Announcing Real Contact API 2.0: New IP Intelligence and Improved Contact Grading
You can already rely on Real Contact API to verify your lead data. You can check if an email is deliverable, confirm a name matches a contact record, and grade leads by who is most likely to pick up the phone. Now, with Real Contact API 2.0, we’re adding an IP intelligence layer to help you evaluate lead legitimacy, and refining our phone contact grades to maximize your contactability and reduce wasted outreach efforts.
What’s New in Real Contact 2.0
Real Contact API 2.0 builds on everything in version 1.1 (phone, email, and address verification) at no additional cost, and introduces three major improvements:
- IP-based Intelligence: New attributes (trust_score and ip_distance_to_address) help evaluate the authenticity of an interaction.
- Enhanced Phone Contact Grading: A more precise A-F grading system designed to increase contactability.
- Improved Response Structure: A reorganized JSON response for easier integration and readability.
Why IP Intelligence Matters Right Now
Lead and contact data is easier to fake, recycle, and weaponize than it’s ever been. Scraped identity data is cheap. Bots have become more sophisticated. Residential proxies make automated traffic look human. Verifying contact data alone doesn’t tell you the full story of who you’re actually dealing with.
Lead data can be real and verifiable while the submission itself is fraudulent. You can call a lead, reach the right person, and confirm their name, only to hear them say with complete confidence that they’ve never inquired about your product. Their data was submitted by a bot cycling through stolen identities, and by the time it landed in your CRM it was indistinguishable from a legitimate lead.
This is the gap IP intelligence closes. Contact verification confirms the identity is real. IP intelligence helps confirm that the real person was the one behind the submission.
IP Intelligence Signals: “Can I Trust This Interaction?”
Phone, address, and email-level signals tell you whether a contact is reachable and whether the identity data matches. IP signals add an additional layer by helping you evaluate the authenticity of the interaction (i.e., lead, contact, signup).
IP Trust Score
The trust_score is a 0–100 score that indicates if an interaction appears legitimate based on the user’s IP address. It evaluates IP behavior, network type, and usage patterns to flag suspicious, illegitimate, or high-risk signups. Higher scores indicate a stronger likelihood of reaching a genuine contact, while low scores indicate potential bot traffic or a bad actor.
The trust score is calculated by evaluating multiple characteristics of the IP address and how it behaves over time, including location signals, IP metadata, sharing patterns, and behavioral observations.
IP Distance-to-Address
The ip_distance_to_address attribute measures the approximate geographic distance (in miles) between the user’s IP location and the physical address provided in the request. This helps you spot geographic mismatches that may indicate inconsistent identity data.
For more information on how to interpret these signals, check out this article.
Where IP Signals Add the Most Value
IP signals are most useful in workflows where you need to understand the legitimacy and quality of traffic on top of verifying identity. They help flag situations where a real identity may be used by someone other than the actual individual, such as:
Suspicious Lead Traffic
Not all lead sources are clean. Some leads are recycled, with the same contact information resubmitted across multiple forms or sources. Other leads that come in look fine but are actually suspicious people masking their identity and intention behind a proxy or anonymizing service. These are signals that phone or email verification alone wouldn’t surface.
A lead can have a valid phone number, a deliverable email, and a matching name and still be a fake lead if it was submitted from a device that doesn’t match normal user behavior. IP-based intelligence gives you visibility into where the submission actually originated, so you can distinguish organic leads from potentially fraudulent or bot-driven ones.
Signup and Form Validation
Contact data verification can tell you that an email is deliverable and indicate if a phone number is active, but it can’t tell you whether the person filling out your form is a real prospect or a bot cycling through stolen identities. During signup and onboarding, you want to catch inconsistencies early before they become bad data in your CRM or wasted cycles for your sales team.
IP-based checks can surface geographic mismatches between a user’s IP location and their stated address, and synthesize signals like network type, usage patterns, and IP behavior into a single indicator of legitimacy. For example, if a signup comes from an IP with a low IP trust score, that’s worth flagging regardless of whether the email and phone check out. These aren’t definitive red flags on their own, but combined with your existing contact validation, they give you a more complete picture of who’s signing up.
Lead Prioritization
When you’re working a list of hundreds or thousands of leads, you need a way to focus effort where it’s most likely to pay off. IP-based intelligence helps you prioritize leads that appear more likely to represent an authentic identity. For example, in a recruiting context, if an applicant’s IP address is thousands of miles from the location on their resume, that geographic mismatch could signal a fraudulent application, which you wouldn’t catch from the resume data alone.
Why You Can’t Skip IP-Based Checks
Without IP intelligence, you’re missing patterns that contact data alone can’t reveal. IP context gives you transparency into signals like:
- The same IP appearing across multiple lead submissions
- Geographic mismatches between a user’s IP location and the address they provided
- Suspicious IP sharing patterns
These are often the first indicators that something is off.
Why Basic VPN and Proxy Detection Isn’t Enough
Basic VPN and proxy detection isn’t enough. A virtual private network (VPN) can mask a user’s real IP address by routing their traffic through a different server, and a proxy works similarly. Both make it harder to tell where someone is actually connecting from.
Traditional IP data providers detect these tools by combining infrastructure intelligence with network and usage signals. This works well for networks like Tor, a well-known anonymization tool where exit nodes are publicly known and traffic patterns are distinct. But not all anonymization is that obvious, nor that suspicious. Plenty of legitimate users are on a VPN for work, travel, or just out of habit. Flagging every VPN connection as risky creates noise.
The Growing Residential Proxy Problem
Residential proxies are an increasingly prevalent problem because they don’t look like proxies at all. Unlike traditional proxies that route traffic through data centers, residential proxies route through real consumer internet connections. There’s no telltale protocol or unusual connection behavior to detect because they are normal IP addresses routed through actual home networks. They’re designed to be indistinguishable from regular traffic.
That means traditional detection methods have no reliable way to flag them. However, similar to VPN usage, not all traffic from a flagged residential proxy IP is malicious. After all, the entire point of a residential proxy is to hijack a consumer’s real IP address, so some of the traffic on that IP can very well be legitimate.
Identifying truly risky residential proxy traffic or separating legitimate VPN usage from suspicious activity means evaluating behavior, network characteristics, and usage patterns to surface risk that basic detection flags miss.
Updated Phone Contact Grading
We’ve also refined our phone contact grading system to improve precision. The updated grades prioritize higher connect rates over raw volume. This means there will be fewer Grade A and B contacts but a higher likelihood that the ones you do get are the right person.
What the New Grading Means in Practice
The new contact grading reduces the volume of Grade A and B contacts by approximately 5-15%, while improving contact and quote rates by roughly 0.5-1%. In practical terms, this means if you were previously reaching out to 10,000 Grade A and B contacts, you’d now target around 9,000 higher-quality contacts with a better chance of reaching the right person. The result is lower outbound dialing costs, higher contactability, and better conversion efficiency – less outreach for better results.
Who Gets Real Contact 2.0
All new users (trial, self-serve, and enterprise) will see Real Contact 2.0 by default. The one exception: new sign-ups added to older accounts that currently use Real Contact 1.1 will stay on 1.1 until the account requests the upgrade.
If you’re already on a previous version and want to move to 2.0, reach out to your account executive or contact support@trestleiq.com.
Get Started
Real Contact 2.0 is available now. If you’re already using Real Contact API, check out the updated Real Contact API documentation to see the new response structure, IP attributes, and grading details.
Or, if you’re new to Trestle, start your free trial today. No credit card required.
