Same mindset, renewed focus using AI

CityBaseLab | Financial Intelligence for Local Governments

After 50+ years working in and around municipal finance — as a city and county budget director, consultant, and analyst — I built the platform I always wished I had. Today, I’m putting it in front of you.

Lewis McLain·Founder, CityBase.Net·May 2026

Why I built this

Local government finance is one of the most consequential and least-understood disciplines in American public life. A city’s ACFR, its rate-setting models, its long-range capital plan — these documents quietly decide whether a town can afford its next fire station, whether bonds get priced fairly, whether a growing population gets the services it pays for.

And yet almost all of that intelligence lives buried in static PDFs. Hundred-page documents that get printed, filed, and quoted from selectively at council meetings six months later. Even the cities doing it well — and there are many — struggle to turn their own data into a decision tool. The story is in the data. The data isn’t in the conversation.

I’ve watched this play out in council chambers, bond pricing calls, budget workshops, and rate hearings for five decades. I built CityBaseLab to close that gap.

What CityBaseLab does

CityBaseLab turns the public financial record — ACFRs, monthly comptroller data, population projections, capital plans, debt schedules — into working decision tools. Not dashboards-for-dashboards’ sake. Actual instruments a finance director, a city/county manager, a school district superintendent, an elected official can use the week before a vote.

It’s organized as three layers stacked on top of one another:

Data Layer

Audited financials, monthly sales-tax distributions, debt registries, certified property values, population projections — ingested from the actual public sources (Texas Comptroller, the entity’s own ACFR, U.S. Census, NCTCOG, the Bond Review Board, EMMA), structured consistently, and kept current.

Intelligence Layer

On top of the data, the tools that interpret it: trend analysis, rolling-12 windows, per-capita normalization, scenario engines, structural-balance signals, debt-stress ratios, and the explanatory text that ties what the numbers say to what it means. Can you analyze every city, county and school district, and then provide commentary on each? I already have with the help of AI.

Decision Layer

The view a specific role — not a generic user — opens before a specific decision. A finance director the morning of a pricing call. A city manager the day before a budget workshop. A council member the night before a rate vote. Each gets their own framing of the same underlying data.

Who it’s for

City Managers

The structural read on revenue, capacity, and growth pressure before the next strategic decision.

Finance Directors

Rate-setting, MYFP, debt strategy, and pricing-day tools that hold up to FA scrutiny and council questions.

Elected Officials

The honest one-page view of where the city stands — growth, debt, services — in plain language.

Analysts & Auditors

Reproducible numbers, transparent assumptions, and the trail from raw source to printed exhibit.

A practical path, not a moon shot

I’m not asking anyone to rip out their existing systems. CityBaseLab is built to layer on top of what cities already have. How many software systems have been acquired by justifying management information tools – and then only transactional data is produced? A typical first engagement looks like this:

• 30 days — Financial Data Foundation. Pull the entity’s last decade of audited financials, sales tax history, and debt registry into the platform.
• 60 days — Cost Allocation & Operations. Add per-capita normalization, peer comparisons, the structural read, and the working ratios staff actually use.
• 90 days — MYFP & Scenario Modeling. Forward projections, scenario engine, and the briefing views for council and bond pricing.

Three months in, a city has a working long-range financial plan, a defensible scenario engine, and a set of role-specific decision views — built on its own data, anchored to its own ACFR and budgets.

What you can see today

The “Kick the Tires” page links to live, working examples built on the CityBaseLab approach — not screenshots, not slideware. Open any of them in a new tab, click around, and see how the platform turns public financial and demographic data into decision-ready views:

• NTMWD — Cost of Service & Population Dashboard. A full financial-intelligence view of one of Texas’s largest regional water systems.
• Texas Population Atlas. Every Texas city and county, 2010–2024 actuals plus projections to 2100, with revenue-base implications baked in.
• Debt Management & Bond Pricing Lab. 21 tabs covering AAA MMD scale, refunding savings, Texas bond comps, and a fully worked example using a real $2.5B financing.
• McKinney 2025B Refunding Verification. A penny-perfect replication of a real Causey verification report.
• City of Fiscal Bliss — EDC/CDC Long-Range Model. Type A and Type B sales-tax corporation modeling with project pipeline and debt capacity.
• Data Center Fiscal Impact Model. 20-year net fiscal impact of a 100 MW data center on a host city — both sides of the ledger, with full abatement and BPP depreciation modeling.
• North Texas CPI & Construction Escalation Dashboard. Custom composite builder for honest project-cost escalation.
• MYFP & Scenario Engine. A working long-range financial plan covering FY1997–FY2036, built on real McKinney financial data.
• McKinney ISD Financial Data. An analysis of their key financial data all in one place, and ready to tell the story of trends that contain yellow flags.

Every one of those is real. The data sources are public. The methodology is transparent. The math is reproducible. That’s the standard.

What’s next

Over the next few months I’ll be publishing more on the specific decisions CityBaseLab is built to support — rate cases, pricing days, MYFPs, fiscal-impact analyses for large developments — with worked examples from real cities. If you run finance for a city, school, special district, or transit agency, I’d like to talk.