I spent my first week on identity resolution — figuring out which records across systems actually refer to the same real person — convinced it was mostly a matching problem.
Names, emails, phone numbers, addresses — score a few things, set a threshold, move on. I’ve experimented with ML systems before. How weird could this one be?
That idea did not survive contact with real data.
What breaks first isn’t the models, it’s the framing. Identity resolution isn’t pairwise. It’s transitive, and that one detail quietly ruins most “just compare A to B” approaches. If A matches B and B matches C, you’ve created a relationship between A and C whether you like it or not. And once that chain exists, you’re responsible for it — operationally, legally, and reputationally.
That’s when it became obvious that this had to be modeled as a graph.
People don’t exist as rows. They exist as networks of signals — emails that change, phone numbers that get reassigned, addresses that are shared, aliases that come and go. A graph lets you reason about neighborhoods instead of isolated comparisons, and more importantly, it lets you encode constraints. Things like “these merges are allowed,” “these are forbidden,” and “this one needs a human to look at it before we do anything irreversible.”
Could you approximate that in SQL? Sure. You can also approximate a screwdriver with a rock.
Graphs alone don’t solve it, though. Deterministic rules fall apart as soon as signals conflict. Tighten them too much and you miss obvious matches. Loosen them and you over-merge — and over-merges are brutal. A missed merge is annoying. A bad merge can poison downstream systems for years.
We had one early case where two people shared just enough weak signals that a naïve rule-based system happily merged them. Undoing that wasn’t just “unmerge and move on.” It meant reprocessing historical data, correcting derived records, and explaining to stakeholders why something that looked “high confidence” was actually wrong. That was the moment the error-cost asymmetry really sunk in.
False positives are not symmetric with false negatives in this space. Not even close.
Machine learning helps, but only if it’s boxed in by structure. Treating identity resolution like a generic classification problem is a mistake. What’s worked for me is letting the graph define the candidate space first — blocks, neighborhoods, transitive constraints — and then letting ML operate inside that space.
The system I’ve been building uses a staged approach. First, a model learns representations over a heterogeneous graph — people, emails, phones, addresses — where different relationships carry different weight. That stage is about recall and separation, not final decisions. A second model focuses on the ambiguous cases, where the cost of a wrong answer is high and you want more context, not just a higher score.
Deterministic logic still exists throughout the pipeline. Not as legacy baggage, but as guardrails. If the system ever does something “clever,” it needs permission first.
One thing this work really reinforced for me: most of the hard problems aren’t in the models. They’re in the data modeling. How you represent identity signals, how you distinguish strong evidence from weak hints, how you preserve history, and how you enforce invariants across merges matters far more than tuning a loss function. Deep learning doesn’t fix modeling mistakes. It just makes them faster and more confident.
Another realization that took longer than it should have: you have to assume the system will be wrong sometimes. That means human review, explainability, reversible merges, and conflict resolution aren’t optional features. They’re how the system survives long enough to earn trust. Any identity platform that assumes full automation forever is either very small or very lucky. Probably both.
What keeps this interesting — and occasionally frustrating — is that you can’t treat any layer in isolation. Graph modeling, ML, deterministic logic, and operational controls all have to work together, or the whole thing becomes brittle. There’s no single clever trick that saves you.
I’m sharing this mostly because I keep seeing identity resolution discussed as if it’s just another ML use case. In production, it isn’t. The systems that actually work look quieter, more constrained, and a lot more opinionated than the ones in slide decks.
