Faraday

Some sessions hit different. You know the ones: where you're scribbling faster than you can think. That's Lead-to-Customer. Faraday's got your back with notepads and pens. Register now: https://ow.ly/hQ4b50ZQ0tA

Consumers don't mind #AI. They mind experiences that don't feel authentic. Hear perspectives from Robin Spencer (Faraday), Kate Chamberlain (SimpliSafe), and our very own Julia Lovett.

Major milestone for Faraday this week as we launch our first truly self-service experience, Faraday Pro. Advanced customer context was once something only larger organizations with sophisticated marketing automation teams could use effectively. But with agents eating the marketing world, we knew it was time to cut out all the red tape. Faraday Pro lets humans and their agents define the context payloads they need, then retrieve in real time (MCP or API) or batch (file append or recurring warehouse deployment). It’s the same context you can get from Faraday Enterprise—rich consumer profile attributes, unique predictions, identity, etc.—only you can get started with a credit card rather than a 3-call sales cadence. Check out the blog post for all the details, and I’m also sharing a private signup link for my LinkedIn friends here that comes with 400 free credits (expires in a week): https://lnkd.in/gTd-HSGk https://lnkd.in/gD4wj5AJ

52% of respondents in PX’s State of the Industry survey expect every lead to carry significantly more context and information. The next question is: what do we do with that context? During our keynote panel discussion at LeadsCon, Robin Spencer spoke about adding an AI layer on top of richer lead data to first understand leads as people with families, pets, preferences, and buying needs, and then create more personalized journeys between consumers and the brands they want to buy from.

I’m super proud to share that v2 of Faraday’s identity graph (FIG) is now live. This is the single biggest technical project we’ve ever shipped in our 12+ years here and I wanted to take a minute to focus on the WHY. FIG is our consumer profile dataset, 1,500+ attributes on nearly a quarter billion U.S. adults ranging from demographics to shopping behaviors. It’s how we’re able to build such effective predictive models here with a couple of clicks or API requests. It feels like the age of the algorithm, but really we’re living in the context era. What distinguishes LLMs from the volatile history of AI is their voraciousness. They are context machines. Indeed, the whole of public written knowledge wasn’t enough: they want your calendar, your CRM, or your codebase. This—not the ingenuity of the algorithm—is what makes them so preternaturally effective. And yet when it comes to engaging customers, context falls flat. Sure, the agent has access to the brand’s inventory and the customer’s transaction history, but none of this says anything about who the customer is or what they want. When context falls flat, engagement falls flat. As brands cede labor to AI, they must invest in context as much as tokens. That’s why we just spent 7+ months on FIG v2 in an effort that involved every single member of the team. Here’s why FIG v2 is so important in our age of context: ✅ Historical data — it’s useful to know a customer’s income. It’s far more useful to know what a customer’s income was when they made their first transaction 3 years ago, and every transaction since. This is called “longitudinal data” and is available in FIG v2 as a first-of-its-kind consumer dataset. ✅ Data freshness — we rebuilt our data ingestion infrastructure to reduce intake from our opt-in, permissioned data sources to a single day. Fresher data means more timely context and more accurate behavior predictions. ✅ Metadata — LLMs like data, but they love structured data. Our new data catalog declares all of the important details that let the agent understand the context behind the context: distributions, types, directionality. This helps them autonomously interpret and incorporate context in decisioning. See the blog post linked below for more. I’m so excited about this. Thibault Dody led the project with ongoing assistance from our newest colleague Zach F.. Michael Musty and Brendan Whitney managed the data science effort and Tom Caruso implemented the new feature store infrastructure. Zebhdiyah Pykosz extrapolated the new setup to the dashboard and CTO Seamus Abshere, as always, made sure it all happened correctly. Jessica Teipel Teipel (can’t wait for you to get back!) and Tristan N. pitched in hard work as well. Literally every member of the team helped with QA. Thank you Faradaisies! P.S. if you’re a client and want to get bumped up on the migration schedule, ping me and I’ll see what I can do 😉 https://lnkd.in/e9bBYsW6

🛫 Heading to Chattanooga for Project Voice 2026, representing Faraday! We are rapidly expanding in Agentic AI solutions powered by Faraday's consumer context and I can't wait to connect with folks at the intersection of Voice AI and real-time customer insights. I'm particularly excited to join Aneri Shah and Aaron Welch, CISSP on a panel discussing AI in Customer Service, moderated by Mark Michelson. We will explore the growing relationship, and in many cases interdependence, between AI and modern corporate customer service. Bradley Metrock Adam Cheyer Hunter Hillenmeyer Andy Rossmeissl

LeadsCon day ✌️ We have an awesome day ahead with Robin Spencer kicking things off with a keynote on the future state of the industry 😎 Inbetween the action, stop by Faraday’s booth #366 to learn more with Ben J. Rose and I 🕺