Finance & banking
Financial institutions need similarity search over transactions, cases and policies, under strict data-residency rules.
The challenge
Section titled “The challenge”- New fraud resembles old fraud, but not exactly.
- Analysts search policy manuals and regulatory text by keyword.
- Customer records are duplicated across systems.
Why SrvDB fits
Section titled “Why SrvDB fits”- Case similarity: embed descriptions of confirmed fraud cases; compare new cases against them.
- Policy RAG: answer compliance questions from internal manuals.
- Deduplication: flag near-duplicate customer or case records with a cosine threshold.
- Data residency: everything stays in a local directory you control.
Five projects, five levels
Section titled “Five projects, five levels”Each project has its own page: the problem, how SrvDB powers it, what changes, architecture, use cases, core code and limits.
| Level | Project | What it does |
|---|---|---|
| 1 · Beginner | Policy Assistant | Answers staff questions from internal policies and product terms. |
| 2 · Intermediate | Complaint Analyzer | Clusters and deduplicates customer complaints to find root causes. |
| 3 · Advanced | Fraud Pattern Matcher | Compares new cases with confirmed fraud cases and ranks them for investigators. |
| 4 · Expert | Investigator Agent | An agent that gathers context across cases, policies and customer history. |
| 5 · Frontier | On-prem Risk AI Platform | A data-resident AI retrieval platform for risk and compliance teams. |