How Munifi AI Works
What a top municipal finance consultant does in weeks — data collection, normalization, peer selection, benchmarking, and narrative synthesis — Munifi AI does automatically, for every municipality, every time public data updates.
Transparent methodology. No black boxes. Every step documented and available to subscribers.
Data Collection
We pull directly from government APIs and published ACFRs. No intermediaries. Every source is documented with a URL, the date it was retrieved, and the version of the data used.
- Direct API connections to CT OPM, Census Bureau, and MSRB EMMA
- ACFR PDFs processed and validated against official filings
- No third-party data brokers or commercial data vendors
- Every retrieval is timestamped and logged for auditability
Normalization
Every metric is mapped to a canonical definition. Budget and audited actuals are clearly labeled and never silently combined. Adopted, amended, and actual figures are kept separate — and the basis is always cited.
- UCOA codes mapped to a canonical expenditure taxonomy
- Revenue categories standardized across towns regardless of local naming
- Fund-level data aggregated to government-wide consistently
- Fiscal year boundaries normalized across municipalities (June 30 close)
Peer Selection
Peer sets are algorithmically generated from demographic and fiscal similarity, then reviewed for obvious compatibility issues — school district structure, service model differences, outlier wealth levels, or regional cost differences that would make a comparison misleading.
- Initial pool built from population band, budget size, and fiscal capacity (grand list per capita)
- Algorithmic scoring weights each dimension — weights shown in every report
- Reviewed for structural compatibility: unified vs. regional school districts, urban vs. suburban service models
- Outlier wealth removed: a town at 3× the median grand list per capita skews every comparison
- Regional cost differences flagged when peer crosses a labor market boundary
- Final peer set of 8 towns — defensible enough to present to a skeptical board
Analysis
A deterministic rules engine computes all benchmarks, ratios, and scores. An LLM delivers the narrative. The math is never delegated to AI — only the plain-English explanation of what the numbers mean.
- All ratios and scores computed by a tested, versioned rules engine
- LLM used only for narrative generation — not calculation
- Narrative is constrained to reference only computed values
- Every AI-generated sentence is traceable to a specific data point
Confidence Labels
Every data point carries a confidence label. We believe uncertainty should be visible, not hidden.
Data sourced directly from an audited or official government filing. ACFR figures, certified mill rates, and UCOA actuals all carry this label.
Data sourced from a survey, preliminary filing, or derived from a model. For example, mid-year actuals estimated from budget execution reports.
Data is sparse, stale (>2 years old), or inconsistent with adjacent sources. We show this label rather than hiding the uncertainty.
Every number is source-attributed
We pull from verified government filings and official data portals. Every figure carries a source citation and a confidence label — audited, estimated, or pending — so readers always know the basis.
Annual Comprehensive Financial Reports — audited and filed with the state. The authoritative source for all financial data.
State-collected budget actuals by department and category, normalized to a uniform chart of accounts across all municipalities.
The assessed value of all taxable property in the municipality, and the tax rate applied to generate property tax revenue.
Electronic Municipal Market Access — official MSRB repository for all municipal bond filings.
Audit management letters filed through the state Electronic Audit Reporting System — flags material weaknesses and audit findings.
Actuarial valuations for defined benefit pension plans and other post-employment benefits — lags one year behind the ACFR.
Monthly unemployment rates and labor force statistics at the municipal level from the Department of Labor.
American Community Survey 5-year estimates for demographic and economic context — population, income, housing, and more.
Consumer Price Index data from the Federal Reserve — used to benchmark expenditure growth against inflation.
Sample data points shown above are illustrative. Every metric in every report is cited to its exact source document with a URL.
Questions about the methodology?
We're happy to walk you through any part of our analysis pipeline — just request a demo.
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