The complete guide to embedding predictive AI in your platform

Every modern platform is under pressure to be smarter. Your customers expect AI-powered features, predictive insights, and personalization baked into the products they pay for. A vertical SaaS company without intelligent recommendations starts to look like a database with a UI. An agentic AI platform without real-world context starts to look like a wrapper on a foundation model.

But building that intelligence from scratch is a different kind of project than most product teams are set up for. It requires licensing consumer data at enterprise scale, hiring data scientists to build and maintain models, standing up identity resolution and privacy compliance infrastructure, and absorbing the kind of unpredictable computing costs that make finance teams nervous. The typical timeline is multiple years. The typical upfront cost is six figures before a single prediction ships.

The result is a gap between what your product needs to know about the people it's serving and what your platform can actually deliver. We call this the context gap — and for platform builders, it looks like delayed AI roadmaps, watered-down features that don't move the needle, or shipped products that feel generic because they lack the underlying consumer intelligence to be anything else.

The good news: embedding predictive AI and consumer context into your platform is a solved problem. The question isn't whether to do it — it's whether to build it yourself or partner with infrastructure that's already production-ready.

What gets in the way of building predictive AI in-house

Most product teams start by exploring the in-house route. Here's where they run into trouble.

Challenge	In-house Build	Powered by Faraday
Data foundation	6-figure broker fees, complex ETL pipelines	Immediate access to Faraday Identity Graph (FIG)
Deployment time	Multi-year roadmaps, high data science overhead	Prebuilt propensity models, prototype in 1 day
Cost predictability	Unpredictable compute overages, high entry floors	Usage-based pricing scaling with business
Compliance & privacy	DIY infrastructure, ongoing regulatory overhead	Built-in via logical account separation and permissioned data sourcing

They’re missing the consumer data foundation

Predictive models need a deep well of data to find an accurate signal. Even if your platform already collects rich first-party data from your customers, that data only describes how users interact with your product — not who they are outside of it. To make accurate predictions about behavior, fit, or future value, you need third-party consumer context: demographics, household economics, life events, lifestyle indicators, property data, and more.

Sourcing that data through traditional brokers means six-figure upfront licensing fees, multi-month contracting cycles, and ongoing engineering investment to ingest, normalize, and maintain hundreds of millions of consumer records.

The fix: Faraday gives you immediate access to the Faraday Identity Graph (FIG) — 240M U.S. adults and their households, with 1,400+ verified consumer data points spanning demographics, household economics, property data, life events, lifestyle indicators, and behavioral data. No upfront licensing fee. No ETL pipeline to build. The data is production-ready on day one.

Deployment and contracting are expensive and time consuming

A custom predictive AI pipeline typically takes multi-year roadmaps to ship — and that's after you've hired the data science team to build it. You need infrastructure for identity resolution, model training, score deployment, and ongoing maintenance as data freshness and model drift become real problems. Most platform teams can't pull engineers off their core roadmap for that long, and the ones who try often end up with something that works for one use case but doesn't scale to the next.

The fix: Faraday's propensity models, AI personas, and recommenders are prebuilt and plug-and-play. You connect your data, define the outcome you want to predict, and Faraday handles the modeling infrastructure, bias mitigation, and score deployment. SalesRabbit shrunk a multi-year roadmap to a matter of days. Hazel had a working prototype in a single day of engineering.

Costs are unpredictable and often expensive upfront

Even if you can absorb the upfront investment, the ongoing cost of running predictive AI at scale is hard to forecast. Compute costs grow unpredictably as your customer base expands. Data licensing renewals come with escalators. And if your platform serves both enterprise and SMB customers, the variance in monthly prediction volume makes budgeting a guessing game.

The fix: Faraday's usage-based pricing scales with your business. You pay for what you use, on terms that match how your own customers pay you. No surprise renewals, no six-figure floors, no compute overages. And because Faraday's predictions are cached and delivered via API, you get the performance benefits of pre-computed scores without the infrastructure overhead.

Two paths, one platform

The teams embedding Faraday fall into two broad groups, each with a different use case but the same underlying capability.

Vertical SaaS: predictive intelligence inside your product

If you're building software for a specific industry — field sales, roofing, home services, real estate, insurance — your customers are looking to your product for guidance, not just record-keeping. Predictive lead scoring, customer prioritization, and territory optimization turn a workflow tool into a strategic advantage.

Faraday's API and prebuilt prediction recipes let you embed scores directly into your product UI, your customers' CRMs, or wherever your users make decisions. Every score is calculated using your customers' first-party data enriched with FIG, so the predictions are tailored to each customer's business without you having to build a separate model for each one. And because Faraday supports subaccount architecture, your customers' data stays logically separated and compliant from day one.

SalesRabbit used this approach to launch DataGrid AI, embedding likelihood-to-convert scores directly into their field sales app and shrinking a multi-year roadmap to days. AccuLynx used Faraday's API to launch Lead Intelligence — a weekly lead scoring feature now included in every AccuLynx subscription for roofing contractors across the U.S.

