI.

Faraday agents: Why we changed the way we're talking about our core products

[Klaviyo](https://www.klaviyo.com/) made a big splash last year, bringing entry-[level predictive scoring](https://faraday.ai/blog/why-you-need-predictive-lead-scoring-today) to their massive audience. And we love it—the industry is catching up to what we’ve been doing for years. With AI-powered tools like [predictive analytics](https://faraday.ai/templates/lead-prioritization), [personalized product recommendations](https://faraday.ai/templates/next-best-offer), and advanced [sentiment analysis](https://faraday.ai/blog/seamus-bigquery-interview) for their emails lists, Klaviyo is giving marketers powerful capabilities to forecast customer behavior, personalize messaging, and automate campaigns. But if you’re looking to take it to the next level, there’s a critical missing piece: [deep consumer insights powered](https://faraday.ai/features/faraday-identity-graph) by [third-party data](https://faraday.ai/docs/abstractions/traits).

## Klaviyo’s predictive tools, and their limitations

[Klaviyo’s AI tools](https://www.klaviyo.com/features/ai) offer a solid foundation for email marketers, helping to predict engagement and automate messaging. But recent changes—including increasing costs and [stricter policies around active contacts and list sizes](https://help.klaviyo.com/hc/en-us/articles/24263920096027)—are making efficiency more important than ever.

Though Klaviyo’s AI is powerful, their analysis is also stymied by its lack of inclusion of third-party data. This type of rich demographic and behavioral data inherently improve the depth, nuance, and critically, speed-to-deployment of predictive models. It also radically improves the accuracy and value that they can be expected to produce.

Only with this type of rich third-party data can marketers expect to achieve the highest levels of  speed and precision with their predictive marketing, and that’s where Faraday’s built-in integrations comes in.

## Optimizing Klaviyo lists with Faraday personas

Integrating Faraday’s [personas](https://faraday.ai/docs/abstractions/persona_sets) and [customer segmentation](https://faraday.ai/docs/how-to/connections/segment) capabilities into your existing Klaviyo stack takes your email lists to the next level, allowing you to gain deep insights that Klaviyo alone can’t provide.

With Faraday, you can segment your email lists not just by basic engagement metrics but by [demographics, life events, property details, and other behavioral signals](https://faraday.ai/features/faraday-identity-graph). This level of insight empowers you to tailor content, messaging, and campaigns that are perfectly suited to your audience. Whether it’s understanding the impact of a new home purchase or identifying consumers in a particular life stage, Faraday’s data-driven approach helps you reach the right people at the right time with the most relevant messaging.

By adding these third-party insights to your Klaviyo platform, you unlock smarter segmentation, faster insights, and a deeper understanding of your customers—making your email marketing more efficient, relevant, and impactful.

### The inclusion of this this data improves your Klaviyo lists by:

* **Building smarter segments** based on real-world demographics, life stages, and property data.  
* **Refining automated journeys** to align with consumers’ actual behaviors and preferences.  
* **Improve conversion rates** by sending the right message to the right people at the right time.

## The cold start problem—and how to overcome it

An additional benefit of integrating third-party data into your Klaviyo stack is the ability to avoid the [cold start problem](https://www.sciencedirect.com/topics/computer-science/cold-start-problem), a classic challenge faced by many marketing tools, including Klaviyo’s out of the box predictive scoring. While these tools are able to generate generally accurate predictions on long time customers using only first-party data, they quickly run into issues when targeting leads or recently acquired customers with very little observed behavior. 

In these cases, the model struggles to quickly generate meaningful insights, as it lacks the foundation of prior data to build from. This delay in data accumulation can significantly limit the effectiveness of campaigns until enough data has been collected.

Faraday inclusion of our third-party data eliminates this barrier by enabling you to build highly targeted customer segments right from the start. Instead of waiting months for data to accumulate, Faraday’s built-in data provides immediate insights, empowering you to craft campaigns optimized for engagement and conversion from day one. Without waiting for slow data accumulation, you can launch campaigns quickly with confidence.

## So who will actually convert? Let’s find out.

Your Klaviyo lists might still be growing, but are they optimized for conversion? Are you maximizing their value-generating potential? With Faraday, you can build a free persona model and see for yourself. Think of all the ways you could use this—better segmentation, more precise messaging, and automated journeys that actually resonate.

Klaviyo is a great start for any email marketing, but Klaviyo \+ Faraday together can take your email marketing to the next level\!

Enhance your Klaviyo lists with Faraday’s third-party data and personas to unlock deeper insights, smarter segmentation, and higher conversion rates—solving the cold start problem and optimizing your email marketing from day one.