As a self-certified data nerd, I’m proud to say that Crossbeam is a true powerhouse of a data platform. Data pipelines, security, user management, interfaces, integrations… the list goes on. But there is one rarely-discussed piece of Crossbeam’s data stack that touches every iota of value we produce for our customers: The Crossbeam Matching Engine.
When we talk about “Matching” here, we’re talking about matching data points across company lines: Making sure that “my account” and “your account” actually represent the same company or that “my contact” and “your contact” are actually the same person.
In this article, we’ll dig into “the matching problem” that Crossbeam has tackled over many years, and a glimpse into some of the secret sauce that powers our engine today.
The matching problem
Matching may sound like a simple problem at first, but it quickly spirals into a complex, multifaceted surface:
What happens when we need to match across different CRM platforms who organize concepts differently (i.e. HubSpot vs. Salesforce vs. a Google Sheet?)
What happens when companies have distinct data points that don’t overlap at all (i.e. one has a name, another has a domain, a third has a contact?)
How do you weigh a name vs. a domain vs. an email vs. a DUNS Number vs. a physical address etc etc etc…
How do you handle subsidiaries and company hierarchies?
For example, what if one company has an account named “Delta” that represents Delta Airlines, but another has one called “Delta” that represents Delta Faucets? They shouldn’t match. But in another case, a company could have a record with the domain delta.com and another’s account has a domain of deltaairlines.com? They SHOULD match (the airline owns both domains, and one redirects to another).
Getting this right across millions of companies (and billions of CRM records) is the whole ballgame, and the Crossbeam Matching Engine is the star.
Why matching matters
In simple terms, matching is the core of our trust engine — and trust is our business.
Most companies use Crossbeam to enable rules that say something like “if this account matches a customer in my partner’s CRM, then share the details from our CRM.” As a result, the brain that decides when “a match is a match” is also the trust engine that governs how and when data changes hands across companies.
If the matching algorithm gets it wrong, that can come in two flavors: False Positives and False Negatives.
False Positives
If we accidentally declare something a match that isn’t really one, it’s a big deal. This is called a “false positive.”
We have a very low tolerance for false positives, as they can result in data unintentionally being shared. We take extreme care to make sure that each “match” is supported by a strong level of statistical and hard data evidence.
False Negatives
The flip side is “false negatives,” where we “miss” a match and fail to identify it. This is also painful, but more on the value creation side. Every match that we miss is a missed opportunity for new business and ROI for our customers. This creates motivation for us to make the algorithm as robust and multifaceted as we can to ensure every match is found without creating false negatives. Over time, this has been the driving motivator to make the algorithm more robust.
The evolution of matching
Crossbeam’s current matching engine is built around a highly controlled, facts-based framework designed to ensure accuracy. It follows a clear process to narrow raw CRM data down to the most trusted identifiers:
Data cleaning. From potentially hundreds of CRM fields, we focus only on those that are most actively present, updated, and exclusive (on their own or in combination): company name, domain, phone numbers, emails, and the like. These are cleaned, normalized, and reduced to a consistent root format.
Quality filtering: We automatically remove junk data, test domains, and placeholder records that can distort matching outcomes.
Network-informed enrichment. When a record is partially complete but there is a high quality unique identifier (or set of them) present, the engine is able to look for common associations that are observable across and anonymized and aggregated slices of our data set. These allow us to do proprietary enrichment to further fortify the record and find common properties even if the starting point made the records disparate.
Matching: Once the data is sanitized, matching happens through strict comparison. If the data points align, it is a match. If they do not, it is not.
As we continue to invest in matching, we are seeing the system become smarter and more context-aware. This natural evolution is allowing us to scale with our growing ecosystem and diverse universe of customer types while still maintaining the highest standards of accuracy and trust.
Here are a few areas where we’re currently investing:
1. Expanding the data points that power matching
We are moving beyond the most common CRM properties to include a richer set of account attributes: phone numbers, tax IDs, DUNS identifiers, and detailed location data.
For small businesses and franchises that often lack consistent domains, we will extract brand or franchise names from social profiles and marketplace pages, turning previously ignored data into valuable matching inputs. All these new fields will be normalized and structured so that they can be compared intelligently across systems.
2. Early detection and adaptive classification
Before matching even begins, we will identify and classify records by type. It will detect and exclude junk data, recognize duplicate entries, and apply tailored logic for different business categories such as franchises or large enterprises. For example, if a phone number appears across too many CRM records, it will automatically be discounted, while domains shared across multiple accounts will be flagged as potential franchise indicators.
3. Multi-attribute enrichment powered by ecosystem data
With more than 30,000 companies in our network, Crossbeam has a unique view into how real-world data behaves across CRMs via anonymized, aggregated slices of that data. We will leverage that intelligence, mapping which fields tend to co-occur within real companies across the ecosystem.
If an account record is missing key details, the system will use this knowledge to infer the most likely associated attributes (such as websites, phone numbers, or country code) creating a more complete, consolidated record. This network-level enrichment strengthens inputs before they ever reach the matching stage, improving precision and uncovering matches that would otherwise be invisible.
4. Weighted, confidence-based scoring
The new engine will calculate a confidence score for every potential overlap. Each attribute contributes to the overall score according to its reliability and context.
For a large enterprise, a domain match may be sufficient. For a franchise, additional signals like phone number, location, or brand alignment might be required to reach the same confidence level. These confidence scores will allow for ranked results, smarter automation thresholds, and human review where needed.
5. Continuous improvement through AI
It’s worth noting that matching billions of data points against each other is not necessarily a job that is best solved by an LLM, although LLMs introduce opportunities to help us analyze, weigh, ideate, and iterate on the logic we apply. In other words, we’re not training a custom LLM to do this job or using our data as training data. We’re just better at building our matching engine because LLMs are our co-pilots in creating continuous improvements.
AI models will guide how attributes are weighted, how thresholds are tuned, and how exceptions are handled. Over time, these models will learn from real match outcomes and adjust parameters to optimize accuracy.
The system will also perform automatic evaluations to refine preprocessing rules and improve how different attributes contribute to overall confidence.
The result is an engine that is smarter, scalable, and increasingly autonomous. It learns from the ecosystem itself, gets more accurate with every record, and delivers cleaner, high-confidence matches.
Conclusion
Matching sits at the heart of Crossbeam’s value — and it’s only getting smarter. As our network expands and our data grows richer, the Matching Engine continues to evolve from a deterministic system into an adaptive intelligence that learns from billions of real-world relationships. It’s not just about finding overlaps anymore — it’s about understanding them, ranking them, and enabling our users to act on them with confidence. Every improvement we make here compounds across the entire Crossbeam ecosystem, sharpening the insights that power our products.
This is why we call it our “secret sauce.” Matching is the invisible engine that turns trust into action and data into revenue. It’s the quiet superpower that ensures our platform scales accurately, securely, and ethically across tens of thousands of connected companies. As we look ahead, the Matching Engine will remain the foundation for everything we build — from ecosystem-wide analytics to AI-driven revenue orchestration — fueling the next generation of ecosystem intelligence.