The fundamental issue is that LLMs don't have a concept of canonical entity identity. They pattern match on text similarity rather than understanding that "Apple Inc" and "Apple Records" are completely different entities. It gets even worse when you realize companies can legally have identical names in the same country - text matching becomes completely unreliable.
Without proper entity grounding, any business logic built on top becomes unreliable.
If curious, details here:
https://engineering.ramp.com/post/transaction-embeddings
https://engineering.ramp.com/post/fixing-merchant-classifica...
Having dealt with this challenge at my last 3 companies, it's easy to hack together something that works most of the time. The hard part is dealing with gnarly customer inputs, the long tail of private businesses globally, and getting close to 100% accuracy (important for legal and risk use cases).
We're building what's essentially an AI-powered version of D&B - combining government registrar data globally with real-time web data at scale. Much more accurate on obscure entities and way faster updates than the legacy providers.
I actually shot you an email - would love to chat more about this if you're up for it.
We built an AI agent specifically for this that's backed by 265M legal entities. Last week it tested 160% better than our customer's existing system on their real data.
Still in stealth but happy to share our API docs if anyone's dealing with this: https://docs.savvyiq.ai/api-reference/#tag/entity-resolution
Open to chat about this problem if anyone wants to connect - email is in my HN profile.
(Disclosure: I'm the CTO)