Stop sending your AI agent into conversations blind: get customer context via the Faraday API

Even the most powerful model in the world can’t drive real value if it doesn’t know who it’s talking to. As our CEO, Andy Rossmeisl, put it: “Without the specific identity and history of the customer it’s talking to the world's most advanced AI can do little more than offer a polite, generic greeting."

That's not a model problem. It's a context problem.

The real issue isn't your model — it's what your model knows

Most AI agents start every conversation blind. Without history or clear signal about who's actually on the other end. So they default to generic responses. Not because they're poorly built, but because they have nothing to work with.

The instinct is to throw more data at the problem. More attributes. More integrations. More inputs. But raw data doesn't tell your agent anything useful about this specific person, right now. You can have a thousand attributes about someone and still not know whether they're price-sensitive, in-market, or a high-value prospect worth a personalized offer.

As Andy puts it: "Context is the guiding light that turns a cold interaction into a warm conversion. It is the vital distinction between having a list of names and possessing the clarity to know exactly who a prospect is, and how to best engage them."

That shift — from dumping data at your model and hoping it figures things out, to feeding it curated signals — is what separates agents that plateau from agents that keep getting smarter.

What Faraday does

Faraday enriches incoming identities against the Faraday Identity Graph — 1,500+ consumer attributes across 240 million U.S. adults. You send us PII. We return a rich consumer profile, appended to your customer record, ready to pass directly to your agent or model.

For you, it’s as simple as a clean API call that turns an anonymous identity into someone your agent actually knows.

What this means for what you're building

Your agent personalizes from message one. No more cold-start problems. The moment an identity hits your system, it can be enriched with the context your agent needs to skip the generic greeting and get to the relevant conversation. This is exactly what Hazel did with our data — their agentic marketing platform went from knowing nothing about a user to having real customer context on every interaction, with a working prototype in a single day.

Your agent acts on signal, not noise. Curated traits — household composition, lifestyle indicators, behavioral factors — slot directly into your agent's reasoning. Platforms like Heatseeker work with us to turn their customers' flat transactional records into rich consumer profiles overnight to power smarter personas, better targeting, and a shared language across the org.

Your roadmap moves at API speed. What would take a multi-year build internally — sourcing data, hosting it, modeling against it, maintaining it — becomes a few API calls. There's no infrastructure overhead, no six-figure procurement cycle, and no contract to renegotiate as you grow. Brands like Hazel and SalesRabbit collapsed timelines from months or years down to days — and scaled on the same infrastructure they prototyped with.

The bottom line

Your agent isn't underperforming because it's poorly built. It's underperforming because it doesn't know who it's talking to.

Faraday fixes that with a single API call — enriching every incoming identity with 1,500+ consumer attributes before your agent says a word. No data sourcing, no infrastructure build, no long procurement cycle. Just context, from day one.

If you’re ready to accelerate your roadmap and get the context your agent needs today, start here — or talk to a context consultant about what enrichment looks like for your use case.