In our last blog, we introduced Large Database Models (LDMs) - a new approach to querying enterprise data by meaning and similarity rather than rigid filters. It's a compelling idea in the abstract, but where does it actually apply?
The honest answer: almost anywhere your organization already relies on comparing a new situation to past ones. Below are ten use cases, spanning industries you would expect and a few you might not, that show how far "find me records like this one" actually reaches.
1. Fraud Detection in E-Commerce Order Fulfillment
A rule that flags "orders over $500 shipped to a new address" catches the obvious cases and misses everything else. Similarity search instead compares a new order's full pattern - shipping-to-billing address mismatch, order velocity from the account, device fingerprint, basket composition - against past orders that were later confirmed fraudulent. Fraud rings tend to reuse the same playbook with small variations, and that's exactly the kind of pattern similarity search is good at surfacing, even when no one has written a rule for that specific variation yet.
2. Small Business Loan Underwriting
Small business lending is a harder underwriting problem than consumer credit - there's often no long credit history, and outcomes depend on things like cash flow seasonality and industry - specific risk that a generic score doesn't capture well. By comparing a new applicant's revenue trend, industry, time in business, and existing obligations against the businesses most similar to it in the lender's own historical book, underwriters get a case-based answer: "the 30 most similar small businesses we've financed had a 91% on-time repayment rate."
3. Health Insurance Plan Underwriting
Rather than pricing a health plan purely off actuarial tables, insurers can compare a new group's workforce demographics, claims history, and industry against the most similar groups they've previously underwritten - surfacing a realistic expected-claims range grounded in actual outcomes rather than a generic actuarial assumption, and flagging when a new group doesn't closely resemble anything in the existing book.
4. Subscription Box Churn Prediction
A subscription box company doesn't just want to know if a customer will churn - it wants to know why, so it can act. By comparing an active subscriber's recent box-skip rate, product rating history, and support contact pattern against subscribers who canceled in the last quarter, retention teams can catch the drift early and tailor the save offer to what actually worked for similar past subscribers, rather than sending a generic discount to everyone.
5. Streaming Content Recommendations
"Because you watched X" recommendations based on genre tags are shallow - two shows can share a genre and have nothing else in common. Similarity search over viewing behavior - what a viewer actually finishes, rewatches, or abandons early - captures taste more precisely than metadata tags do, and it works immediately for newly released titles once even a small viewer base has interacted with them.
6. Cybersecurity Threat Detection
Security teams are flooded with alerts, most of which are false positives from static signature-based rules. Similarity search over network activity - login patterns, data transfer volumes, access sequences - lets analysts compare a suspicious session against previously confirmed breaches or insider-threat incidents, surfacing the sessions that actually resemble past attacks instead of every session that merely trips a threshold.
7. Talent Matching and Recruiting
Keyword-matching resumes against a job description misses strong candidates who describe their experience differently than the posting is worded, and it over-indexes on candidates who happen to use the "right" words. Comparing a candidate's full profile - skills, project history, career trajectory - against the profiles of people who succeeded in similar roles previously surfaces a better-fit shortlist than keyword matching alone, and it works across differently worded resumes because it's matching on substance, not phrasing.
8. Real Estate Comparable Valuation
Appraisers and real estate platforms already use "comps" - comparable recent sales - to value a property, but building that comp set is usually manual and limited to a handful of obvious matches. Similarity search across square footage, condition, renovation history, and neighborhood characteristics can surface a wider, better-matched set of comps automatically, including ones a human might not think to check because they're in a different but genuinely comparable area.
9. Legal Case and Precedent Matching
Legal research often starts with keyword search across case law, which misses precedents that are conceptually relevant but use different terminology. Similarity search over case facts, issues, and outcomes can surface precedents that share the underlying legal reasoning or fact pattern, even when the specific wording is completely different - turning "find cases like this one" into a genuinely useful research starting point rather than just guessing keywords.
10. Retail Replenishment and Demand Forecasting
Forecasting demand for a newly stocked item at a specific store location is hard without history for that exact store-item pair. Similarity search lets a new store-item combination "borrow" a demand curve from the most similar existing store-item pairs - matched on store demographics, category performance, and local seasonality - giving replenishment teams a grounded starting forecast instead of a flat category-average guess.
What’s Common
Notice what's consistent across all ten, even though they span retail, finance, healthcare, security, HR, real estate, and law: none of these require building and maintaining a separate machine learning model for each specific question. They all reduce to the same underlying operation - find the records most similar to this one, and let their outcomes inform the decision. That's what makes LDM-style similarity search so attractive: one capability, applied to database tables you likely already have, unlocking use cases across nearly every function in the business.