Agentic AI and martech: real-time consumer context for your agents

If you're building agentic AI, an AI coworker, or a martech platform that orchestrates customer interactions, your agents need consumer context at the moment of decision — not in a batch report delivered the next morning. An agent making a routing decision, personalizing outreach, or triaging a lead needs to know who it's talking to from the very first interaction.

Faraday's Lookup API and MCP server deliver consumer context in real time — single-record enrichment on demand, with sub-second response times. Your agent calls Faraday, gets a complete consumer profile back, and grounds its next action in context that no foundation model can provide on its own. As we've written before, an AI model without context is just a convenient way to deliver the wrong message at scale.

Case study: Hazel ships a working prototype in one day

Hazel is an agentic marketing platform that provides an AI coworker to marketers, helping them analyze contextual data on their business, their customers, and their market to make optimal decisions.

The problem

To fulfill its mission, Hazel's agent needed to sit at the nexus of three data sources: first-party customer data, business-specific fundamentals, and third-party consumer context. Solving for the first two was a matter of building integrations with their clients' data warehouses. Solving for the third meant either engaging with traditional data brokers — six-figure upfront fees, multi-month engineering builds, no MCP support, no real-time API — or finding a different way.

For a startup with limited engineering resources and working capital operating in the emerging, hyper-competitive agentic marketing vertical, the traditional path was untenable.

The solution

By integrating with Faraday's API, Hazel gained immediate access to FIG without taking on the burden of hosting, maintaining, or licensing the underlying data. Faraday's MCP and Lookup API ensured Hazel's agent could retrieve consumer context in real time, supporting a wide range of client use cases. And Faraday's usage-based pricing meant Hazel could scale their capital investment with client uptake rather than commit to upfront fees before generating revenue.

The result

• Upfront costs reduced from $100K+ to$0 — payments scale with usage
• Initial engineering investment reduced from multi-month to multi-week
• Working prototype shipped in a single day of engineering time
• Ongoing data maintenance overhead eliminated entirely
• Built-in privacy compliance via logical account separation

The partnership gave Hazel a competitive advantage over traditional SaaS analytics and BI tools — winning clients in a hyper-competitive emerging market where time-to-value directly correlates to market share.

The data foundation

When you embed Faraday into your platform, the intelligence your customers experience is powered by the Faraday Identity Graph. For your end users, that depth is what makes the difference between a feature that feels like a gimmick and one that actually changes how they work. A roofing contractor using AccuLynx Lead Intelligence isn't just getting a score — they're getting a signal derived from hundreds of data points about the household in front of them. A field sales rep using DataGrid AI isn't just getting a priority ranking — they're getting a prediction trained on what their best customers actually look like. That's only possible because of the breadth and quality of data sitting underneath the API.

For platform builders specifically, two things about FIG are worth calling out. First, FIG includes historical data — full data point timelines, not just current-state snapshots — which means your models can train on what someone looked like just before they took the action you care about. That's what separates a model that finds more customers from one that just describes the ones you already have. Second, FIG data is consented, offline, and sourced from permissioned vendors — not the third-party cookie ecosystem — so the compliance infrastructure your platform inherits is built on a foundation that holds up.

Ready to power your platform with Faraday?

Whether you're building vertical SaaS, an agentic AI platform, or a martech product that needs to act on real-time consumer context, the starting point is the same: a conversation about what you're building and how Faraday fits into it.

• Talk to a Context Consultant: Book a session to walk through your product roadmap, your data architecture, and where predictive context can deliver the biggest lift.
• Explore the docs: Browse our developer documentation to see how easily we could integrate into your existing stack.
• Try the data first: Get started on buy.faraday.ai — enrich a sample file against FIG and see the data quality for yourself before committing to an integration.

FAQ

How is this different from building on a foundation model directly?

Foundation models give your platform language and reasoning capability. Faraday gives it the consumer context to ground that capability in reality. An LLM can write a thousand outreach variants, but it can't tell you which household is most likely to convert, what life stage they're in, or whether a given offer will resonate. Faraday delivers that context via API or MCP, so your AI features have the grounding they need to actually perform.

Do we need a data science team to build with Faraday?

No. Faraday's prebuilt prediction recipes — propensity models, personas, recommenders, churn prediction — handle the modeling infrastructure for you. Your team defines the outcome you want to predict, connects your customers' data, and Faraday handles the rest. SalesRabbit shrunk a multi-year roadmap to days without growing their data science team.

How does Faraday handle multi-tenant data separation?

Faraday supports subaccount architecture, which lets each of your customers' data live in a logically separated environment within your platform. That means clean data isolation, per-customer model training, and built-in privacy compliance — without you having to build the infrastructure yourself.

What's the pricing model?

Usage-based. You pay for what you use — predictions made, records enriched, API calls served — on terms that match how your own customers pay you. No six-figure upfront licensing fees, no compute overages, no surprise renewals.

Can we white-label Faraday in our product?

Yes. Faraday is designed to power your product, not replace it. Your customers see your UI, your branding, and your workflow. Faraday handles the data and modeling layer behind the scenes — the infrastructure that makes intelligent features possible without the multi-year build.