Public Funds Analytics

Cross-entity pattern discovery for public-sector auditors.

Industry	Level	Difficulty	Recommended mode
Fiscal auditing	5 of 5	Frontier	hnsw

Coming soon

A complete, runnable Public Funds Analytics reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Public spending spans many entities.
• Patterns cross organisational boundaries.
• Archives span years.

How SrvDB powers Public Funds Analytics

1. Multi-year records from many entities are embedded.
2. Similar patterns are linked across entities.
3. Offline field audits supported.
4. Crash-safe evidence index.

What changes

Today	With Public Funds Analytics on SrvDB
Entity silos	Cross-entity patterns
Year-by-year view	Multi-year search
Office-only tools	Field-capable

How it works

1. Ingest records.
2. Embed.
3. Link patterns.
4. Review.
5. Report.

SrvDB features it uses

• HNSW graph (hnsw): approximate search that visits a small part of large collections; tune with set_ef_search().
• Fully offline: SrvDB runs in-process and never opens a network connection.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• Several databases: each database is a directory, so different data types or tenants stay separate.

Use cases

• Procurement patterns
• Grant misuse signals
• Payroll anomalies
• Asset audits
• Performance audits
• Multilingual audits

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./public-funds-analytics-db", dimension=768, mode="hnsw")

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def related_patterns(record, k=5):
    hits = db.search(embed(record), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Signals require auditor judgement.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Fiscal auditing projects

Level	Project	What it does
1 · Beginner	Working-paper Search	Search prior working papers and findings by meaning.
2 · Intermediate	Finding Drafter	Drafts audit findings and management letters from similar past findings.
3 · Advanced	Duplicate Invoice Detector	Finds duplicate and near-duplicate invoices and vendors.
4 · Expert	Continuous Audit Agent	An agent monitoring entries and contracts continuously.
5 · Frontier	Public Funds Analytics (this page)	Cross-entity pattern discovery for public-sector auditors.

See also: Use cases · Live examples (coming soon)